31st North Sea Flow Measurement Workshop 2013

2013

Paper

No Paper Title Author Details Company Page

1 Using Multiphase Meter for Fiscal Purposes – Field Experience

Bjarne Syre Ole Anderson Steinar Fosse

DONG E&P Norge Maersk Oil Norwegian Petroleum Directorate

3

2

Performance of Subsea Multiphase Meters and a Topside Test Separator During Well Testing, After Skarv Field Start-up

Barbara M. Wrobel Andrew Hall Lukasz H. Wrobel

BP Norge AS BP Operating Company Ltd

25

3 Challenges with Salinity Measurements in Multiphase Flow Metering

Anton Gryzlov, Erik Undheim, Ebbe Nyfors, Lyndall Jordann, Stine Jæger Alvær and Elin Steinsland

Emerson Process Management, Roxar Flow Measurement

40

4

Allocation & Fiscal Allocation Applications: A Comparison of Current North Sea Practice with Recently Issued Guidelines and Regulations

Bruno Pinguet, Michael Smith and Eivind Venthe

OneSubsea 53

5 Allocation in an Uncertain World: Maximising the Use of Data with UBA on Global Producer III

Phil Stockton, Allan Wilson, Juliet Johnston and Robert Sibbald Neil Corbet

Accord Maersk

58

6

Challenges on Using Subsea Wet Gas Meters for Gas and Condensate Allocation Between the Wells at Sleipner Vest Field

Taurn Grover, Knut Kr Meisingset, Oystein Tesaker, Siv Kari Lien, Solfrid Loken Tonstad and Terje Kristian Wilberg

Statoil 92

7 Flow Measurement of High Viscosity Fluids

Chris Mills, Craig Marshall, Andy Kay and Marc Macdonald

NEL 100

8 Qualification of Fiscal Liquid Ultrasonic Meter for Operation on Extended Viscosity Ranges

Øyvind Nesse and Tore Brattan

Statoil 133

9 A New Measurement Program for VOC Emission During Offshore Oil Tanking of Shuttle Tankers

Kjell-Eivind Frøysa and Stian H. Stavland

Christian Michelsens Research AS 154

10 Flow Disturbance Cone Meter Testing

Gordon Stobie Richard Steven Kim Lewis Bob Peebles

GS Flow Ltd CEESI DP Diagnostics Ltd ConocoPhillips Company

173

11

Performance Improvement of Large Installation Base of Wellhead Venturi Wet Gas Measurement in Petroleum Development Oman (PDO)

Abdullah Al Obaidani, Khalil Al Hanashi, Hamed Al Hadhrmai and Dawood Al-Sulaimani

Petroleum Development Oman 203

12 The Application of Clamp-on Ultrasonic Flowmeters in Production Monitoring

Theo Warmenhoven Bernhard Funck and Peter Liptrot

GDFSUEZ E&P Flexim GmbH

211

13 Paper Not Available

2013

Paper

No Paper Title Author Details Company Page

14

Accuracy and Long-term Stability of Ultrasonic Gas Meters at Varying Operational Pressures and Different Liquid Loadings – Field Experience

Alexander Jakschik, Jörg Wenzel and Volker Herrmann Theo Warmenhoven

SICK AG GDF SUEZ E&P Nederland BV

239

15 Dynamic Testing Raymond J. Kalivoda, Jim H. Smith and Nicole L. Gailey

Not List 261

16 Sampling: What the Standards Don’t Tell You

Mark Jiskoot and Gary Potten

C-A-M 277

17 The Emperor’s New Clothes? – Oil with Water Flow Metering

Richard Steven and Damon Mayers Casey Hodges and Terry Cousins

CEESI CMSI (CEESI)

295

18 Field Test for the Comparison of LNG Static and Dynamic Mass Measurement Methods

Tore Mortensen and Henning Kolbjørnsen

Justervesenet 324

19 Assessment of LNG Sampling Systems and Recommendations

Asaad Kenbar NEL 339

20 Experiences with Samplers on Cold Liquids

Ole-John Melkevik Statoil ASA 359

21

Multiphase Meter Capable of Detecting Scale on the Pipe Wall and Correcting Flow Rate Measurements

Arnstein Wee Øystein Fosså Vidar Rune Midttveit

MPM ConocoPhillips Statoil ASA

373

22 Magnetic Resonance Technology: A New Concept for Multiphase Flow Metering

Jankees Hogendoorn and André Boer Matthias Appel,Hilko de Jong and Rick de Leeuw

Krohne Shell

394

23 Flow Swirl and Flow Profile Measurement in Multiphase Flow

Anusha Rammohan, Aditya Bhakta and, Vinay Natrajan John Ward and Manoj Kumar

GE Global Research GE Oil and Gas

416

24

Uncertainty Analysis of Multiphase Flow Meters Used for Allocation Measurements: Field Experiences and Future Challenges

Kjetil Folgerø, Jan Kocbach and Kjell-Eivind Frøysa Eiving Lynd Soldal, Kåre Kleppe and Erik Åbro

Christian Michelsens Research Statoil ASA

437

25 Uncertainty Analysis Based on Historical Data

Calum Hardie NEL 457

Poster 1

Subsea Sampling on the Critical Path of Flow Assurance

Eivind Vethe, Michael Smith and Bruno Pinguet Bernard Theron, Malcolm Atkinson and Onur Ozen

OneSusbea Schlumberger

478

Poster 2

Condition Based Monitoring – A Fully Automated Station Solution

John Lansing CEESI 494

31st International North Sea Flow Measurement Workshop

22nd - 25th October 2013

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Using Multiphase Meters for fiscal purposes - A field experience

Bjarne Syre, DONG E&P Norge Ole Andersen, Maersk Oil

Steinar Fosse, Norwegian Petroleum Directorate

_____________________________________________________________________________

1. INTRODUCTION

During the last decade there has been a trend that more and more marginal fields have been put into production. Typical characteristics for these fields are that the economy does not defend a standalone development. These fields require an existing installation that can act as a host. Normally, modifications to the host installation are required. Both weight limitations and economic issues will be normative for the chosen design.

Traditional metering systems which measure the different phases out of a large separator may not be feasible. The projects are challenged to come up with a solution that is smaller, lighter and cheaper. Multiphase flow meter (MPFM) has then often been introduced to achieve such requirements in the project (Thorn, et al., 2012).

Accordingly, the need for measuring multiphase flow has increased. However, the industry is still facing challenges related to accurate measurement of multiphase flow. When talking about tie-back developments to a host installation, the metering system will be essential for field allocation and thereby the income to the owners of the fields involved. Uncertainty in the measurement system is then critical to secure correct income, tariffs, taxes and in some cases also fees to the government.

Irrespective the challenges related to multiphase measurement and multiphase flow meters, the oil and gas industry has over the last few years installed more and more multiphase flow meters for fiscal services.

DONG E&P Norge (DONG) is the operator of the Trym field which is located in the southern part of the Norwegian sector of the North Sea, close to the Danish border. The field was developed during a period of two years from 2009 to 2011 as a tie-back to a Maersk Oil (Maersk) operated platform and it uses multiphase flow meter for fiscal metering. After more than two years in production, DONG has, together with Maersk gathered valuable experience on how to operate a tie-back field with multiphase flow meter as the fiscal meter.

This paper discusses the metering system, how it has been operated and its performance. The failures and the successes are included. In addition it includes what may be looked at as best practices. Two oil & gas operators and two Authorities, Norwegian Petroleum Directorate (NPD) and Danish Energy Agency (DEA) have been involved in both the development and the operation phase. Both the role of the Authorities and their experience from the development and operation are considered herein.

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Figure 1: Trym tie-back to Harald

2. METERING FOR FISCAL AND ALLOCATION PURPOSES – THE USE OF MULTIPHASE METERS

Before going any further it is crucial to have a common understanding of the term "metering for fiscal and allocation purposes". This paper is dealing with a field that is subject to Norwegian regulations. The fiscal metering system covered herein is within the scope of The Measurement Regulations (Last amendment 8.3.2012) (Norwegian Petroleum Directorate, 2001). The Scope for the Measurement Regulations are among other things described as:

These regulations are applicable to the petroleum activities in areas … relating to petroleum activities and … relating to tax on discharge of CO2 in connection with petroleum activities on the continental shelf, specifically:

a) in planning, design, construction and operation of metering systems for measuring produced, transported and sold quantities of oil and gas (fiscal measurement systems)…

Furthermore, there are some important definitions to be aware of (Norwegian Petroleum Directorate, 2001):

Allocation: Apportionment of petroleum between various groups and owner companies.

Fiscal metering: Metering carried out in connection with purchase and sale and the calculation of taxes and royalties.

Allocation is also defined by Energy Institute (Energy Institute, 2012) and is included here to give a graphical definition on the term:



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Figure 2: Definition of allocation (Energy Institute, 2012)

The Measurement Regulations are valid for both fiscal and allocation measurement systems and fiscal metering or fiscal measurement is used as common terms. In general the same requirements for measurement uncertainty apply for both fiscal- and allocation metering systems. But there is an opening in the regulations to argue for a higher uncertainty based on cost-benefit analysis. If the metering system is by their tasks defined as fiscal, it should be designed and operated according to the Measurement Regulations.

Figure 3: Accepted measurement uncertainty

Measurement systems that are subject to the Measurement Regulations shall be accepted by NPD. There is a requirement to obtain consent from the NPD prior to start-up of the metering system. As part of both the processes described above there shall be information related to the chosen solution including uncertainty figures. If the proposed measuring solution does not fulfil the requirements in the Measurement Regulations it is important to involve the authorities as early as possible.

Several fields have been developed by use of MPFM as the fiscal meter during the last years. On Norwegian continental shelf there are currently installed 14 fiscal metering systems that are using MPFMs and several more are under development. For UK the similar number are approximately 15 fields1 and for Denmark it is 42. Most of the mentioned systems in use in Norway have an estimated uncertainty of 2-5% of total hydrocarbon mass according to the Plan for Development and Operation (PDO).

The introduction of MPFM for purposes related to fiscal and allocation metering has not been without problems. In 2012 NPD started a project related to MPFMs which are subject to the Measurement Regulations. The reason for the project was to summarize the experiences during the last years. NPD realised that for several of the developments, the metering systems based on MPFM 1 Source: DECC 2 Source: DEA



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did not work as expected and caused disputes between the licenses. In some cases the owners of the different licenses ended up in arbitration tribunal (Norwegian Petroleum Directorate, 2012).

The Measurement Regulations were revised in 2012 partly as a consequence of the increased use of MPFM in fiscal metering systems. NPD wanted to formalise the use of MPFMs in such systems, both to increase the awareness of critical factors to succeed, and to avoid dispute related to the measurement system after production start-up.

3. THE TRYM FIELD

Figure 4: Location of production facilities (www.ens.dk)

The Trym field is located in block 3/7 (Production License 147) in the southernmost area of the Norwegian part of the North Sea. It is located just 3 kilometres from the border against Denmark, with the Harald installation as the closest installation. The water depth in the area is around 65 metres. The discovery was done in 1990 by A/S Norske Shell, and together with their partners they started to plan for development and operation in 2005. In 2008 Shell decided to sell their ownership in Trym without being granted an accepted PDO. There were also some other changes in the ownership and it all ended up with DONG E&P Norge as the operator (50% share) with Bayerngas Norge as the only partner (50%) (Norwegian Ministry of Petroleum and Energy, 2013).



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The Trym reservoir contains gas and condensate at a depth of about 3400 metres and the recoverable reserves were originally estimated to 1.5 million Sm3 of condensate and 4.3 billion Sm3 of gas. Development was done by a four slot subsea template and a 5 kilometre pipeline to the Harald platform. The reserves are produced solely by natural depletion via two horizontal wells and the production started in February 2011.

Trym DUC Lulita

DONG E&P Norge 50,0 %

Bayerngas 50,0 %

Maersk Olie og Gas 31,2 %

Shell 36,8 %

Chevron 12,0 %

Danish North Sea Fund 20,0 %

DONG E&P Danmark 21,8 %

Noreco 28,2 %

DUC 50,0 % Table 1. Ownership interests of the involved Licences

The Harald platform is part of the Dansk Undergrunds Consortium (DUC) and is operated by Maersk. The installation consists of a production platform and a living quarter platform. It has been producing since 1998 from both the Harald field and a neighbour field called Lulita. The Lulita field is 50% owned by DUC (Danish Energy Agency, 2012).

One important issue with the Harald platform is the operation philosophy. There are only a few persons working on the installation and the offshore installation manager is shared with the Tyra field centre located approximately 60 kilometres away. There are 16 beds on the installation and with a regular crew of 13, there are only a few beds available for other personnel. There are also limited numbers of helicopter flights to the platform. The consequence is that it may be a challenge to send service personnel to the installation when needed, and the operation and maintenance philosophy has to reflect this.



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Figure 5: The Harald installation

4. CROSS BORDER PRODUCTION AND THE AUTHORITIES

The Trym development is the first cross border development between Norwegian and Danish continental shelf. Before the development was approved an agreement between Norway (Ministry of Petroleum and Energy) and Denmark (Danish Ministry of Climate, Energy and Building) that described the production of oil and gas resources from Norwegian sector via Danish infrastructure (platforms, pipelines and terminals) needed to be in place (Norwegian Ministry of Petroleum and Energy, 2010). An obstacle before the agreement could be signed was a 5% tax for use of oil pipeline in Denmark. The result after negotiations was that produced oil from foreign countries was not subject to pay the above mentioned tax for use of Danish pipeline infrastructure.

As a part of the agreement there was issued a cooperation agreement between NPD and DEA regarding fiscal metering issues.

The selected development solution for Trym as a cross border to a Danish installation gives the outcome that both Norwegian and Danish authorities are involved in the fiscal metering system. Regulations related to fiscal metering for both countries have to be taken into account. Practically, it means that Trym metering system shall be in compliance with both NPD and DEA's regulations for fiscal metering. As there are no specific regulations in place in Denmark that covers fiscal metering in detail, a common practice is that compliance with Norwegian regulations fulfils the Danish requirements.

Another result is that both NPD and DEA are involved in metering audits. There is also a yearly cooperation meeting between the parties involved (DONG, Maersk, NPD and DEA) where the status for Trym metering system is closely covered. Until now there have been three government audits and three cooperation meetings, so the performance of the Trym metering system has been under close surveillance from the authorities after start-up.



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5. TRYM FISCAL METERING

The Trym fiscal metering system consists of a MPFM and a Test Separator both installed at the Harald platform. The MPFM is defined as the fiscal metering point and its output provides the input to the allocation system and in the end a monetary value.

Maersk as the Harald operator is responsible for the day-to-day operation and maintenance. DONG is, as the operator of the Trym field, responsible for compliance with Measurement Regulations and also the focal point for all authority communications related to Trym.

The MPFM is installed downstream the Trym inlet choke and is calibrated and adjusted against the Test Separator regularly in accordance with the agreements between the parties. The MPFM and meter runs installed on the Test Separator gas and oil outlets with its instrumentation, are all part of the fiscal metering system and are therefore subject to the Measurement Regulations.

The Gas Volume Fraction (GVF) for Trym field was estimated to be from 92% to 98% during the field lifetime. The change between gas and oil mode is planned to be done at a GVF of approximately 95%. From start-up of the field until now the GVF has increased from 92% to 94%.

The Test Separator metering system was upgraded as part of the Trym field tie-in project. The upgrade was needed to improve the overall measurement uncertainties that were specified in the PDO to be ± 4 - 9% of total hydrocarbon mass (95% confidence level). The upgrade of the Test Separator involved a new 4” liquid meter run with Coriolis meter, water cut meter and sampling point, additional differential pressure, pressure and temperature transmitters at the orifice gas meter. Finally the flow computers were upgraded from Emerson S500 to S600 model.

The supervisory system consists of:

o DMS (Daniel metering system) computer. o Data communication to/from Harald Distributed Control System (DCS) and Plant Server

system.

The multiphase metering station consists of:

o Flow computer o Multiphase Flow Meter.

The installation is prepared for a second Multiphase Flow Meter.



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Figure 6. Trym metering system

The Test Separator liquid metering station consists of one 2" meter run and one 4" meter run. The test separator liquid meter station is equipped with:

o Flow computer o 2" meter run (2 off Coriolis meter installed in series (master/slave)) o 4" meter run (1 off Coriolis meter) o Water in oil meter on each meter run. o Pressure transmitter. o Temperature transmitter. o Spot sampling outlet.



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The Test Separator gas metering station consists of one 12" meter tube with a senior orifice fitting and orifice plate. The test separator gas metering station is equipped with:

o Flow computer, with AGA 8 calculations for density. o Orifice meter. o 2 off Differential Pressure transmitter o Pressure transmitter o Temperature transmitter o Spot sampling outlet

There are no automatic sampling systems installed on the Test Separator outlets. Pressurised manual spot samples of oil and gas are retrieved at separator pressure and used as representative samples. These are analysed and recombined to a fluid composition at an approved laboratory. The results are evaluated and used as input to the metering and allocation system, process simulation model for DUC and, if found necessary, as PVT input to the MPFM.

There is a high-capacity network connection between the Harald installation and Maersk offices in Esbjerg that enables for remote surveillance and operation of the metering system. This is an absolute requirement because of the operation philosophy of the Harald field. In addition there is a system established for transfer of essential real time metering data from the Harald installation to DONG's premises in Norway.

The verification, also called proving, of Trym MPFM is as earlier mentioned done by routing the Trym production through the Test Separator. There are routines that define flushing time to ensure representative Trym fluid in the vessel. The proving lasts for approximately 24 hours and all data during this sequence is stored on a dedicated part of the Harald Distributed Control System (DCS). Further on, the proving of the MPFM is thorough documented and all vital information is stored in a historical database. If, in the future, the result of a proving should be questioned, it will be possible to do a proper verification and if necessary a correction.

After the proving is completed, meter factors for the MPFM are calculated, evaluated and uploaded to the flow computer. All uploading is initiated by a Maersk metering engineer after the results have been evaluated and accepted.



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Figure 7: Part of report issued after verification, showing HC mass and oil mass

6. ALLOCATION OF PRODUCTION FROM TRYM

The production from Trym is processed and introduced to an infrastructure that was established decades ago, the DUC infrastructure. Compared with similar infrastructure systems in Norway and UK there is one significant difference. Namely that there is only one ownership structure for all of the production entering the system. Consequently, ownership allocation has not been an issue before the Trym tie-back.

Producing the Trym field via the DUC infrastructure has introduced the need for allocation between different ownerships. The allocation (and metering) has a direct impact on the taxable income to Denmark and Norway. So without doubt, the quality of the metering and allocation is of high importance for both the licenses and the countries involved.

A common way of doing allocation when exporting through a pipeline is to allocate the different components on a mass basis. This is also the case for allocation of Trym. It is also worth mentioning that the DUC is allocated by difference. This can be described by the following equation:

𝑄𝐷𝑈𝐶 = 𝑄𝑇𝑜𝑡𝑎𝑙 − 𝑄𝑇𝑟𝑦𝑚



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When doing allocation by difference, it is essential to consider the relative quantities provided by each user. Measuring the minor stream will keep the uncertainty relatively low (Energy Institute, 2012) as done in this specific case.

The metering system determines the quantities produced from the field. However, what really counts is the allocated final sales products which are dry gas to the market and stabilised oil. This is a process that happens downstream the fiscal meter, and for Trym the dry gas is delivered at gas export points at either Tyra West or Tyra East while the stabilised oil is delivered at the oil terminal in Fredericia, Denmark.

7. OPERATIONAL EXPERIENCE

Trym has been in production for more than two years and the production has been as expected. The metering system has been working quite well, however, a close follow up has been a premise to achieve this. Some of our experiences from the operation are discussed below.

7.1 Preparation before first-oil

Based on the context that Trym is a marginal field and was developed exclusively for natural deployment, most of its values would be produced during the first years of production. The PDO profile was indicating that 50 % of the condensate and 31 % of the gas would be produced during the first two years in operation.

Figure 8: Expected yearly condensate production from PDO

Figure 9: Expected yearly gas production from PDO



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As a result of this there was no time to try and fail. Best practice for operation of the multiphase meter as fiscal meter had to be in place before first-oil. To achieve this ambitious goal both the metering system, the organisations and the quality management system needed to be prepared. Proper communication with both Norwegian and Danish authorities was also an important part from day one.

But what if the metering system failed?

No matter how well prepared everything was it could happen that the system did not work as provided. There are many parts in the metering system that may fail during start-up and operation. The system was vulnerable because of only one MPFM installed, without a back-up meter available. Worst case scenario was a failure to the multiphase meter that resulted in the need of replacement. A contingency plan on how to utilize the Test Separator and still have acceptable measurements was prepared.

The metering system was implemented into the existing metering system on the Harald platform. A third party verification of Trym metering system was done. This included also verification of the calculations done in the flow computers and the computer system for proving of MPFM.

Figure 10: Trym Metering System - Reporting lines

Trym metering organisation is quite simple with a metering responsible person at DONG and an onshore metering engineer at Maersk, responsible for daily operation. In addition to this the Harald crew supports the metering engineer with offshore tasks. Awareness to the Trym metering system is a key factor, and all of the Harald crew went through an introduction course covering Trym metering system and the NPD regulations. The importance of the MPFM and how to deal with metering upsets was pointed out. However, as previously mentioned, the manning on Harald is very limited and it is not realistic to maintain a competent fiscal trained manning on Harald. All data evaluations, verifications, flow computer maintenance and parameter changes are therefore done by the onshore metering department. This has been possible due to the fact that all computers taking part of the Trym Metering System are fully accessible from onshore. All calibration and adjustment of equipment taking part of the fiscal reference is also done onshore.



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Figure 11: Some of the procedures established by Maersk for Trym Metering

Maersk established a metering quality management system containing operational and maintenance procedures. Detailed procedures for different main tasks were established in addition to procedure regarding corrective actions to be taken in event of a measurement malfunctions.

7.2 The Multiphase Flow Meter and Allocation

Originally multiphase flow meters were used to replace separators, and measuring the different phases was of high importance. E.g. measuring of well streams for well allocation is a common use of multiphase meters (Pinguet, et al., 2012). The multiphase flow meter is a high-tech instrument that has a significant amount of information available for the end user. Trym MPFM has about 80 different outputs available.

Figure 12: Available data from MPFM

Using the MPFMs for fiscal metering is different from other applications that MPFMs are used for. The main objective for the fiscal meter is to measure the produced quantities that generate an input to the allocation system. It is of high importance to have knowledge about the allocation system.



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Figure 13: The only output that matters when it comes to fiscal purposes for Trym.

The only important input from the metering system required for the allocation system, is the total hydrocarbon mass (HC mass).

This means that the measured quantities of oil and gas phases are not important for the main purpose of the fiscal meter. This is important information that those responsible for the metering system should be aware of, from planning to operating the metering system.

But how is the amount of oil and gas then decided when the measured phases from the MPFM are not used? The answer to this is in the allocation system. The example below is a quite common way of designing an allocation system:

• The total HC mass from the metering system is entered into the allocation system. • Hydrocarbon composition is entered into the allocation system. • Calculate the mass of each component in the total HC stream. • Calculate the Process Allocation Factors (PAF).

o There is a PAF for each component and it is a ratio number that tells how much of the total amount of a component that is delivered as gas. The rest is delivered as liquid. The PAFs are calculated by use of the process simulation model for the DUC system.

• Calculate the split between exported gas and exported liquid by use of PAFs. • Some of the gas is allocated to fuel and flare and is deducted from the gas stream.

By learning about the allocation system before first-oil from Trym, it was easy to focus on the most important output from the MPFM. All follow up of the MPFM afterward has been done with the total HC mass in mind. However, optimising the system for the total HC mass has also given some challenges. Other disciplines within the organisations may use other data from the metering system and it is important to identify this to avoid the use of inaccurate data that may give consequences for e.g. reservoir management or financial reporting.

It is also worth mentioning that Trym has been producing oil and gas with very low water content. That makes it easier to calculate total HC mass. If and when Trym starts producing a higher content of water, there might be challenges that have not been seen so far. However, significant amount of water is not expected from Trym.



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Figure 14: Allocation model

7.3 Verification of Multiphase Flow Meter against Test Separator

Verification of the MPFM against the test separator was agreed between the Trym and Harald owners to be done frequently and on regular basis. So far it has been done monthly, given that the field has a stable production. The verification, also called proving, takes all in all about 24 hours to perform. This includes necessary work to route the production from the HP separator to the Test Separator, flush through the Test Separator with Trym fluid, perform the proving, and route the production back to the HP separator.

All the data during the proving sequence is stored in a dedicated proving system which is part of the DCS system. It is then possible to review the data after the test is finished and define the stable and representative period which shall be used to calculate new meter factors (MF). There are several meter factors in use for the MPFM, one for oil (MFoil), one for gas (MFgas) and one for water (MFwater). The proving application calculates new meter factors which then have to be manually verified and approved. Approved MFs are downloaded to the Flow Computer and will be used until new MFs are established.

The regular access to Test Separator and proving of the MPFM has been a key factor for the confidence to the Trym metering system, both from the operators and the authorities. Experience from other fields using multiphase meters as fiscal meter is that commercial agreements may limit the access to test the multiphase meter against a reference, or it may involve a relative high cost in form of compensating for other field's deferred production. That may cause mismeasurement over a significant period with the need for fiscal corrections afterwards. Missing access to Test Separators (as reference) may be caused by the host platform using this separator to optimise own production.

As mentioned above, proving of the MPFM against the Test Separator has been done monthly. In addition there have been done ad-hoc tests due to irregularities in either the operation or the performance of the MPFM. Results from the proving of the MPFM from Trym start-up until April 2013 are shown in Figure 15.



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Figure 15: Deviation between Wet Gas Meter and Test Separator

One interesting and important result is that the total HC mass does not differ much. When it comes to the separate phases the results are not that promising. As earlier stated regarding the input to the allocation system the total HC mass is the most important parameter. The trend gives a confidence to the measurement of Trym even with significant deviations for both oil and gas phase. However, it is essential not to underestimate the importance of the phases and how they behave. During the two years of operation they have been used as an important quality indicator for the MPFM. Investigations have been started when the deviations have exceeded given threshold values. The observed difference in phase measurement between Test Separator and MPFM has been useful to identify actual problems with the MPFM.

A requirement in the Measurement Regulations is that "all valves of significance to the integrity of the metering station shall be accessible for inspection to secure against leakage". Just a short time before first-oil it was realised that there were several possible leakage points around the Test Separator during proving of MPFM. The reason for the possible leakage was that Trym is producing at 90 bar while Harald and Lulita is producing at 30 bar, and unfortunately there are not a single valve isolating the Test Separator from the test manifold. During one of the first verifications of MPFM it was confirmed that leakage was a problem and had to be dealt with. Compared to the Trym flow rates and the overall system uncertainty the leakage rate was very small (< 0.1%) but because it is a custody transfer system such a systematic error cannot be neglected but have to be compensated for. A procedure was made and the principle was to perform a test and calculate leakage rate before and after the proving.



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Figure 16: Possibility for leakage during proving of Trym MPFM

Coriolis meters are used on the liquid outlet from the Test Separator and the use of such meters has been thoroughly follow-up by NPD the last years. It has been experienced that tests of the meter at line conditions are necessary to confirm its suitability (Fosse, et al., 2013). As the production pressure from Trym has been 90-95 bar, it may have a significant impact on the Coriolis meters. The manufacturer of the Coriolis meters are quoting the pressure effect on flow rate accuracy to be -0.016% per bar, giving a correction of -1.44% at 90 bar. Early 2013 it was discovered that the pressure effect was not included in the flow calculations for the meters and a fiscal correction will be carried out. The Coriolis meter will be tested to confirm the pressure effect.

7.4 Sampling

As for traditional metering systems, sampling is important to determine the quality of the hydrocarbon stream when using MPFM. For Trym the results from the samples are used as input to the allocation system and the process simulation model.

Sampling is a great challenge when it comes to multiphase flow and the decision for Trym was to do sampling during proving. Both the oil and gas outlet from the Test Separator are equipped with sampling points for manual sampling. An external company is responsible for performing the sampling and there are procedures in place to secure as representative samples as possible. A report summarizing the sampling is issued after each sampling campaign. It contains vital information about time, pressure, temperature, measured rates etc., and it is a part of the quality assurance system. The report is also part of the final documentation for the specific sampling campaign.

For Trym the sampling is carried out every second month in combination with proving. One master and one backup sample are secured from both the oil stream and the gas stream. The samples are sent to a laboratory in the UK for analysis. It has been experienced that there is a long lead time



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from the sampling is performed until the final analysis report is issued. This causes some problems related to the allocation process and therefore also some financial issues.

The PVT model in the MPFM has been updated three times since start-up. The updates have only had minor impact on the measured total HC mass. The oil and gas phases have shown more significant changes. The conclusion is that for Trym, the PVT model in the MPFM is not very sensitive for the fiscal use of the meter.

7.5 Analysis results and the need for re-allocation

The laboratory in UK is accepted by all parties and is responsible for doing the analysis of the Trym samples. Oil and gas samples are analysed. Oil rates, gas rates and the two analyses are used to calculate a recombined total HC stream.

The analysis report is evaluated by both Maersk and DONG before it is finally approved and used. There have been a couple of instances where the results have been questionable, with the results that the backup samples have been analysed and used. There are procedures in place both at Maersk, the sampling company and the laboratory on how to perform the different tasks related to sampling, analysis, reporting and validation.

In the agreement for producing Trym it is described how the recombined composition shall be handled in the allocation. Since there is a time delay from a sample is taken and until the new recombined composition can be implemented, a bias in the allocation will occur. When a new composition is available, it will be used to re-allocate the production from the time the sample was implemented, and back to the time halfway between the time the last sample was taken and the time the second last sample was taken. This is called the mid ranging principle and has shown to give some challenges.

Figure 17: Mid ranging principle



Page 19

As long as the field is producing, the Gas Liquid Ratio (GLR) will increase gradually over time. The principles in the allocation system do not reflect this gradual change. The allocation system uses the last available composition with its static GLR, until it is updated. As the GLR will increase over time the reallocation will change the amount of oil and gas allocated to the Trym owners. The gas is under-allocated until a new composition is available and reallocating is done, and the DUC owners have to return gas to Trym. It is the opposite situation for condensate. The total BOE (Barrels of Oil Equivalent) is almost conserved, but because of the difference in value for a BOE of gas compared with a BOE of condensate this will cause some financial implications. It has also shown to be a challenge when forecasting the production and performing daily nominating of gas production.

7.6 The Authorities

The Norwegian Petroleum Directorate has confirmed that the good dialogue between Dong/Maersk and the authorities both in Norway and Denmark had been maintained during the design, testing and operation of this metering application.

The Danish Energy Agency has followed the good collaboration between the Maersk and DONG and their approach and dedication through the design phase to operation, and do appreciate and value the good understanding and dialogue between the DEA and NPD. The DEA has confirmed that it finds that despite of the complexity of the whole downstream allocation system, that the parties have found a good robust metering- and allocation system that with continuous focus, proper adjustments and improvements is a satisfactory solution for a metering system for marginal fields3.

7.7 What could have made the metering system even better?

Even if the overall experience from operation of the Trym metering station is positive, there are things that could have been done differently. Listed below are some main items that could have made the metering system even better.

• A service agreement with the supplier of the MPFM that includes remote access and frequently performance checks would have been beneficial for this application. The workload on the Maersk engineer following-up the Trym metering system would have been relieved and it would have added more expertise to judge the performance of the system. Based on the experience from the operation of Trym and other MPFM's on the DUC installations, Maersk is about to sign a service agreement with the supplier regarding remote access and surveillance of MPFM's.

• Trym consist of two wells and the compositions of the fluids from the wells are comparable. Changes in flow rates from the different wells will not have a significant influence on the composition of the HC stream. This may change over time, and will then cause a higher uncertainty in the metering and allocation. One subsea MPFM on each well would, based on

3 Source: DEA



Page 20

each wells composition and the wells flow rate, make it possible to calculate a dynamic composition to use in the allocation.

• The Test Separator was upgraded as part of the Trym project but still it carries a relative high uncertainty (approx. 1.8% on total HC mass). A reduced uncertainty for the Test Separator would give a better confidence to the reference measurements.

• Possible solution to reduce the Test Separator overall uncertainty is to install a pump in front of the Coriolis meters to reduce the risk of flashing in the Coriolis meters. Change out of the orifice meter on the gas outlet to an ultrasonic meter will reduce the risk for errors during operation of the meter.

7.8 Best practice

Best practices in operation of MPFM have been described in several publications before (Couput, 2011) (American Petroleum Institute, 2013) (Norwegian Society for Oil and Gas Measurement (NFOGM), 2005). However, below is a list of what is considered as best practice based on the experience done after operation of MPFM for Trym.

• Be prepared before start-up of the metering system (Quality management system, organisation, back-up solutions, etc.).

• Close cooperation with the authorities during both planning and operation phase. • Metering and allocation specialist involved in preparation of commercial agreements to

secure the possibility to operate the metering system in a proper and decent way. • Frequent verification of the multiphase meter against the reference. • Establish service agreements including remote access solutions for the MPFM supplier. • Periodic maintenance of the multiphase meter by use of the MPFM supplier. • Establish check parameters and accept criteria • Storage of all vital data • Use of supplier if mismeasurement occurs. • Close follow-up of metering system by dedicated personnel.

8. CONCLUSIONS

The use of the multiphase flow meter as the fiscal meter for the Trym field has been a success, given the conditions approved by the authorities and the licenses. There are several reasons for this success, but most important was the awareness to the metering system and its properties during the project phase and further into the operation phase.

Commercial agreements between the licenses have also been a factor for success as they are describing a regular and frequent verification of the multiphase flow meter against the Test



Page 21

Separator. Such agreements may also serve as an obstacle that makes it impossible to operate the metering system in accordance with the regulations.

The quality management system prepared for Trym metering gives a proper foundation in operating the system. The quality management system is custom made for Trym metering system and includes all main tasks that are performed. Preparation of the organisation so it is ready for operation before first oil is vital.

The results from verification of the MPFM against the test separator shows that the total HC mass does not differs more than approximately 4%. That gives a confidence to the metering system and its suitability in this application.

During operation the experience may be summarised as this: A simplified metering system that is based on the use of MPFM needs a thorough follow up by dedicated personnel with the right competences. It is also important to prepare for a fall back plan in case of failures to the MPFM. It also takes a long time to rectify in case of failures. Proactivity is therefore needed up front to avoid mismeasurement or shut-in of the production due to lack of measuring.

Using MPFM as fiscal meter is possible, but it needs to be done with a high attention to the complete system to avoid situations that may require corrections afterwards.

9. BIBLIOGRAPHY

American Petroleum Institute, 2013. Measurement of Multiphase Flow MPMS 20.3, Washington: API.

Couput, J.-P., 2011. Subsea multiphase measurements: where we are and what's next from an oil & gas operator perspective. Tønsberg, 29th International North Sea Flow Measurement Workshop.

Danish Energy Agency, 2012. Oil and Gas Production in Denmark 2012, and subsoil use, Copenhagen: Energistyrelsen.

Energy Institute, 2012. HM 96 - Guidelines for the allocation of fluid streams in oil and gas production, London: Energy Institute.

Fosse, S., Vervik, S. & Øiestad, O., 2013. Coriolis Meters for Fiscal Applications. Houston, The Americas Flow Measurement Conference.

Norwegian Ministry of Petroleum and Energy, 2010. Avtale mellom Norge og Danmark om utvinning og transport av petroleum fra Trymfeltet på norsk kontinentalsokkel til Haraldinnretningen på dansk kontinentalsokkel. Oslo: lovdata.no.

Norwegian Ministry of Petroleum and Energy, 2013. Facts 2013. Oslo: Norwegian Ministry of Petroleum and Energy.

Norwegian Petroleum Directorate, 2001. Regulations relating to measurement of petroleum for fiscal purposes and for calculation of CO2 tax (The Measurement Regulations). Last amendment 8.3.2013 ed. Stavanger: The Norwegian Petroleum Directorate.



Page 22

Norwegian Petroleum Directorate, 2012. Prosjekt - Måling - Eierskapsallokering med bruk av flerfasemålere (Allocation by use of Multiphase meters). Stavanger: Offentlig Elektronisk Postjournal (oep.no).

Norwegian Society for Oil and Gas Measurement (NFOGM), 2005. Handbook of Multiphase Flow Metering. 2nd ed. Oslo: NFOGM.

Pinguet, B., Vågen, N., Vethe, E. & Smith, M. T., 2012. Fiscal Allocation: A new endeavour for Multiphase Flowmeter. Kuala-Lumpur, South East Asia Flow Measurement Conference.

Thorn, R., Johansen, G. & Hjertaker, B., 2012. Three-phase flow measurement in the petroleum industry. Measurement Science and Technology, p. 17.

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1

Performance of subsea multiphase meters and topside test separator during well testing, after Skarv field start-up

Barbara M. Wrobel1, Andrew Hall2, Lukasz H. Wrobel1

1BP Norge AS, 2BP Exploration Operating Company Ltd. 1. Introduction Since start-up of the Skarv field in December 2012, subsea multiphase flow meters (MPFMs), and a topside test separator have been routinely used for well testing of both oil and gas-condensate wells. There are five MPFMs installed on Skarv, with three installed on the oil-producing templates and two on the gas-condensate producing templates. There are two production headers on each template and one test header where the MPFM is located. Flow is directed either to the test or to the first stage separator through one of two risers. Figure 1 shows schematically the options for routing flow from the oil and gas-condensate wells, either through multiphase meters or directly to the test or first stage separators. A number of wells share one MPFM on each of the production templates. Therefore for monitoring and historical tracking purposes, the positions of the diverter valves for each of wells are plotted against time, as shown in Figure 2. The reasons why MPFMs are used for well testing in addition to the test separator include the following: - To allow for metering of flowrates from oil and gas-condensate wells which have been

drilled in different reservoir sections having different CGRs and GORs. This is especially important for a field with a large number of producing wells. There is also the possibility of testing of two or more wells with different GOR using the test separator and interpolating values for individual flowrates for each well from the total flowrate; however, this method would give increased uncertainty;

- Increased testing capacity by using the MPFM when occasionally the test separator is not available, for example due to cleaning or intervention;

- Surveillance of well performance during start-up using the MPFM, while the test and first stage separators are already in use.

According to [1], the main advantage of the MPFM over the test separator is reduction in time to perform measurement. Due to accumulation of liquid, the potential for slugging in the flowlines and risers, and the need to allow the separator to fill and stabilise when changing over wells for test, the response time for stable measurement using the MPFM is much shorter than for the test separator. This paper consists of several examples, presenting performance of the subsea multiphase meters and the test separator on Skarv, as well as associated challenges.

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5

TABLE 1 CALIBRATION OF THE MULTIPHASE METER FOR NEGATIVE WATER CUT

1) Current mass attenuation coefficients from diagnostic data: LE HE OIL -0.0248837 -0.0172139 WATER -0.0348428 -0.0170521 GAS -0.0257691 -0.0180237 Calculation of operating point for oil-gas-water mixture from diagnostic data: LE -2.51319 GVF 97.51% HE -1.80647 WC -28.89% 2) Keeping the same GVF, but adjusting OIL LE and HE to give known value of water cut: LE HE OIL -0.0163627 -0.0144886 (not physically possible) 3) Calculation of operating point for empty pipe count rates, to give known, positive value of water cut and the same GVF: March 2009 modified LE 30219 30580 HE 17218 17283 After this procedure was employed, the resulting water flowrate had a positive value (~1 m3/h) and it was still possible to differentiate between the amounts of water from each of the gas-condensate wells, depending on which well was currently connected to the MPFM. In case of the MPFM for the oil wells, the measured water cut was positive from the start of using the meter (water flowrate >3 m3/h). Water cut measured by an MPFM employing a dual energy gamma source is sensitive to the amount of sulphur in oil; therefore it is recommended to test for sulphur and provide it together with the hydrocarbon composition for calculation of correct oil mass attenuation coefficient. The composition used for initial setup of the meter did not contain sulphur content. A recent PVT test of Skarv oil showed that it contains a small amount of sulphur. The PVT will be updated with this information and this will decrease the indicated water flowrate slightly. In order to get confidence with using multiphase meters it is advisable to get to know deeply its working principles, to be aware of ways of calibration and to be acquainted with the multiphase meter standard maintenance procedure, such as updating composition of hydrocarbons and water (including water salinity) whenever they change or to perform in situ tests with oil or water samples [4]. 4. Test Separator The test separator operating pressure has been fairly constant, at around 80 bara, however the temperature fluctuates from 50 to 80oC with a difference of up to 10°C between the gas and oil legs. The gas flowrate out of the test separator is measured by a differential pressure Venturi meter; this meter is instrumented with two differential pressure transmitters, allowing for increased turndown, with a low flowrate range (0 to 7 mbar, 0 to 818 m3/h) and a high flowrate range (0 to 500 mbar, 0 to 6 920 m3/h). The oil flowrate out of the test separator is measured by one of two differential pressure orifice meters (designed for a low flowrate range with a maximum of 22 400 kg/h and a high flowrate range with a maximum of 256 600 kg/h). Water flowrate is measured by a magnetic meter with a measurement range from 6 700 to 67 000 kg/h [5].

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FIGURE 5

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7

The correction calculation consists of two parts: for multiphase meters and for the test separator. Petroleum Experts PVTp software has been used for calculation of gas and oil densities, vapour CGR, GOR, Bo and Bg, at any pressure and temperature conditions (within the MPFM and test separation ranges), based on PVT data for the oil and gas-condensate wells. Calculated parameters were used for conversion of flowrates from line conditions (multiphase meter or test separator) to standard conditions [8]. A single-stage flash from line to standard conditions was simulated using Constant Composition Expansion experiment. Although a real fluid separation is performed for three separation stages, a single-stage flash was performed, for consistency with the multiphase meter and test separator calculations. An equation of State (EOS) tuned for Skarv fluids was used in order to minimise uncertainty in the results [9]. In addition, Petroleum Experts PROSPER was used for calculation of water compressibility, Bw, within the operating pressure and temperature ranges. If hydrocarbon composition changes then new PVT properties will need to be calculated. This also applies for changes in water composition and salinity. A well test is performed based on a long series of data (24h test, with test point every 2 minutes), which after conversion of rates to standard conditions is then averaged to single flowrate values, as a final well test result. 5.1 Flowrates at MPFM Conversion of flowrates from multiphase meter line conditions to standard conditions was performed using equations (1) to (3). The total oil flowrate includes oil dissolved in gas, and the total gas flowrate includes gas dissolved in oil at meter conditions, which at standard conditions appear as free phase. Qvo_sc = Qvo_MPFM / Bo_MPFM + CGRMPFM*(Qg_MPFM / Bg_MPFM) (1) Qvg_sc = Qvg_MPFM / Bg_MPFM + GOR *(Qo_MPFM/Bo_MPFM) (2) Qvw_sc = Qvw_MPFM / Bw_MPFM (3) PVTp software produces a table with discrete values at pressure and temperature steps. Therefore, interpolated values at the exact pressure and temperature of the MPFM and test separator are calculated based on weighted average: Bo_av=[(1/x1)*Bo1+(1/x2)*Bo2+(1/x3)*Bo3]/(x1+x2+x3) (4) Where: x1 - is the first smallest distance between Bo @ line p,T and available Bo (at discrete p,T) x2 - is the second smallest distance between Bo @ line p,T and available Bo (at discrete p,T) x3 - is the third smallest distance between Bo @ line p,T and available Bo (at discrete p,T) Comparison of MPFM flowrates at standard conditions to flowrates calculated from the spreadsheet is presented in Figure 6. Average relative errors for gas and oil flowrates are 2% and 3.7%, respectively, with maximum errors of 4% for gas and 10.5% for oil. This deviation is due to differences between the EOS used in the multiphase meter and in the PVTp software, with a higher impact at high operational pressure. Three out of four well tests with Qo error of 9 to 10%, were performed for higher pressure at the MPFM (130 bar) than the average of 97 bara.

FIGURE 6CONDITIO

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6 QUALITY CHONS.

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= Qvw_sep / B

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= Qliq_sep / (W= (1-WCsc)*

WCsc*Qliq_

ction of flow

tion, gas floogrammed cbara, Z = 0.laced offlinsoftware, w

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wrate from ted to stand

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ATES FROM M

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to (10). dissolved addition, water is e.

(5) (6)

(7) l (8)

(9) (10)

orrected. = 0.9 for numbers ity from

mperature

ditions is l density m PVTp

9

software. Such replacements resulted in changes of gas flowrate up to 33% and oil flowrate up to 18%, which is a significant difference that needed to be accounted for. Water flowrate converted to standard condition resulted in change by 1.5%. Both Venturi and orifice meters are differential pressure flow meters, which are mass (density) dependent. Therefore without knowing correct density, these meters will not give accurate measurement. An improvement in the system would be installation of a densitometer, especially on the oil outlet of the test separator. However, the current offline correction is providing good results with substitution of densities of gas and oil from PVTp software. This calculation will have to be maintained as it requires update as composition changes. 6. Analysis of well testing results Table 2 contains examples of calculated errors of oil and gas flowrates at standard conditions, based on well test results for a few months after field start-up. Flowrates obtained from the multiphase meter and test separator are compared along with the flowrates calculated by the well testing spreadsheet. As a rule of thumb, the aim is to obtain relative differences of up to ~5% for gas and ~10% for oil flowrates between the meter and separator. Cells highlighted in grey in the table represent measurements that are within the target, on average within 1.4% for gas rates and 5.5% for oil rates. This proves good agreement between the test separator and the multiphase flow meters. Measurements marked with * or **, have much bigger deviations. Comparing flowrate deviations between the MPFM and the spreadsheet (columns 5 and 8) for all well tests, it can be seen that errors in white cells are of the same size, as for “good” measurements in grey area. This indicates that conversion of meter rates to standard conditions is not a problem, and rather something else has impact on the deviations. In order to explain these large deviations, consider Table 3. Here, gas and oil flow velocities are calculated at two locations: in the multiphase meter body and at the inlet into the test separator. Velocities were calculated from flowrates at MPFM and test separator conditions, taking into account local gas and liquid fractions. Tests were performed using the MPFM and test separator simultaneously. Comparing ratios of gas to oil velocities (columns 7 and 8), which represent how much faster gas flows in relation to liquid and ranking them for 1 being the highest and 4 being the lowest (columns 9 and 10), it can be seen that it is not necessarily always the case that the MPFM and test separator ranks are the same. For example, for gas well A, test no. 3, gas has the highest velocity compared to liquid in the MPFM and at the inlet to test separator. However, for gas well A, test no. 2, gas has 2nd highest velocity in the MPFM, but it is the lowest on test separator (see, also Figure 7). This can explain why sometimes there is a difference in GOR or CGR between the MPFM and the test separator, defined as gas to oil flowrates at standard conditions due to differences in gas and oil velocities. Such cases happened for well tests lasting 24h, while well conditions and flowrates were stable. The velocity difference is related to decreasing values of pressure and temperature from the MPFM (operating at 100 bara, 100 oC), through the flowline, up to the test separator (operating at 80 bara and 70oC). In addition, the geometry of flowlines may have influence on gas and oil velocity differences. Resulting errors may be even further enlarged, when oil and gas flowrates are converted to standard conditions.

10

TABLE 2 CALCULATED RELATIVE DEVIATIONS OF GAS AND OIL FLOWRATES BETWEEN MPFM, SPREADSHEET AND TEST SEPARATOR, FOR PERIOD OF A FEW MONTHS AFTER FIELD START-UP.

Max. Qg @SC deviation, % Max. Qo @ SC deviation, % Type of well Well

test no.

MPFM and test separator

Sheet and test separator

MPFM and sheet

MPFM and test separator

Sheet and test separator

MPFM and sheet

Gas well A 1 0.12 1.27 1.15 24.43* 26.97* 2.04Gas well A 2 8.14* 8.39* 0.23 1.77 2.44 0.66Gas well A 3 0.29 2.14 1.85 2.35 3.12 5.55Gas well B 1 0.74 2.51 1.76 3.17 0.63 3.82Gas well C 1 2.87 0.52 2.33 9.32 12.04*,** 2.49Oil well A 1 2.89 0.24 2.64 7.27 9.86 2.42Oil well A 2 11.13* 8.57* 2.36 9.21 11.95*,** 2.51*- Anomalies in calculated deviations may be related to differences in gas and oil velocities in multiphase meter and in test separator. ** - Deviations may be influenced by different PVT models used in the spreadsheet and MPFM for conversion of flowrates to standard conditions. TABLE 3 CALCULATED REAL VELOCITIES (WITH INCLUDED SLIP) IN MPFM AND AT INLET TO TEST SEPARATOR, BASED ON OIL AND GAS FLOWRATES @ LINE CONDITIONS.

Type of well

Well test no.

MPFM Ug, m/s

Sep. Ug, m/s

MPFM Uo, m/s

Sep. Uo, m/s

MPFM U_g/U_o

Sep. U_g/U_o

MPFM Rank

Sep. Rank

Gas well A 1 11.95 2.64 0.37 0.47 32.22 5.58 4* 3 Gas well A 2 17.75 3.29 0.43 0.60 41.44 5.53 2 4* Gas well A 3 19.02 4.04 0.44 0.62 42.75 6.49 1** 1** Gas well B 1 18.81 4.00 0.48 0.65 39.00 6.17 3 2 *- The highest Qg/Qo @ LC on MPFM (for MPFM Rank) or test separator (for Sep. Rank) **- The lowest Qg/Qo @ LC on MPFM (for MPFM Rank) or test separator (for Sep. Rank)

FIGURE 7 RATIOS OF GAS TO OIL FLOWRATES AT MPFM AND TEST SEPARATOR CONDITIONS FOR THREE WELLS TESTS FOR GAS WELL A AND ONE WELL TEST OF GAS WELL B.

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14

and 54 bar). Drawdown drives oil and gas velocities. With higher drawdown, more free gas is flowing from wells, which is reflected by higher GOR. Based on data shown in Figures 10 to 12 the conclusion is that measured by MPFMs CGR values for gas-condensate wells and GOR values for oil wells are very consistent throughout the time observed. Multiphase meters have provided valuable information about the behaviour of Skarv wells during early field operation, which in addition to production surveillance and wells allocation purposes, are also used for constraining the dynamic model.

7. Summary This paper presents several issues related to performance of subsea multiphase meters and a test separator after start-up of the Skarv oil and gas-condensate field: 1) routing possibilities for oil and gas-condensate wells through the multiphase meters and to the test separator, 2) an issue with negative water readings from multiphase and magnetic meters for very low water flowrate (< 1 m3/h), as well as calibration solutions for the multiphase meter, 3) correction of Venturi and orifice flow meters, due to programmed constant gas and oil densities and gas compressibility factor, 4) potential impact of flow instabilities on the test separator, 5) importance of correct calculation of oil, gas and water flowrates to standard conditions, together with presented equations, 6) presented examples of well test results for Skarv oil and gas-condensate wells, including: - study of short term effects that influence CGR and GOR ratios: individual oil and gas velocities in the multiphase meters and on the inlet to the test separator, PVT models for calculation of standard conditions flow rates, - description of mechanisms that may influence CGR and GOR ratios in long term perspective:

o for the gas-condensate wells: immobile condensate droplets below critical saturation and compositional grading of reservoir fluid,

o for the oil wells: gas injection support, which may affect produced fluid composition and the value of drawdown.

The use of subsea multiphase meter technology in combination with a topside test separator has been demonstrated to provide a robust way to determine well production rates in a system of subsea tiebacks, where wells are manifolded into flowlines and therefore otherwise difficult to test effectively.

15

8. Acknowledgments We would like to acknowledge Skarv Base Management and Reservoir teams for support and interest in performance of the MPFMs and the test separator. We also would like to acknowledge BP and partners in the Skarv field: Statoil Petroleum AS, E.ON E&P Norge AS and PGNiG Upstream International AS for consent for publishing this paper and presenting it at the 31st International North Sea Flow Measurement Workshop conference in Tønsberg in Norway, 2013. 9. Abbreviations MPFM - multiphase meter GOR - gas to oil ratio [Sm3/Sm3] CGR - condensate to gas ratio [Sm3/Sm3] GVF - gas volumetric fraction [-] CVF - condensate volumetric fraction [-] WVF - water volumetric fraction [-] PVT - pressure volume temperature STO - stock tank oil LC - line conditions SC - standard conditions Bg - volume factor for gas [m3/Sm3] Bo - volume factor for oil [m3/Sm3] Bw - volume factor for water [m3/Sm3] BHFP - bottom hole flowing pressure [bara] 10. References [1] Norwegian Society for Oil and Gas Measurement, Tekna. Handbook of multiphase flow

metering; 2005. [2] Skarv ProcessNet pages: well routing to test separator, wells on MPFM, flowrates on

MPFM and test separator. [3] Framo Engineering AS: PhaseWatcher Vx Working Principles [4] Framo Engineering AS: PhaseWatcher Vx Maintenance Procedure. [5] Internal BP document: ICS functional specification system 20, separation and

stabilisation systems. [6] Universal Flow Monitors, www.flowmeters.com. [7] Flow Handbook. A practical guide: measurement technologies - applications - solutions,

Endress+Hauser Flowtec AG, Reinach; 2006. [8] Dake LP. Fundamentals of reservoir engineering. Elsevier Science B.V; 1978. [9] Internal BP document: PERA. Skarv, Snadd, & Idun Surface and Reservoir EOS Fluid

Model. Norway; 2006.

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Challenges with salinity measurements in multiphase flow metering

Anton Gryzlov, Erik Undheim, Ebbe Nyfors, Lyndall Jordaan, Stine Jæger Alvær, Elin Steinsland

Emerson Process Management, Roxar Flow Measurement 1. Introduction

In situ measurement of multiphase flow over a wide range of flow conditions is not a trivial task and imposes various challenges for multiphase meters. Although the metering technology has evolved over the last decade, there is a potential for improvement in certain areas. Experience shows that one of the main sources of uncertainty for multiphase meters is insufficient/incorrect knowledge of fluid properties: such as density, viscosity and permittivity. In multiphase meters based on the measurement of flow permittivity, the measurements are especially affected by changes in the salinity of the water phase. This is due to the strong relationship between the water permittivity and the conductivity and hence the salinity of the water. The measurements are predominantly affected by this dependence in water continuous flow.

Traditionally, the salinity has been obtained from a water sample, and the result of the lab analysis was used as an input to multiphase meters. The response time with this approach is limited by the sampling interval and taking a representative sample was not always possible. In order to reduce the response time and the uncertainty of the multiphase meters outputs, the requirement for the modern multiphase flow meters is to be independent of changes in water salinity. In addition, the salinity of the water phase is a valuable parameter for flow assurance purposes. In order to meet the market needs in multiphase and wet gas metering Roxar has developed dedicated sensors for salinity measurement for a broad range of multiphase flow conditions as shown in Figure 1. These sensors are based on various principles but they all employ microwave technology. The current portfolio for a salinity measurement system includes the following tools:

- The formation water detection function in the Roxar Wetgas meter: designed for the detection of the onset of the formation water production in high gas volume fraction (GVF) applications. It is based on a two-parameter measurement of the resonant frequency and Q-factor of the microwave cone resonator [1, 2].

- The microwave surface sensor designed for the salinity measurement in wet gas flow at high to ultra high GVF, i.e. a wet gas or a multiphase flow with high gas volume fraction. This sensor is designed for use in both the Roxar Wetgas meter and Roxar Multiphase meter and it is a ceramic cavity resonator mounted in the wall of a meter body [3, 4].

- The three-probe differential transmission sensor, further referred to as microwave salinity probe: for measuring the salinity of the water in a water continuous multiphase flow. The sensor is based on a two-parameter measurement, i.e.

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2

measurement of differential attenuation and phase. This paper focuses on the development work of this sensor and the challenges that have to be overcome related to gas-liquid three phase flow.

- Roxar is also developing a technology for measuring the salinity of water in oil continuous multiphase flow. A patent application has been filed.

The operating envelop of the salinity measurements is illustrated in Figure 1. Note that the formation water detection is not depicted in this figure, as this is a functionality within the Roxar Wetgas meter and does not require any additional hardware. Also note that values of GVF in Figure 1 should be considered only as the indicative of the boundaries between different flow regimes and may be different for actual meters and applications.

Gas Volume Fraction

Roxar Multiphase meter Roxar Wetgas meter

0 – 100% 90– 100%

Microwave surface probeMicrowave salinity probe

90– 100%0– 90%

Figure 1. Metering equipment provided by Roxar and the corresponding salinity sensors.

The microwave ceramic sensor can calculate an absolute salinity value based on a

two-parameter measurement of the resonant frequency and Q-factor. This sensor is to be used with Roxar Wetgas meter, where the GVF>90% and the absolute amount of water is small. The lower limit of the operating range is defined by the amount of gas to ensure low-loss conditions and the existence of a resonance peak.

With the decrease in GVF the flow transforms from wet gas to multiphase and this brings a new challenge for a metering technology. For a multiphase flow with significantly higher amounts of water, the resonator-based sensors are not feasible to use. In order to be able to measure salinity in water continuous flow over a wide range of operating conditions, Roxar has developed a transmission based microwave salinity probe. This sensor consists of three antennas, of which one is for transmitting and the other two are for receiving. The sensor measures the effect of the flow on the propagation of the microwave signal in the

3

volume between the antennas. The salinity of the water phase and the local water-liquid ratio can then be calculated.

The results of the initial development stage and basic theory behind sensor principles are given in [5]. Currently, the sensor design and algorithm development work has been finalised. Several flow tests have been performed, which indicate that the developed sensor is capable of measuring conductivity of the water phase for multiphase oil/gas/liquid flows and can improve the performance of the multiphase meter. The upper limit of the operating range of this sensor is defined by the onset of annular dispersed flow regime, which is specific to wet gas flow. The operating envelops of both transmission sensor and ceramic sensor overlap, which ensures that salinity can be measured for all possible gas volume fractions as depicted in Figure 1. This sensor can be considered as an additional functionality, which improves the performance of the multiphase flow meter and adds additional flow monitoring capabilities.

This paper summarises the main highlights of the microwave salinity probe development process and addresses the difficulties in measuring conductivity in multiphase gas-liquid flows. First, the measuring principle of the Roxar microwave salinity probe and the physics behind the technology are discussed. Secondly, the challenges in the salinity measurement, which are generic to gas-liquid three-phase flow, are outlined. Next, based on actual flow tests the data processing techniques are discussed. The developed algorithm is further used to calculate conductivity and provide an indication of the measuring uncertainty. The paper ends with conclusions and suggestions for the future work. 2. Theoretical background

The permittivity of a multiphase mixture depends on the permittivity of the components and the actual distribution of the fluids in the measurement cross-section of the pipe. Because the permittivity of water depends on the conductivity, the permittivity of a mixture of water, oil and gas then also contains information on the conductivity. The Roxar salinity sensor is based on measuring the complex permittivity of the mixture in order to obtain the conductivity of water.

The water molecule is polar, i.e. it has a permanent dipole moment, which means that the real part of the permittivity of water is high compared to that of both oil and gas. Therefore the permittivity of a water-continuous mixture is dominated by the permittivity of water and the volume fraction of water. If the water contains salt, the real part of permittivity is slightly reduced, but the imaginary part is increased significantly making the water even more dominating. As a first approximation the oil droplets and gas bubbles are considered as void in the water host. To find the conductivity of the water from the mixture permittivity one needs to perform a two-parameter measurement. There are several options as described in Sec. 2.5.4 of [6]. These are e.g. measuring the Q-factor and resonant frequency of a resonator, the attenuation and phase shift with a transmission sensor, or measuring one parameter at several frequencies. Because a water-continuous mixture, where the water is conductive, is typically a high-loss medium for electromagnetic waves, the resonator method is not so well suited. The Roxar salinity sensor is therefore chosen to be a transmission sensor, where the propagation of electromagnetic waves between small closely spaced antennas is measured.

4

To cancel the effects of the antennas, and all other external factors, differential transmission is used. One antenna is used for transmission, and two antennas at different distance are used for reception. The difference in the received signals is then caused by the difference in propagated distance.

The basic operation can be understood based on plane wave theory, i.e. how plane waves are affected by the permittivity of the medium. To estimate the permittivity of the medium, one needs to know the permittivity of the constituents. The permittivity of water is described by the well known Debye relation

rdrdrrs

rr jfj

εετπ

εεεε ′′−′=+

′−′+′= ∞

∞ 21 (1)

where the subscript r means that values relative to the permittivity of vacuum are used, and f is the frequency. Values for the static permittivity ε’ rs, the infinite frequency

permittivity ε’ r∞, and the relaxation time τ can be found in the literature. In the frequency range of interest (up to a few GHz) the real part is in the order of 80, and the imaginary part is far smaller. When water has ionic conductivity because of salt content, the imaginary part gets an additional component:

+′′−′=

02 επσεεεf

j rdrdr (2)

where ε0 is the permittivity of vacuum (8.854.10-12 F/m) and σ is the conductivity. The 1/f-dependence of the conductivity component means that the imaginary part of the permittivity of water will be far larger at low frequencies than at high frequencies and dominating over the real part even for a low salinity. This will also be the case in a water-continuous mixture.

The permittivity of oil and gas can be estimated from equations found in the literature. The values are far lower than for water, which are roughly 2.1 for oil, and <2 for gas, and the exact values have therefore little effect on the permittivity of the mixture εm, which can then be estimated by a mixture equation, e.g. the Brüggeman equation. For a thorough review on mixture equations see [7].

The electric field of a plane electromagnetic wave propagating in a medium is described by the equation

xkjxkjjkx eeEeEE ′′−′−− ⋅⋅=⋅= 00 (3)

where k is the wave vector and x is the propagated distance. It is seen that the real part of k determines the phase of the wave, while the imaginary part determines the attenuation. The wave vector is given by

−

==′′−′= mrmrmr c

fj

c

f

c

fkjkk επεπεπ 2

Im2

Re2

(4)

Introducing the real and imaginary part of the permittivity gives

′′′

++−′

=′′

′′′

++′

=′22

112

2,11

2

2

mr

mrmr

mr

mrmr

c

fk

c

fk

εεεπ

εεεπ

(5)

5

A study of this equation shows that in a high-loss situation ( mrmr εε ′>>′′ ) the following

approximation can be used

kkkk mr ′=′′′′

=′ ;20

ε (6)

where k0 is the wave vector in vacuum. This means that both the phase and the attenuation depend only on the imaginary part of the permittivity. Measuring both therefore does not give the extra independent information expected from a two parameter measurement. This means that the real and imaginary part of the permittivity must be of the same order of size, or the imaginary part smaller than the real part, for the two-parameter measurement to work. Because of the frequency dependence of the conductivity component

of the imaginary part of the permittivity of water, the ratio mrmr εε ′′′ / also depends on the

frequency so that it decreases with increasing frequency. This means that the measurements of phase and attenuation must be performed at a high enough frequency to yield independent information. This is illustrated by the graphs in Figure 2, where the phase and attenuation of a plane wave have been calculated for a set of different conductivities and water-liquid ratio (WLR) values at two different frequencies. For high conductivity and WLR the frequency should be 1 GHz or preferably even higher for the two-parameter method to give good resolution.

Figure 2. Differential attenuation and phase for different WLR and salinity from the plain wave theory (differential distance 1cm). Radiation frequency 0.1GHz

(left) and 1GHz (right).

As mentioned above, it is also possible to perform e.g. phase measurements only, but on at least two frequencies. The reason of this choice would be the claim that phase measurements are always more accurate than measurements of attenuation, which is true in some applications of free-space transmission sensors. In that case one frequency must be in the high-loss region and the other in the low-loss region to yield independent information.

WLR WLR

6

Because the transition from high-loss to low-loss depends on the conductivity and water volume fraction (WVF), these frequencies must be changed with the conditions, or the lowest frequency must be very low to always be in the high-loss region. Because the differential phase measured at a low frequency is very small due to the large wavelength, this is very impractical. Roxar experience does not support the claim of phase measurements being more accurate than measurements of attenuation in this application, hence measuring both phase and attenuation at a high frequency is a clearly preferred choice.

In practice other phenomena also affect the measurements than explained by the plane wave approximation. One phenomenon is spherical spreading, i.e. the antenna transmits spherical waves, not plane waves. Another phenomenon is related to near-field effects. In the region around an antenna there are non-propagating reactive fields. The size of this so-called near-field region depends on the permittivity of the mixture, and may be significant compared to the distance between the antennas, when the local gas content is high. Another effect is the heterogeneity of the mixture, i.e. there may be a significant difference in the local WVF in the paths to the two antennas. This effect depends on e.g. the flow velocity, GVF, pressure, WLR and viscosity. Another effect comes from the influence of the pipe. The upper end of the GVF range for the sensor is partly defined by when the mixture is no longer a high-loss medium. Then reflections from the pipe walls, the cut-off frequency and propagating wave modes affect the measurements. These phenomena make the measurement situation far more complex than described by the plane wave theory alone, and call for advanced signal processing, and the use of more than two input parameters. However, the plane wave theory describes the basic underlying physics, which the measurement method is based on. The most significant consequence is the need of a high enough measurement frequency.

3. The sensor The prototype which was introduced in [5] consists of one probe with three small

closely spaced antennas in a triangular pattern. One antenna is used for transmitting and the other two for receiving. The receivers are at different distances from the transmitter. The phase shift and differential attenuation are measured, i.e. the difference between the two received signals. In this way no extra reference is needed, and the characteristics of the antennas are eliminated. The same concept has been maintained, except for the probe. A small dedicated high-pressure probe with a single antenna has been developed for this purpose (Figure 3).

The new antenna has a PEEK insulator facing the flow instead of glass or ceramic making it more efficient due to lower permittivity. Three such antennas are mounted in separate locations in the meter body, which is preferred to a single large cavity both from a mechanical integrity and sealing point of view. In addition, this antenna can be used on all pipe sizes, and has made it easier to find the optimal geometry and distances between antennas. The sensor design and the optimal distance between antennas have been extensively studied theoretically, by simulations using HFSS (High Frequency Structure Simulator), and from multiple tests in the lab and flow loops.

7

Figure 3. Microwave salinity probe installed in a spool piece (ID=87mm) for static

laboratory test. 4. Conductivity measurement with microwave salinity probe

The measured differential attenuation (∆A) and the differential phase (∆ϕ) depend on the complex permittivity of the multiphase mixture, which in turn is the function of water salinity and local water volume fraction

),( WVFSAA ∆=∆ (7)

),( WVFSϕϕ ∆=∆ (8)

Note that the local water volume fraction corresponds to the amount of water in the volume between the antennas and it is not equal to bulk water volume fraction. However, local water volume fraction is usually correlated to the bulk water volume fraction. For two-phase liquid-liquid flows (without gas) equations (7) and (8) provide accurate predictions of water salinity. With a known salinity, the conductivity can be calculated using special models, e.g. section 2.3.2 in [6]. Once no gas is present, the phases are normally well-mixed and the flow, which is homogeneous, is described well by the Brüggeman equation. In this case, the conductivity is simply given as

),( ϕσσ ∆∆= A (9)

The presence of gas, which is the case for multiphase flow, dramatically changes the situation. In this case, even if the gas fraction is small, the phases cannot be considered as homogeneously distributed, which limits the usage of the mixing formulas. This is illustrated in Figure 4 below, where the typical sensor response is given for a relatively low gas content. One can note significant variation of the measured signal, which was not observed on the data for the similar flow conditions (conductivity, water-liquid ratio, pressure and temperature) at pure liquid flow. This is due to explicit effect of the gas phase on the sensor measured outputs. Even if the gas fraction is small, there could be different flow scenarios corresponding to the actual phase distributions, which result in absolutely different sensor

8

response. In particular, gas bubbles can be located in either of the flow paths between the antennas, can cover the antenna fully at the moment of measurement or in the extreme case there could be no liquid at all in a measurement volume. The variation of the signal will be affected by the size of the bubble: for homogeneously distributed dispersed bubbly flow the variation of the signal will be lower. However, it will still be significant compared to pure liquid flows, as the characteristic size of an oil droplet in water is significantly smaller than the typical bubble size.

Figure 4. Example of sensor response for three-phase flow. Differential attenuation sweeps

(left) and differential phase (right) at GVF=25%.

The effect of gas has a direct impact on the output of the microwave salinity probe and requires special data processing techniques, such as optimal averaging, filtering and identification of extremes, etc. This is further illustrated in Figure 5, where the actual sensor response at a fixed frequency is compared for two-phase and three-phase conditions.

For two-phase liquid-liquid flow, as it follows from the plane wave theory, the output of the sensor, which is represented in Figure 5 (left) by differential attenuation and differential phase shift at 1GHz frequency, is defined by conductivity (or salinity) of water and local water volume fraction. The plotted differential attenuation and phase are obtained from averaging transient data over a certain time interval. The conductivity can then be easily predicted by using equation (9).

Once the gas is introduced in the flow, the actual sensor response is biased, as the conditions, which correspond to certain conductivity at 100% liquid flow may produce the same sensor output as for lower conductivity and different gas content. Hence, the conductivity cannot be calculated using a two-parameter approach due to the ambiguity of the sensor response. The presence of gas in fact introduces an additional unknown which needs to be taken into account by using a dedicated modelling approach.

The output of the sensor is affected not only by the bulk gas volume fraction but also by the actual interface between liquid and gas in the volume between antennas. This

9

distribution, which is referred to as a local flow regime, is defined by volumetric gas content, actual velocities of the fluids, operating pressure and to a certain extent by the pipe diameter. Note, that this local flow regime is different from the bulk flow regime observed in a pipeline. Accounting for the flow regime impact is not a trivial task, as the temporal variation of the gas-liquid distribution in the measurement volume of the microwave salinity probe is not easily predicted neither theoretically nor numerically. The experimental data is needed in order to understand how the actual flow conditions affect the interaction of microwave signal with gas-liquid interface and outputs of the sensor.

Figure 5. Illustration of the modelling challenge for three-phase gas-liquid flow. Data

corresponds to low pressure testing of an earlier version of the sensor. Differential attenuation and the phase are plotted at 1GHz. WLR changes from 60% to 100%.

These phenomena are not specific to the salinity measurements, but rather represent

general challenges in performing measurements in non-uniform multiphase flows. The effect of pressure on the sensor outputs is illustrated in Figures 6-7. The results correspond to data at 80% GVF at 1750MHz. Variation of the signal for a fixed pressure is due to a different water-liquid ratio. One can observe that at sufficiently high pressures the changes in differential attenuation and differential phase become less dependent on pressure. This is due to better mixing of the flow, which will be explained in more detail in Section 6 of the paper. The volumetric flow rates have a certain impact on the sensor output as well.

The resulting equation for calculation of conductivity in a three-phase flow is hence given by

),,( regimeFlowA ϕσσ ∆∆= (10)

Therefore, in order to calculate conductivity in three-phase flow one needs to account for the effects related to actual flow conditions inside the multiphase meter. The way this information is obtained in the Roxar microwave salinity probe constitutes the major part of the developed technology. Advanced modelling methods and software have been used to identify the optimal set of variables to be included in the models.

WLR

Salinity

WLR

Salinity

Flow Regime

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Figure 6. Differential phase as a function of pressure. Data correspond to GVF 80% are

plotted at 1750MHz.

Figure 7. Differential attenuation as a function of pressure. Data correspond to

GVF 80% are plotted at 1750MHz.

5. Test results The performance of the microwave salinity probe has been evaluated via multiple

flow tests. These include flow testing at Christian Michelsen Research centre in Bergen, Norway at low pressure, where both the sensor configuration and the modelling approach have been optimised. High pressure testing has been performed at K-lab, Statoil’s metering and technology laboratory at Kårsto, Norway.

The results for a sensor installed in a 87mm spool piece are presented in Figure 8 where the calculated conductivity for tested pressures is plotted against reference values of conductivity. The conductivity changes from 2 S/m to 14 S/m within the tested a temperature range of 15-60oC.

The demonstrated performance of the sensor both at low and high pressure is given in Figures 9 and 10. The results indicate that the developed sensor can measure the conductivity of water with an absolute uncertainty less than 0.5 S/m and lower relative uncertainty limit of 1%. One can note that the uncertainty of the results which correspond to high pressure data from K-lab flow test is significantly lower than for CMR flow loop, where the pressure was significantly lower. This is due to the better quality of the data obtained at high pressure testing because of a better mixing and possibly more homogeneous flow.

For a bubbly flow regime the size of a typical bubble is normally defined by turbulent forces, which act to break the gas bubbles into smaller ones, prevent their coalescence and disperse them into continuous liquid phase [8]. The rate of turbulence is defined, amongst others, by liquid velocity, which will be different at different pressures. At higher pressure the gas density is increased, which decreases its buoyancy and hence the relative velocity with liquid, i.e. slip. So even if the superficial velocities are equal (i.e. volumetric flow rates), the liquid velocity at no-slip conditions will be higher than it is in the flow with slip, resulting in a higher turbulence level and better mixing.

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Figure 8. Microwave salinity sensor performance: Calculated conductivity vs. reference

conductivity at multiphase flow loop.

Figure 9. Absolute uncertainty in

conductivity vs. reference conductivity. Figure 10. Relative uncertainty in

conductivity vs. reference conductivity.

The effect of flow homogeneity is also illustrated in Figure 11, where the absolute

uncertainty is plotted against reference GVF. One can note that the best performance is achieved at two-phase conditions (i.e. GVF=0%) and gradually deteriorates with the increase in GVF. This is caused by the increased heterogeneity of the flow due to presence of gas, which was discussed in section 3 of the paper.

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Figure 11. Microwave salinity sensor performance: Absolute uncertainty in conductivity for a

tested GVF range. 6. Further development

The development work for the microwave salinity sensor is summarised and it is ready to be released as a fully qualified product. The ongoing development is focused on extending the range in terms of the amount of gas the sensor can handle while still delivering an accurate value of conductivity. As it has been demonstrated in this paper, the new sensor is capable to measure salinity in the range up to 90% GVF (which roughly corresponds to the transition from multiphase to wet gas flow). It has been observed during internal testing that it is most likely to extend its operating range higher in GVF. Such a sensor, which is capable of measuring in a higher end of multiphase flow range, will inevitably have a lower sensitivity to smaller amounts of water, which is specific to such flow conditions. This may introduce the need of considering actual flow conditions and analysis of the placement of the sensor within the body of the multiphase meter. Similar type of analysis to identify the optimal location of the ceramic salinity sensor has been performed in [3]. 7. Summary and conclusions

The paper has introduced the microwave salinity sensor, which is capable of measuring water conductivity in multiphase gas/liquid flows over a wide range of flow conditions. The sensor has high sensitivity to salinity and, despite the challenges generic to multiphase flow, it is capable of measuring the conductivity of the water phase. The sensor is based on a two-parameter measurement, the differential attenuation and the differential phase

13

respectively, which together with information on the local flow regime, unambiguously provides conductivity of the water phase.

The sensor has been tested in low and high pressure flow loops. The test results show that the expected absolute uncertainty of conductivity measurement is 0.5 S/m with the lower relative uncertainty limit of 1%. The sensor has been tested within a conductivity range of 2-14 S/m through a test envelop which includes GVF 0-90%, water-liquid ratios 60-100%, pressures 5-125 Bar and temperatures 15-60oC.

9. References [1] Nyfors, E., Lund Bø, Ø. (2003), Compact wet gas flow meter based on microwave and differential pressure measurements, Proc. 5th Int. Conf. on Electromagnetic Wave Interaction with Water and Moist Substances, ISEMA 2003, Rotorua, New Zealand, pp. 146-153. [2] Compact Flow Meter, United States Patent, No. 6,915,707. [3] Gryzlov, A., Nyfors, E., Jordaan, L., Undheim, U., Braaten, N.A., Gregersen, S.E., Steinsland, E. (2012), Fluid Mechanical Aspects of wet gas metering, Proc. SPE Russian oil and gas exploration and production technical conference and exhibition, Moscow, Russia, SPE 160464. [4] Flow Measurement, International Patent Application PCT/NO2008/000013. [5] Lund Bø, Ø., Nyfors, E. (2002), Application of microwave spectroscopy for the detection of water fraction and water salinity in water/oil/gas pipe flow, Journal of non-crystalline solids, 205, 345-353. [6] Nyfors, E., Vainikainen, P., (1989), Industrial Microwave Sensors, Artech House, 351 pp. [7] Sihvola, A., Electromagnetic mixing formulas and applications, IEE Publishing, London, 1999. [8] Shoham, O. (2006), Mechanistic modeling of Gas-Liquid Two-Phase Flow in Pipes. SPE, 408pp

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31st North Sea Flow Measurement Workshop 22-25 October 2013

Copyright Onesubsea

Allocation & Fiscal Allocation Applications: A Comparison of Current North Sea Practice with Recently Issued Guidelines and Regulations

Bruno Pinguet, Michael Smith and Eivind Vethe, OneSubsea

1. ABSTRACT Multiphase and wet gas flowmeter measurement devices have been deployed in the North Sea and elsewhere for around 20 years. From the early days it has been known that, to work properly, these devices need information about the produced fluids. To enable conversion from line conditions to standard conditions, all multiphase and wet gas flow meters require inputs such as permittivity, conductivity, and density of each of the phases, plus PVT models. Several guidelines and regulations relating to North Sea operations have recently been published concerning the measurement of multiphase production streams for the purposes of fiscal allocations. These include the API Committee on Petroleum Measurement and Allocation Measurement of Multiphase (MPMS) Flow Chapter 20.3 which states requirements regarding measured inputs and PVT requirements along with guidance on how they should be managed. This paper reviews the methods listed under API MPMS 20.3, specifically those outlined in section 3.4.7 “Changing fluid properties over field life for maintaining inputs”, with a focus on reducing uncertainty of the final input values. This section is critical, because each method of evaluating fluid properties has its own uncertainty associated with it and choosing one over another will have an impact on final statement uncertainty. API MPMS 20.3 states that vendors have proposed specific technology solutions involving a variety of methods from in-line measurement of some fluid properties to extracting samples to measure the properties in lab conditions. This paper reviews what can be applied today and the practicality of these methods versus the trend of the industry over the last 15 years. At the same time as API worked on its guideline document, national governments have developed their own requirements for technologies to be used for allocation within their borders. The North Sea is a leading area where governments have shown a willingness to define standards that take advantage of the technologies available for improving the accuracy of measurements used for fiscal allocation. The paper considers regulations defined by Norwegian and UK legislative authorities and how they compare to the API MPMS Chapter 20.3 document. Other countries developing their own standards include Mexico and Brazil, and subsea measurement technologies are increasingly being used to resolve complex cross-border fiscal allocation issues, as have previously been presented in this conference. 2. INTRODUCTION

In the petroleum industry, allocation refers to practices of breaking down measures of quantities of extracted hydrocarbons across various contributing sources (API 2013). Allocation aids the attribution of ownerships of hydrocarbons as each

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contributing element to a commingled flow, i.e. where produced fluids from multiple wells and/or producing zones are combined before flowing to a surface facility, which in deep water is typically a floating production, storage and offloading (FPSO) unit. Commingled flow is becoming increasingly common in the growing number of deepwater developments, where subsea production facilities combine flow before joining riser systems that transport hydrocarbons to the surface. Such systems often include tiebacks to other oil or gas fields operated by different company partnerships. Allocation of produced hydrocarbons has major fiscal consequences for a variety of involved parties. It directly impacts the revenue and tax liabilities of oil and gas operators and is the subject of increasingly stringent guidelines and regulations from industry bodies and government authorities. Manufacturers of equipment and suppliers of services to the oil and gas industry are challenged to meet the requirements of these guidelines and regulations, and have developed new technologies and processes to enable more accurate measurement of hydrocarbon production on a well-by-well basis. Multiphase flowmeters (MPFMs) installed at the subsea wellhead are used to measure the flow rates of oil, water and gas, and hence compute allocation. The meters require knowledge of physical parameters of the fluids - such as density - to provide accurate measurements. Fluid characteristics are usually tested during drilling of with wireline deployed instruments shortly after drilling in the exploration phase. However, it is known that produced fluids change over time, and computation of measurements based on samples obtained during exploration may not be representative of reservoir fluids flowing after several years of production. There is a tendency in the subsea measurement market for solutions the authors believe is to be inaccurate to overcome some challenges, such as use of densitometers to gather fluid properties in a producing well. Associated required assumptions such as that the pipe is clean and that the fluids are pure are likely to be inaccurate in upstream oil and gas production. The authors of this paper believe that automatic updates based on an assumed salinity and non-representative in-situ measurements do not provide accurate and reliable information that can eliminate the need for physical sampling of produced fluids. 3. API MPMS 20.3

API published its Manual of Petroleum Measurement Standards (MPMS) Chapter 20.3 in January 2013, superseding API Recommended Practice 86-2005, which is withdrawn. The new standard addresses multiphase flow measurement in the production environment, upstream of the custody transfer (single-phase) measurement point, where allocation is applied. The document addresses operational requirements or constraints in multiphase measurement systems, including expectations for flow meter acceptance, calibration criteria, flow-loop and in-situ verifications. It specifically describes representative sampling as the ultimate way of setting up any MPFM. Furthermore, API MPMS Chapter 20.3 points to the importance of representative sampling as essential to reduce uncertainty in the overall measurement.


Copyright Onesubsea

The API MMPS 20.3 also points to the challenges of capturing representative subsea samples, and suggests that multiple subsequent samples should be taken, allowing each sample to completely separate before the WLR is measured. For some crude oils, this will require the use of a de-emulsifier. It also proposes that the sampling point should be close to the MPFM. An acceptable sample should contain all of the fluid constituents and the time frame for the samples shall be selected such that the samples are representative for the liquid constituents passing through the MPFM during the same time frame. 4. NORWEGIAN REGULATIONS

The Norwegian Petroleum Directorate (NPD) recently issued new requirements in its “Måleforskriften”, which addresses metering for production allocation for fiscal regulations. This also states that subsea inline MPFMs are typically set-up using samples obtained during drilling. It acknowledges that the composition of produced hydrocarbons changes over time and these changes are likely to be significant over the life of a deep sea development. Obtaining representative samples from the production system at line conditions enables maintenance of the inputs of multiphase and wet gas flow meters, leading to more accurate allocation.

5. UK REGULATIONS

The UK Department of Energy and Climate Change (DECC) Licensing, Exploration and Development Guidance Notes for Petroleum Measurement Issue 8 for systems operating under the Petroleum (Production) Regulations was published in July 2012 (DECC 2012). It states that fiscal multiphase measurement may be appropriate in production allocation applications where hydrocarbons from more than one field are commingled in a shared production facility, and where cost benefit considerations indicate that single-phase measurement of each field cannot be not economically justified. The guidance notes state that the operator should provide the MPFM vendor with details of the anticipated initial values of flow rates, pressures, temperatures and composition as well as their expected profiles throughout life of the field. It notes that all MPFMs depend on knowledge of fluid characteristics for their correct operation. When the fluid properties change, systematic bias in the output of the MPFM may be expected unless the relevant parameters in the meter software are updated to reflect these changes. The DECC guidelines note that obtaining a representative sample from a multiphase stream is challenging, particularly in subsea MPFM applications, and there are currently no standards which provide guidance in this area. However, subsea sampling is actually already a proven technology and delivered technology. With proper planning of subsea completion systems is cost-effective to implement. We believe, with increasing industry acceptance of the technology, authorities such as DECC are likely to include sampling as a requirement in future revisions of their guidelines. 6. THE NEED FOR SUBSEA SAMPLING


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By definition the fluid coming from the reservoir is always in an equilibrium state between the liquid and the gas phase and with changes is pressure and temperature there is a migration in or out of each phase. Although the overall composition may not change, the composition of each individual phase may vary considerably. For example, H2S could be migrating from the gas phase to the oil and water phases, or the gas composition might be richer in light components dependent on pressure. In addition, the overall composition of produced fluids is likely to change considerably over the life of a field. It is unlikely that, without representative sampling, MPFM settings will accurately represent the particular conditions of the wellhead. Guidelines and regulations increasingly require subsea sampling in recognition of the impact of changing fluid composition on MPFM accuracy and hence allocation accuracy prior to comingling. It is necessary to sample production from each well at line conditions of pressure, volume and temperature (PVT) in order to properly convert the measurements to the point-of-sale conditions at the surface for fiscal allocation. Representative sampling enables accurate calibration of instruments and reduced uncertainty in the measurements. Remote monitoring of measurements from the subsea instruments can then better identify and take action if issues arise as early as possible to eliminate large scale allocation imbalances. Fluid parameters required for accurate meter measurements include viscosity, densities, water properties, and attenuation (electromagnetic or gamma ray). There is no multiphase flow meter on the market capable of accurately measuring fluid properties that is independent of knowledge of these parameters. For example, no system is capable of measuring the volume of gas dissolved inside the oil, which can be extremely critical in wet gas conditions. Current MPFM systems to measure fractions of oil, water and gas are based on microwave and/or nuclear technologies, both of which are affected by changes in the full composition. While some systems estimate the salt content, this is not the only factor impacting compositional variations that impact the accuracy of measurements.

Sampling is a key technology for reliable measurements and there is a clear trend for the sampling use in the subsea business and specifically in wet gas conditions, as highlighted by Pinguet et al (2012). The need for sampling should be considered during the field subsea pipeline architecture design. Benefits include not only more accurate allocation, but also verification of the compatibility of multiple fluids and, most importantly, for flow assurance purposes. 7. CONCLUSIONS

Industry organizations such as the API and North Sea regulators such as the NPD and DECC agree that, for accurate allocation, MPFM measurements require representative samples of the produced fluids. As oil and gas developments move into ever deeper waters, subsea productions systems will become increasingly common, and will involve comingling of fluids from multiple wells, reservoir zones and/or fields. Subsea sampling at the wellhead is a proven technology, and regulations are moving towards making this a requirement for fiscal allocation purposes. The authors question whether oil and gas industry are moving in an


Copyright Onesubsea

appropriate direction, or even conforming to the latest regulatory requirements for mentioned type of measurements. REFERENCES American Petroleum Institute (API) 2013, Manual of Petroleum Measurement Standards Chapter 20.3 Measurement of Multiphase Flow. Department of Energy and Climate Change (DECC) 2012, Licensing, Exploration and Development Guidance Notes for Petroleum Measurement Issue 8 for systems operating under the Petroleum (Production) Regulations. Norwegian Petroleum Directorate (NPD) 2001 (last translated 2012), Regulations relating to measurement of petroleum for fiscal purposes and for calculation of CO2 tax (the measurement regulations). Pinguet, B., Vethe, E., Smith, M., Smith, G., Sbordone, A. and Nighswander, J., (2012) Reducing Uncertainty from PVT by Representative Sampling Subsea, OTC OTV-23340-PP.

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Allocation in an Uncertain World:

Maximising the Use of Data with UBA on Global Producer III

Neil Corbett, Maersk

Juliet Johnston, Accord

Robert Sibbald, Accord

Phillip Stockton, Accord

Allan Wilson, Accord

1 INTRODUCTION

This paper describes the application of non-linear Uncertainty Based Allocation

(UBA) to allocate oil and gas between the Dumbarton and Lochranza fields produced

across Maersk’s Global Producer III (GPIII) FPSO (Floating Production Storage and

Offloading).

All Lochranza wells’ production is measured continuously using individual subsea

multiphase flow meters (MPFMs). However, Dumbarton wells’ production is only

available from estimates derived from subsea multiphase well tests. Hence the

uncertainty in Lochranza’s production is significantly better than Dumbarton’s.

However, rising water cuts and the increasing presence of gas lift started to produce

relatively high uncertainties in calculated oil and produced gas respectively for all

MPFM measurements.

In this challenging measurement environment, the application of UBA, allows the

maximal use of all available, pertinent data, including field GORs, to allocate oil and

gas products simultaneously in a robust fashion, whilst optimising allocation

uncertainty.

The approach is described using simple theoretical examples and illustrated with real

data supplied by Maersk. The data from the real system covers a five and half year

period (Jan 2007 to July 2012). In the first three years, only Dumbarton wells were

flowing (Jan 2007 to Jan 2010) before Lochranza commenced production.

Section 2 describes the GPIII subsea configuration and topsides process, the

Dumbarton and Lochranza fluids and the previous, historical allocation system. It also

describes some of the issues encountered with the old system which prompted the

investigation to examine alternatives. Section 3 describes the alternative allocation

approaches analysed and examines their performance whilst Section 4 provides a

direct comparison of all the approaches considered in terms of impact on allocated

quantities and associated uncertainties. Finally Section 5 provides some conclusions

on the use of UBA on GPIII.

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2 DESCRIPTION OF SYSTEM

2.1 Process

A schematic of the sub-sea well configuration is presented in Figure 1:

Figure 1 – Dumbarton, Lochranza Sub-Sea Configuration


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Figure 2 shows the subsea and GPIII topsides process and associated topsides

metering:

Figure 2 – GPIII: Simplified Schematic of Subsea and Topsides Process

The GPIII FPSO handles production from both the Dumbarton and Lochranza fields.

Production is metered with a combination of export product meters and subsea

multiphase flow meters (MPFM). While each of the Lochranza wells has a dedicated

MPFM, there is a single MPFM for each of the Dumbarton drill centres (DCC &

DC2) which are used to test the performance of the Dumbarton wells. Lift gas to each

Lochranza well is also individually metered.

As shown in Figure 2, the Dumbarton and Lochranza fluids are commingled upstream

of the 1st stage separators.

Oil separation on the GPIII is achieved using two-stage separation with inlet and 2nd

stage heating. Gas from separation is sent to the Low Pressure (LP) and High Pressure

(HP) compression trains with produced water passed to the produced water handling

package.

Currently, the plant is recycling large quantities of NGL from the compression trains

to the separators. This has presented difficulties with the modelling of the process in

simulation packages and is discussed further in see Section 2.3.

F

1st Stage Sep

1st Stage Sep

MV-0401

LP Sep

DCC Manifold

P6 P7 P4

P1 P2

DC2 Manifold

P8 P9 P11

P3

Lochranza

Metering Skid

P14

P13

Riser Base

LP Compressor

F

F

NGLUSM

FUSM

USM

FUSM

Export Compressor

MV4502

Gas Lift

F

P12 P15

Oil to

Storage

LP Flare

HP FlareFuel

Import

Gas

Export


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The oil is stabilised and offloaded by tanker and shipped to market. The export gas is

transported via the MacCulloch FPSO tie-in, onto Piper B and into the Frigg system at

St Fergus.

All oil and gas product streams (including fuel and flare) are measured.

2.2 Historical Allocation Scheme

Historically, once Lochranza commenced flowing the Dumbarton field was allocated

By-Difference. For example the Dumbarton’s allocated oil was determined by

subtracting the totalised Lochranza MPFM dry oil flow, after allowing for shrinkage,

from the commingled oil export meter.

Similarly, the total produced gas was calculated by summing fuel, flare, export (and

netting import) measured flows and subtracting the Lochranza MPFM gas flow, after

allowing for lift and process effects, to obtain Dumbarton allocated gas.

The calculations are illustrated using a simple example in Figure 3:

Figure 3 – Simplified Example: Historical Dumbarton By-Difference Allocation

The figures can nominally be considered to be in tonnes but they are presented merely

for illustrative purposes and hence no units are shown.

Lochranza is allocated an oil product quantity equal to its MPFM measured dry oil

(1,000 units) – processing effects or shrinkage have been ignored to render the

calculations simpler. Dumbarton is allocated the difference between the measured

export oil and the allocated Lochranza oil (1,800 – 1,000 = 800). Gas product is

allocated in a similar fashion.

This simple example is utilised later to illustrate alternative allocation methodologies.

2.3 Fluid Compositions

An important feature of both the Dumbarton and Lochranza reservoirs is that their

pressure is maintained above the bubble point. This means that the hydrocarbons in

the reservoir rock will be in a single phase and hence when produced up the well bore

the composition of each field’s hydrocarbon fluids entering the GPIII process should

be essentially constant.

This is illustrated in Figure 4 below which plots and compares the compositions of

samples taken of the various Dumbarton wells (obtained at various times).

Dumbarton Sum of Gas Products 270 120 Allocated Dumbarton

150 Allocated Lochranza

Lochranza MPFM Gas 150 800 Allocated Dumbarton

Dry Oil 1,000 Dry Oil Product 1,800 1,000 Allocated Lochranza

Allocated GOR 0.15 Dumbarton

0.15 Lochranza

GPIII Process


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Figure 4 – GPIII: Dumbarton Well Sample Analyses

Samples obtained:

Well 15/20a-14 (red) Jan 2004

Well 15/20a-4 (green) Aug 1987

Well 15/20a-D5 (blue) Sep 2006

Well 15/20-1 (purple) Mar 1975.

And similarly for the Lochranza fluids:

Figure 5 – GPIII: Lochranza Well Sample Analyses


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Samples obtained:

Well 15/20a-13 (red) May 2003

Well 15/20a-13 (green) May 2003

Well 15/20a-13 (blue) May 2003

Well 15/20A-L1A (purple) Sep 2010.

This also means that the Gas Oil Ratio (GOR) of the fields’ allocated fluids (ratio of

produced gas to produced oil) should remain relatively stable, though some variation

is to be expected due to commingling effects and variations in operating conditions in

the topsides process. In addition, because of their stability, the allocated GORs should

provide a metric with which to monitor the veracity of the allocation system.

A plot of Dumbarton’s allocated GOR for the three year period from January 2007 to

the beginning of January 2010, when only Dumbarton was produced, is presented in

Figure 6:

Figure 6 – GPIII: Dumbarton Allocated GOR

Dumbarton Production Only

The GOR presented is mass based and is calculated as the sum of total measured

produced gas mass (fuel, flare, injection and export) divided by the measured export

oil mass. Though there are some outliers, the data indicates a reasonably stable GOR.

The average, standard deviation and range of values associated with the data in the

above plot are presented in Table 1.


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Table 1 – Allocated Dumbarton GOR Statistics (Jan 2007 to Jan 2010)

All Data Outliers

Removed Average 0.121 0.118 Maximum value 0.545 0.246 Minimum value 0.030 0.030 Standard Deviation (abs) 0.044 0.033 Uncertainty (rel %) ±72% ±57%

Based on all the data, Dumbarton’s GOR is estimated to be 0.121 tonne/tonne with a

standard deviation of 0.044 tonne/tonne which is equivalent to an estimated

uncertainty of ±72% (twice the standard deviation expressed relative to the average).

This uncertainty or variability in the GOR appears to be due to a number of factors:

Measurement uncertainty in the oil and gas product meters

Variation in the wellstream composition (if any)

Variation in the GPIII process operating conditions (temperatures and

pressures)

Process instabilities due to high NGL recycle

Other unknown causes.

These factors are considered in turn below.

Measurement Uncertainty

The uncertainties of the gas and oil product measurements are presented in Table 2:

Table 2 – Oil and Gas Product Meter Uncertainties

Meter Relative

Uncertainty (±%)

Oil Export 0.5% Gas Export 1% Fuel Gas 2% HP Flare 5% LP Flare 5% Gas Injection 2% Gas Import 1%

These figures are typical nominal values appropriate for the type of meter installed.

Based on these figures the uncertainty in the Dumbarton GOR due to meter

uncertainty alone can be calculated and is predicted to be between ±1% and ±5%,

depending on the relative flow rates of oil and gas. (These uncertainties in the GOR

have been calculated using the approached described in the GUM [1], termed Taylor

Series Method (TSM), which is used to model the propagation of uncertainties. The

use of the term “analytical” with reference to uncertainty calculations denotes this

TSM method. This is to distinguish that approach from the Monte Carlo Method

(MCM), which is described in a Supplement to the GUM [2]).


22nd

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October 2013

8

It is possible that the quoted meter uncertainties are optimistic but it appears unlikely

that the variability in GOR is due to measurement uncertainty alone.

Compositional and Processing Uncertainty

Based on a single stage stock tank flash of the Dumbarton field composition the GOR

is predicted to be 0.113 tonne/tonne, which is in good agreement with the average

allocated GOR.

However, when simulated in the full multi-stage topsides process, the GOR is

predicted to be around 0.075 tonne/tonne, significantly below the average observed

figure. The reason for this is considered further in the section on process instability

below.

In order to estimate the impact of wellstream compositional measurement uncertainty

and variability in process operating conditions, the steady state topsides process

model was used as part of a Monte Carlo simulation. In this simulation, the feed

compositions and process operating conditions were randomly varied within known

process limits, the process model solved and the variation in the resultant modelled

GOR calculated. It was found that this produced a variation consistent with an

uncertainty of approximately ±10%.

Process Instabilities

The observed variation in GOR is too great to be accounted for by compositional and

flow measurement uncertainties or variations in steady state process operating

conditions alone.

As mentioned above the when simulated in the full multi-stage topsides process the

GOR is predicted to be significantly below the average observed figure.

A problem with the full process simulation model is that in order to reach steady state

thermodynamic equilibrium in all the vessels, the NGL recycles have to reach

unfeasibly high flow rates. This means that steady state equilibrium is not being

established in the actual process. Failure to establish equilibrium would most likely

result in carryover of liquid in the gas through the process and hence would probably

tend to result in a higher GOR than predicted by the steady state model. This possibly

also accounts for a good deal of the variation observed in the measured GOR.

Dumbarton GOR and Uncertainty

It appears that a significant proportion of the observed uncertainty is due to process

instability and other unknown factors (which can effectively be lumped together).

However, despite these issues the data appears to confirm the hypothesis that the

GOR is essentially stable in accordance with an ostensibly constant Dumbarton

wellstream composition resulting from the fact that the reservoir is above its bubble

point and the hydrocarbons being single phase therein.


22nd

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October 2013

9

The observed variation in the GOR is due to a number of factors discussed above, but

based on the historical data, the GOR can be stated to be a nominal value albeit with

an expected variability or uncertainty determined from the standard deviation of the

observed data. Analysis of that data however, does reveal a number of points that

appear to be outliers and these are apparent in Figure 6.

Outliers can be identified statistically using the Grubbs’ Test1 [4]. This was applied to

the Dumbarton GORs presented in Figure 6; 14 out of 979 data points were identified

as outliers and removed from the data used to calculate the average GOR and

uncertainty. The second column of Table 1, presents the statistics for the data set but

with outliers removed resulting in an average GOR of 0.118 tonne/tonne with an

estimated uncertainty of ±57%.

2.4 Lochranza and Dumbarton Allocation Data

When Lochranza wells (P13 and P14) came on stream in January 2011 Dumbarton

was then allocated by-difference. As might be anticipated there was an increase in the

uncertainty of Dumbarton’s allocated gas and oil. The relative uncertainty will

increase as Dumbarton production reduces relative to Lochranza. This is typical of a

by-difference allocation scheme.

Oil Allocation

The oil flows2 for Dumbarton and Locranza for the full 5½ year period analysed are

presented in Figure 7:

1 The Grubbs' test is a statistical test used to detect outliers in a univariate data set

assumed to come from a normally distributed population. 2 Actual data has been filtered to smooth the data points for reasons of clarity.


22nd

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October 2013

10

Figure 7 – Lochranza and Dumbarton Allocated Oil Mass

As can be observed, Dumbarton’s oil production is generally declining, though when

Lochranza starts up it is the dominant flow so allocation by difference appears a

reasonable approach. However, as more Lochranza wells came on stream and its

production increased it might be anticipated that Dumbarton’s allocation By -

Difference would become problematic in terms of allocation uncertainty. This can be

observed at the right hand edge of the chart when a third Lochranza well (P15) was

started up and Dumbarton became the minor flow.

The problems were exacerbated by the rising water cut of the Lochranza wells which

had increased to over 80% for the first two wells (P13 and P14) by the end of the

study period. Increased water cuts in an MPFM result in increased relative uncertainty

in the dry oil measurement.

The uncertainty in both fields’ allocated oil mass can be calculated using the relative

field flow rates (Figure 7), uncertainties in the oil export meter (from Table 2) and

Lochranza multiphase flow meter measurements. The MPFM uncertainties are

reproduced in Table 3:

Table 3 – Lochranza Well MPFM and Lift Gas Meter Uncertainties

Meter Uncertainty

type Uncertainty

(±%) MPFM Gas Flow relative 3% MPFM Liquid Flow relative 3% MPFM Water Liquid Ratio (WLR) absolute 3% Lift Gas relative 3%

The MPFM uncertainties are based on a paper delivered at the 2010 North Sea Flow

Measurement Workshop [3] and are in accordance with a GVF below 90% and

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Jan/07 Jul/07 Jan/08 Jul/08 Jan/09 Jul/09 Jan/10 Jul/10 Jan/11 Jul/11 Jan/12

ton

ne

s/d

ay

Dumbarton Oil Lochranza Oil


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October 2013

11

operating pressure above 20 barg. For an MPFM the dry oil flow uncertainty is

function of the measured liquid and WLR and their associated uncertainties. The

relative uncertainty in the oil flow is given by:

WLR

eWLR WLRLiq

MOil

1

122*

(1)

Inspection of the above equation reveals that the relative uncertainty in the oil

becomes very large as the WLR approaches 1.

Using the analytical TSM method, the relative uncertainty in the allocated oil for both

fields is presented in Figure 8:

Figure 8 – Lochranza and Dumbarton Allocated Oil Mass Relative Uncertainty

The period presented includes 6 months of Dumbarton only (Jul 2009 to Jan 2010)

when its oil allocation uncertainty was that of the oil export meter (±0.5%). Once

Lochranza starts up Dumbarton’s allocated oil uncertainty experiences a step increase

to around ±5%. However as anticipated, this rises towards the end of the period

analysed to more typically around ±20%. Some of the allocated oil uncertainties are

considerably greater than these values (indeed some are considerably in excess of

±70% off the chart) at low Dumbarton flows.

Lochranza’s uncertainty is determined from its measured dry oil uncertainty with

some additional uncertainty due to the process shrinkage from MPFM to oil export

conditions. As the water cut of P13 and P15 rises, Lochranza’s allocated uncertainty

steadily rises from around ±5% initially to in excess of ±20%.

0%

10%

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30%

40%

50%

60%

70%

Jul/09 Jan/10 Aug/10 Feb/11 Sep/11 Apr/12

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October 2013

12

Gas Allocation

For the gas allocation similar trends are observed. The produced gas flows3 for

Dumbarton and Locranza for the full 5½ year period analysed are presented in Figure

9:

Figure 9 –Dumbarton and Lochranza Allocated Produced Gas Mass and

Lochranza Lift Gas

The gas production mimics the associated oil production since the GORs of both

fields are stable.

Exacerbating the allocation uncertainty is the inclusion of lift gas in the Locranza

MPFM measured gas rates which has to be netted off. (The uncertainty in each well’s

lift gas measurement increases the well’s calculated produced gas uncertainty). In fact

the lift gas starts to dominate the measured flow as indicated by the dashed red line in

Figure 9.

The analogous gas allocation uncertainties are presented in Figure 10:

3 Actual data has been filtered to smooth the data points for reasons of clarity.

0

100

200

300

400

500

600

700

800

Jan/07 May/08 Sep/09 Feb/11 Jun/12

ton

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ay

Dumb Gas Loc Gas Loc Gas plus Loc Lift


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October 2013

13

Figure 10 – Lochranza and Dumbarton Allocated Gas Mass Relative Uncertainty

Again, the period presented includes 6 months of Dumbarton only when its gas

allocation uncertainty was that of the combined gas product meters (between ±1% and

±5%).

Similar to the oil allocation, once Lochranza starts up Dumbarton’s allocated gas

uncertainty experiences a step increase to between ±10% to ±20%, though the values

can be considerably in excess of this, (in some cases exceeding 200% off the chart) at

low Dumbarton flows.

Lochranza’s allocated gas uncertainty is consistently above ±20% rising to around of

±40% at the end of the period when the lift gas routed through the MPFMs is

dominating the gas flow.

Allocated GOR

As stated above in Section 2.3, the allocated GORs should provide a metric with

which to monitor the performance of the allocation system. Analysis of Dumbarton’s

allocated GOR shows an increase in variability after Lochranza starts up in January

2010, as illustrated in Figure 11:

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%


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εGas Dumb εGas Loch


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October 2013

14

Figure 11 – Dumbarton Allocated GOR (Mass Based)

Again some points after Lochranza start-up are off the chart in excess of a GOR of 1.5

tonne/tonne. Though perhaps not readily apparent, because of the large vertical axis

scale, the spread of GORs below the average rises once Lochranza starts up. A more

analytical approach to assess the variability change is to consider the statistcs. The

statistics for the two periods pre- and post-Lochranza start-up are summarised in

Table 4:

Table 4 – Allocated Dumbarton GOR Statistics

Pre-

Lochranza Post-

Lochranza Average 0.121 0.104 Maximum value 0.545 2.666 Minimum value 0.030 0.000 Standard Deviation (abs) 0.044 0.153 Uncertainty (rel %) ±72% ±294%

Dumbarton’s GOR has become more variable as indicated by the higher standard

deviation. This is to be expected in accordance with the increased uncertainty in its

allocated oil and produced gas.

Similar to Dumbarton, Lochranza should also exhibit a relatively stable GOR as it has

a consistent wellstream composition (see Figure 5) and its reservoir is also above its

bubble point.

Lochranza’s allocated GOR is also presented in Figure 12 and the associated statistics

summarised in Table 5.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Jan-07 Jun-07 Dec-07 Jun-08 Dec-08 Jun-09 Dec-09 Jun-10 Dec-10 Jun-11 Dec-11 Jun-12

GO

R (

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Dumbarton Field

Lochranza start-up


22nd

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October 2013

15

Figure 12 – Lochranza Allocated GOR (Mass Based)

Table 5 – Allocated Lochranza GOR Statistics

All Data Outliers

Removed Average 0.155 0.144 Maximum value 2.965 0.303 Minimum value 0.001 0.001 Standard Deviation (abs) 0.138 0.040 Uncertainty (rel %) ±178% ±56%

Similar to Dumbarton, Lochranza does exhibit an ostensibly stable GOR with some

variation probably due to process instability effects as was observed with Dumbarton.

The Grubbs’ Test was applied to the Lochranza GORs and outliers removed from the

data to calculate the average GOR of 0.144 tonne/tonne with an estimated uncertainty

of ±56%.

2.5 An Uncertain Future

Concerns arose with the then incumbent Dumbarton By-Difference allocation scheme

because the already increasing uncertainties in Dumbarton’s allocated oil and gas and

variability in allocated GOR were only anticipated to deteriorate further because:

Dumbarton’s production was declining

Lochranza’s MPFMs’ dry oil measurement uncertainty was increasing with

increasing water production

Lochranza’s MPFMs’ gas measurement uncertainty was increasing with rising

lift gas rates

0.0

0.1

0.2

0.3

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0.6

Jan-10 Jul-10 Dec-10 Jun-11 Dec-11 Jun-12

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Lochranza Field


22nd

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October 2013

16

A new field, Balloch, was being tied back to GPIII.

The above factors meant that Dumbarton’s fraction of the total production on GPIII

would continue to fall and its already worsening allocation uncertainty would increase

to unacceptable levels.

The rising uncertainty in Dumbarton’s allocated oil and gas has consequences in that

it provides poor data for reservoir modelling purposes. Indeed the increasing

variability of the GOR was leading to credibility problems with the reservoir

engineers, thus undermining the integrity of the allocation system.

An additional possibility, which occurred in a similar system, is the potential for a

field to be shut-in due to its flare consent limit being breached. Due to a high gas

allocation uncertainty (analogous to Dumbarton on GPIII), an over-allocation of

produced gas was experienced by a low GOR field in this system. This resulted in an

increase in its allocated flare gas which precipitated the very real threat of shut-in of

production as it approached its flare consent limit.

The need to improve Dumbarton’s allocation uncertainty resulted in alternative

allocation schemes being considered and these are discussed in the next section.

3 ALTERNATIVE ALLOCATION APPROACHES

3.1 Pro Rata

The most obvious alternative to allocating Dumbarton By-Difference is to allocate

Pro Rata by incorporating Dumbarton’s estimated production from well test

information into the allocation scheme.

For example the oil can be allocated in proportion to:

the sum of Lochranza’s MPFM dry oil measured rates (after allowing for

process shrinkage)

the sum of Dumbarton wells’ most recent tested dry oil rates (after allowing

for hours in production and process shrinkage) – this is termed Dumbarton’s

estimated oil rate.

This is illustrated numerically again using the simple example:

Figure 13 – Simplified Example: Pro Rata Allocation

Dumbarton Estimate 154 Sum of Gas Products 270 137 Allocated Dumbarton

Dry Oil 1,400 133 Allocated Lochranza

Lochranza MPFM Gas 150 1,050 Allocated Dumbarton

Dry Oil 1,000 Dry Oil Product 1,800 750 Allocated Lochranza

Estimated GOR Dumbarton 0.11 Allocated GOR 0.13 Dumbarton

Lochranza 0.14 0.18 Lochranza

GPIII Process


22nd

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October 2013

17

The total oil product quantity is allocated in proportion to Lochranza’s MPFM

measured dry oil (1,000) and Dumbarton’s estimated production based on well tests

(1,400). Hence Lochranza is allocated 1,800 * 1,000 / (1,000 + 1,400) = 750 and

Dumbarton 1,800 * 1,400 / (1,000 + 1,400) = 1,050. Gas is allocated on a similar

basis.

This approach has been applied to the real data and the analytical uncertainty in the

allocated oil calculated. For this analysis, the uncertainty in the estimated Dumbarton

oil production based on well tests is required. This can be deduced from a comparison

of Dumbarton’s estimated oil and the exported product oil for the period when

Dumbarton only was on stream (Jan 2007 to Jan 2010). The percentage difference

between Dumbarton’s estimated oil rate and the actual measured oil production is

plotted in Figure 14:

Figure 14 – Relative Difference between Dumbarton Estimated vs Measured Oil

The calculated uncertainty in Dumbarton’s estimated oil is relatively poor at around

±67%. Using the analytical TSM approach the resultant uncertainty in the oil

allocated to Dumbarton and Lochranza has been calculated and is presented in Figure

15:

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Jan-07 Jun-07 Dec-07 Jun-08 Dec-08 Jun-09 Dec-09

Percentage DifferenceDumbarton Estimated vs Measured Oil


22nd

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October 2013

18

Figure 15 – Lochranza and Dumbarton Pro Rata Allocated Oil Mass Relative

Uncertainty

Comparison with the Dumbarton By-Difference allocation uncertainties presented in

Figure 8 illustrates that the high uncertainty in the Dumbarton estimated oil

significantly increases, not only the Lochranza allocated oil uncertainty, but also

Dumbarton’s.

Based on these oil uncertainty figures alone Pro Rata does not appear a viable option.

3.2 Using More Information

One of the key features of this system is the identification of the fact that the two

fields’ wellstream compositions are essentially constant resulting in a stable GOR.

The GOR may vary from day to day depending on operating conditions and any

process dynamical instabilities but it should not vary as widely as is observed in the

allocated data. Indeed the variation in the allocated GOR has been used as a metric to

judge the quality of, and consequently question, the allocation results.

The GORs connect the oil and gas allocated to both fields and because they can be

estimated to within a tolerance or nominal uncertainty they can be incorporated as

inputs into the allocation system. The method by which this has been implemented is

described in the next section.

3.3 Uncertainty Based Allocation

Uncertainty based allocation has been previously described in several papers (for

example [5], [6] and [7]) and actually applied in one North Sea oil allocation system

[7]. Its superiority in terms of allocation uncertainty over By-Difference and Pro Rata

approaches has also been discussed [8].

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22nd

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19

The additional effort and complexity associated with UBA is worthwhile in systems

where measurements of the estimated production from two or more fields differ

significantly in uncertainty rendering neither By-Difference nor Pro Rata allocation

suitable over the full range of production. This is precisely the situation with the

GPIII allocation. The additional feature of the inclusion of the fields’ GORs as an

input to the allocation system is also readily afford by UBA as it is based on data

reconciliation techniques (described in [9]). Data reconciliation, as the name implies,

involves the reconciliation of all relevant data to satisfy constraints such as mass

balances, etc. It does this in a statistically optimal fashion by incorporating the

uncertainty in the data.

The application of uncertainty based allocation to the GPIII system involves

simultaneously reconciling estimated oil production, gas production and notional

GOR associated with each field with the product oil and gas measured products. The

inputs quantities to be reconciled are:

Lochranza wells’ MPFM dry oil (adjusted for processing effects)

Sum of Dumbarton’s wells’ estimated oil production from well tests (adjusted

for processing effects)

Lochranza wells’ MPFM gas (lift gas subtracted and adjusted for processing

effects)

Dumbarton’s notional GOR

Lochranza’s notional GOR.

The UBA procedure takes these input quantities and adjusts them until there is a mass

balance with measured product oil and gas. The adjusted or reconciled values are the

allocated values. The adjustments are performed in such a way that the differences

between the actual measured values and the reconciled (allocated) values is

minimised (to be precise, the weighted sum of squares of the differences is

minimised).

The technique takes into account the various uncertainties of each input. By

incorporation of the input quantity accuracies the technique effectively gives more

“weight” to those inputs which are expected to be more accurate. So for example, the

approach still utilises the Dumbarton estimated oil based on well tests, which

degraded the results of the Pro Rata allocation, but its influence on the allocation

results is reduced because its uncertainty is relatively high and it is weighted

accordingly. The Dumbarton estimated oil figure is worthy of inclusion, because at

low Dumbarton flows its uncertainty may be better than that from the By-Difference

approach.

It should be noted that the UBA approach described above is near statistically

optimal. It would be statistically optimal if it also adjusted the product measurements.

However, as any allocated quantities need to sum to the product measurements these

have been excluded from any adjustments. This is appropriate because the product

measurements have much lower uncertainties than the input quantities and therefore

would experience little adjustment in the reconciliation procedure in any case.

The equations used to frame the allocation are:


22nd

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October 2013

20

Minimise (ψ) where:

(

)

(

)

(

)

(

)

(

)

(

)

(

)

(

)

(

)

(2)

Subject to the mass balance constraints on the oil phase and gas phase which are:

Dumb

PPPOil

ADOILM

ADOILMADOILMADOILMDOILM

1514130

(3)

DumbDumb

PPPGas

AGORADOILM

APGASMAPGASMAPGASMTPGASM

*

01 151413

(4)

LochLoch

PPPGas

AGORADOILM

APGASMAPGASMAPGASM

*

02 151413

(5)

There are two gas constraints (equations (4) and (5)): the first ensures that the

allocated Lochranza gas and Dumbarton allocated oil multiplied by GOR equals the

total measured gas; the second ensures that the allocated Lochranza gas equals the

Lochranza allocated oil multiplied by Lochranza GOR.

The fact that the two gas constraint equations include two of the quantities to be

adjusted multiplied together (allocated oil and GOR) means that the problem is non-

linear. The solution to these non-linear equations is more complex than for linear

UBA (as described in [5], [6] and [7]) and an iterative technique is required.

For systems with two fields, the equations can be re-arranged as a series of

simultaneous equations. However, for systems with more than two fields, the

equations are more complex, and a matrix-solution method is recommended. The


22nd

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October 2013

21

derivation and solution method used is described in Section 6. The UBA calculations

have been carried out on Excel spreadsheets using recognised matrix algebra

techniques. A specific matrix add-in for Excel was used to obtain the necessary

capability and numerical precision [10].

Returning to the simple example to illustrate the non-linear UBA calculations:

The input variables have been adjusted to produce the reconciled, allocated values

such that the square of sum of the weighted differences is minimised (red highlighted

value of 1.119), whilst ensuring the allocated oil sums to the measured dry oil product

and likewise the allocated gas sums to the measured gas products and the allocated

GORs are similarly consistent.

For example the Dumbarton estimated oil (1,400) has an uncertainty of ±50% (or

±700 absolute). Its allocated oil is 821, which is 579 less than its estimated value but

when weighted this is equal to (1,400 – 821)/700 = 0.827, and squared = 0.684.

This approach has been applied to the real data and the allocated GOR is plotted in

Figure 16:

Dumbarton Estimate 154 Sum of Gas Products 270 113 Allocated Dumbarton

Dry Oil 1,400 157 Allocated Lochranza

Lochranza MPFM Gas 150 821 Allocated Dumbarton

Dry Oil 1,000 Dry Oil Product 1,800 979 Allocated Lochranza

Estimated GOR Dumbarton 0.11 Allocated GOR 0.14 Dumbarton

Lochranza 0.14 0.16 Lochranza

Inputs Measured Allocated Difference (Weighted Difference)2

Dumbarton Estimated Dry Oil 1,400 821 -579 0.684

Lochranza MPFM Dry Oil 1,000 979 -20.9 0.044

Lochranza MPFM Gas 150 157 6.5 0.047

Dumbarton Estimated GOR 0.11 0.14 0.028 0.264

Lochranza Estimated GOR 0.14 0.16 0.020 0.080

1.119 Sum of squares

50%

50%

GPIII Process

Uncertainty

50%

10%

20%


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October 2013

22

Figure 16 – Lochranza and Dumbarton UBA Allocated GOR

This plot illustrates the reduction in the variability of the GORs, particularly for

Dumbarton. (The GOR stability is considered to be a metric with which to assess the

veracity of the allocation results). It also tends to allocate a slightly higher GOR to

Lochranza compared with Dumbarton which is as expected.

The three allocation methods are compared more directly in Section 4.

4 COMPARISON OF METHODS

4.1 Summary of Allocated Quantities

Table 6 provides a summary of the total allocated quantities generated by all three

methods for the period after Lochranza started up and both fields were flowing:

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Table 6 – Allocated Totals for Period from 2nd

Jan 2010 to 3rd

July 2012

By-

Difference Pro Rata UBA

Dumbarton Total Allocated Oil (tonnes)

1,471,382 1,337,269 -9.1% 1,467,962 -0.2%

Lochranza Total Allocated Oil (tonnes)

987,076 1,121,188 13.6% 990,496 0.3%

Dumbarton Total Allocated Gas (tonnes)

128,863 155,653 20.8%

Lochranza Total Allocated Gas (tonnes)

138,004 111,215 -19.4%

Dumbarton Average Allocated GOR (tonne/tonne)

0.104 0.113

Lochranza Average Allocated GOR (tonne/tonne)

0.155 0.148

The total allocated oil and gas quantities are presented in the first four rows. The

percentage difference in these figures generated by the Pro Rata and UBA methods

compared to the historical Dumbarton by-difference approach are also presented.

The allocated oil results show that the Pro Rata approach has a very significant impact

on the results compared to the historical allocation. UBA however, results in a much

smaller impact on allocated totals compared with the original. This is as expected

since the Lochranza MPFM oil flow uncertainties are much better than those for

Dumbarton estimated from well tests.

A more marked impact is observed in the allocated gas when comparing UBA with

By-Difference. However, the average daily UBA allocated GOR is now more

consistent with the PVT data. (NB. The GOR is calculated from the daily average and

not simply total gas divided by total oil for the period). Pro Rata was not included in

the gas allocation comparisons since the oil allocation was so poor and Pro Rata was

therefore not considered further.

4.2 Allocated Oil Uncertainty

The allocation uncertainties for the three methods are compared for Dumbarton and

Lochranza in Figure 17 and Figure 18 respectively.


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October 2013

24

Figure 17 – Dumbarton Oil Mass Allocation Uncertainties

Figure 18 – Lochranza Oil Mass Allocation Uncertainties

There are only limited data points for the UBA method (indicated by the red dots)

because the uncertainties had to be calculated using a relatively time consuming

Monte Carlo method.

0%

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30%

40%

50%

60%

70%


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By-Diff Pro Rata UBA

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By-Diff Pro Rata UBA


22nd

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October 2013

25

These plots illustrate similar uncertainties obtained using UBA compared with the

historical By-Difference allocation method for both Dumbarton and Lochranza. Both

methods’ uncertainties are evidently much better than the poor allocation results

generated by Pro Rata allocation.

4.3 Allocated Produced Gas Uncertainty

The gas allocation uncertainties for the By-Difference and UBA methods are

compared for Dumbarton and Lochranza in Figure 19 and Figure 20 respectively.

Figure 19 – Dumbarton Produced Gas Mass Allocation Uncertainties

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October 2013

26

Figure 20 – Lochranza Produced Gas Mass Allocation Uncertainties

For the gas significant improvements in allocation uncertainty are observed with UBA

compared to the historical By-Difference approach for both fields.

4.4 Anomalous Allocation Days

With the By-Difference allocation approach, there were a number of allocation days

when Dumbarton was allocated some oil but zero gas, i.e. the measured Lochranza

gas exceeds the total product gas. Application of UBA results in a more realistic

allocation.

For example, this scenario occurred on the last day of the period analysed (2nd

July

2012) and the allocation results using By Difference and UBA are summarised in

Figure 21:

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By-Diff UBA


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27

Figure 21 – Comparison of Allocation Results for By-Difference and UBA for

2nd

July 2012

The blue series shows the By-Difference allocation results with Dumbarton being

allocated zero gas even though it is allocated some oil, i.e. its allocated GOR is zero.

The Dumbarton wells were flowing and hence it might be expected that Dumbarton

would be allocated both gas and oil. By utilising the GOR data, the UBA method

produces a more coherent, credible allocation (red series).

5 CONCLUSIONS

UBA offers a viable alternative allocation approach that:

Utilises all pertinent data in the allocation, including the fields’ ostensibly

stable GORs and Dumbarton estimated well production (based on well tests);

Allocates oil and gas simultaneously in a near statistically optimal fashion;

Ensures Dumbarton’s allocation uncertainty does not rise to unacceptable

levels as its relative production declines, especially with new Lochranza wells

and new fields starting up production;

Produces oil allocation uncertainties comparable with the By-Difference and

much better than Pro Rata approaches;

Produces gas allocation uncertainties significantly better than the By-

Difference approach;

Results in more stable allocated GORs for both fields;

0

500

1,000

1,500

2,000

2,500

Loch Oil Loch Gas Dumb Oil Dumb Gas

Allo

cate

d O

il /

Gas

(to

nn

es)

By Diff UBA


22nd

– 25th

October 2013

28

Avoids anomalous allocation results, i.e. fields allocated oil but no gas;

Produces more consistent, coherent and credible allocation results.

6 MATHEMATICAL ANALYSIS UBA MATRIX SOLUTION

TECHNIQUE

This method is described in [11] and [12] and is based upon the principles of data

reconciliation as described more generally in [9].

It should be noted that a rigorous data reconciliation method would reconcile the

product measurements (DOILM, EXPGM, etc.) as well as the allocated quantities

(ADOILMg, etc). The sum of allocated quantities would not, therefore, exactly equal

the recorded measurements. For allocation, it is generally required that the sum of the

allocated quantities is equal to the recorded measurement. So although the product

(fiscal) measurements are included in the equations below their uncertainties are

assumed to tend to zero, and this ensures the sum of the allocated quantities is equal

to the recorded measurement. This is justifiable because the fiscal product

measurements are generally substantially more accurate than production estimates.

It should also be noted that total produced gas, with its associated uncertainty,

represents the combined fiscal gas export, fuel, flare and injection gas streams less

import gas, and their uncertainties. The total produced gas term can be replaced by the

individual stream quantities and their associated uncertainties in the following

equations. This simply leads to matrices of higher dimension in the equations. The

entries representing fiscal export, fuel and flare gas would all be analogous to those

for total produced gas shown here. Similarly, water could be included in the data

reconciliation, with an additional constraint and inclusion of the necessary metered or

estimated stream masses and uncertainties leading to a further increase in the

dimensions of the matrices involved in the equations.

Theory

The full system of equations to be solved for the GP III system is shown below:

(

)

(

)

(

)

(

)

(

)

(

)

(

)

(

)


22nd

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October 2013

29

(

)

(6)

The mass balance constraints on the oil phase and gas phase are:

Dumb

PPPOil

ADOILM

ADOILMADOILMADOILMDOILM

1514130

(7)

DumbDumb

PPPGas

AGORADOILM

APGASMAPGASMAPGASMTPGASM

*

01 151413

(8)

LochLoch

PPPGas

AGORADOILM

APGASMAPGASMAPGASM

*

02 151413

(9)

The optimum solution to the system is found by minimising the value of Ψ (psi) in

Equation (6), subject to the constraints of Equations (7), (8) and (9).

For systems with two fields, simultaneous equations can be easily written out

explicitly and solved iteratively. However, for systems with more than two fields, the

equations are more complex, and a matrix-solution method is recommended. Such a

solution is described below.

Matrix Solution Method – Inputs

The input data to the matrix solution method are provided in the form of arrays and

vectors. The integer n represents the number of variables to be reconciled.

Y (Input) vector of measured data (dimension n, 1).

X (Calculated) vector of reconciled data (dimension n,1).

V Variance-covariance matrix for Y (dimension n,n). The covariance of each

element to itself is calculated from the square of the absolute uncertainty (U) of

the measurement (Ym) divided by 2, (Um/2)2. The covariance of any element

with any other element is zero because the quantities are independent.

J Jacobian matrix (dimension number of constraint equations n). This contains

the coefficients of the derivatives of the oil and gas constraints Equations (7), (8)

and (9) – see below for derivation.

P Constraint Projection matrix (dimension n,n). This is used to enable the oil and

gas mass balance constraints to be calculated using the Jacobian. It accounts for

non-linear relationships while at the same time removing double-counting from

the constraints. The matrix elements are 1 or 0 on the main diagonal according

to which elements of the Jacobian and which measurements are to be used to


22nd

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October 2013

30

derive the constraints. All off-diagonal elements are 0.

For example, for the 2-field Lochranza and Dumbarton application, the “measured”

data comprised the stream measurements and the theoretical oil and gas production (at

export conditions) for each Field. These were mass values, based on MPFM

measurements for Lochranza Field and on well-tested oil quantities and constant GOR

for Dumbarton Field. All theoretical production quantities were calculated within the

allocation system.

For a higher-order system, such as GP III, the principles are the same but the matrices

are extended to include the additional Field values.

The subsequent matrices are shown with only 2 Fields. Equivalent terms for

additional Fields should be inserted where indicated by “…”.

[

]

[

( )

( )

( )

( )

( ) ]

[ (

)

(

)

(

)

(

)

(

)

(

)

(

)

]

[ ]

The matrix solution is an iterative method, based on the “Jacobian matrix” (J). The

Jacobian terms reflect the non-linear terms in the least-squares-type method used to

determine the minimum value of Ψ in Equation (6). The Jacobian terms represent the


22nd

– 25th

October 2013

31

coefficients of the derivatives of the oil and gas constraints (Equations (7), (8) and (9))

with respect to each reconciled quantity, Xm , e.g., ∂ФOil/∂Xm and . ∂Ф1Gas/∂Xm.

[

]

[

]

The matrix solution is therefore an iterative method, because some of the coefficients

of the non-linear Jacobian terms (AGORDumb, ADOILMDumb, AGORLoch and

ADOILMLoch) are dependent on the previous solution.

For the first iteration only, the Jacobian matrix uses the theoretical estimates of the

non-metered Field GOR and Oil.

Matrix Solution Method

The reconciled measurements X which result in the minimum value of Ψ in the

system of equations described above may be described as follows and are calculated

using the method described in [11] and shown in Equation (10) below:

[

]

00 XYJXfKYX

(10)

Where,

X is the vector containing the reconciled measurements calculated by this iteration. Y is the vector containing the initial measurements, as defined above. K is an intermediate matrix, defined as:

1

TT JVJVJK

(11)


22nd

– 25th

October 2013

32

V is the covariance matrix for Y, as defined above.

JT is the transpose of the Jacobian matrix, J.

f(X0) is the imbalance vector, and is calculated from the product of the Jacobian

matrix (J), the Constraint projection matrix (P) and the current estimated

measurements (X0).

JPXXf )( 0

(12)

J is the Jacobian matrix, P is the Constraint Projection matrix, as defined above.

X0 is the vector containing the reconciled measurements from the previous iteration.

Matrix Solution Method – Initialisation

1. Specify elements of measurements matrix, Y.

2. Calculate elements of variance-covariance matrix, V.

3. Specify initial elements of initial Jacobian matrix, J1, using the theoretical

field quantities.

4. Calculate intermediate matrix K from Equation (11): K=V JT (J V J

T)-1

.

5. Initialise value of reconciled measurements vector, X0 = Y.

6. Calculate new values of reconciled measurements vector, X from Equations

(10) and (12).

Matrix Solution Method – Iteration

7. Update value of reconciled measurements vector, X0 = X from previous

iteration.

8. Update elements of Jacobian matrix, J, using the latest reconciled

measurements.

9. Update intermediate matrix K from Equation (11): K=V JT (J VJ

T)-1

.

10. Calculate new values of reconciled measurements vector, X from Equations 4

and 6.

11. Calculate absolute change in reconciled measurements vector: ABS(X- X0).

12. If the sum of the absolute changes in reconciled measurements has changed by

more than the specified tolerance, repeat steps 7 to 12.


22nd

– 25th

October 2013

33

NOTATION

ADOILM Allocated dry oil mass

AGOR Allocated GOR

APGASM Allocated produced gas

mass

DOILM Measured dry product oil

mass

e Uncertainty (absolute)

EXPGM Export gas mass

f(X0) Imbalance vector

J Jacobian matrix

K Intermediate matrix

NOTGOR Notional GOR

P Constraint projection

matrix

THWGM Theoretical well gas mass

THWOM Theoretical well oil mass

TPGASM Total produced gas mass

U Absolute uncertainty

V Variance covariance matrix

WLR Water Liquid Ratio

X Reconciled or allocated

data vector

X0 Reconciled or allocated

data from previous

iteration vector

Y Input data vector

Greek

Uncertainty (relative)

ψ Objective function

𝜙 Constraint

Subscripts

Dumb Dumbarton

liq mass of liquid

Loch Lochranza

moil mass of oil

P13, etc Well P13, etc

7 REFERENCES

[1] Guide to the Expression of Uncertainty in Measurement, International

Organisation for Standardisation, ISO/IEC Guide 98:1995.

[2] Joint Committee for Guides in Metrology (JCGM), “Evaluation of

Measurement Data – Supplement 1 to the ‘Guide to the Expression of

Uncertainty in Measurement’ – Propagation of Distributions Using and

Monte Carlo Method” JCGM 101: 2008, France 2008.

[3] Proceedings of the 28th

International North Sea Flow Measurement

Workshop, 26-29 October, 2010, Well Testing - An Evaluation of

Test Separators and Multiphase Flow Meters, Amy Ross, and Gordon

Stobie.

[4] Grubbs, Frank (February 1969), Procedures for Detecting Outlying

Observations in Samples, Technometrics, 11(1), pp. 1-21

[5] Use of Subsea Wet Gas Flowmeters in Allocation Measurement

Systems, API RP 85, First Edition, August 2003.

[6] Determination of Measurement Uncertainty for the Purpose of Wet Gas

Hydrocarbon Allocation, R. A. Webb, W. Letton, M. Basil, North Sea

Flow Measurement Workshop 2002.

[7] Experiences in the Use of Uncertainty Based Allocation in a North Sea

Offshore Oil Allocation System, P. Stockton and A. Spence,


22nd

– 25th

October 2013

34

Production and Upstream Flow Measurement Workshop,12-14

February 2008.

[8] Allocation Uncertainty: Tips, Tricks and Pitfalls, Phil Stockton and

Allan Wilson, Proceedings of the 30th

International North Sea Flow

Measurement Workshop, 23-26 October, 2012.

[9] Data Reconciliation and Gross Error Detection, An Intelligent Use of

Process Data, Shankar Narasimhan and Cornelius Jordache, published

in 2000 by Gulf Publishing Company, Houston Texas, ISBN 0-88415-

255-3.

[10] Matrix and Linear Algebra Package for Excel, Matrix.xla - Ver. 2.3.2 -

March 2007, http://digilander.libero.it/foxes.

[11] "Wring more information out of Plant Data", Robert Kneile, Bailey

Controls Co, Chemical Engineering March 1995, pps 110 - 116. Box

"deriving the SDF" p115, equations (9) and (10).

[12] The Estimation of Parameters in Nonlinear, Implicit Models, H. I. Britt

and R. H. Luecke, Technometrics Vol 15, No.2.

http://digilander.libero.it/foxes

fltough

Stamp

Challenges on using subsea wet gas meters for gas and condensate allocation between the wells at

Sleipner Vest Field

Tarun Grover, Statoil; Knut Kr Meisingset, Statoil; Oystein Tesaker, Statoil; Siv Kari Lien, Statoil;

Solfrid Loken Tonstad*;Terje Kristian Wilberg, Statoil

Abstract

Sleipner Vest field constitutes two different reservoirs, Sleipner Beta and Alfa Nord. Sleipner

Beta field is drained by platform wells while Alfa Nord wells are subsea tie-back to the processing

platform. The field is operated by Statoil. This paper highlights the problems related to using subsea

wet gas meters for gas and condensate allocation between the wells at Sleipner Vest Field.

The objectives of this study were to identify the sources of wrong gas/condensate allocation

on Sleipner Vest field and to suggest a robust solution to reallocate gas/condensate correctly.

A thorough investigation was done on production data from Sleipner Beta and Alfa Nord

fields. It was identified that inability and hence failure to update PVT in wet-gas meters were the

source of wrong gas/condensate allocation. To correctly reallocate gas/condensate, gas-condensate

ratio (GCR) was reconstructed by using the wet gas meter raw hydrocarbon mass rates and then

using post processing of reservoir simulation results and the compositional data from the processing

platform. The reconstructed GCR was then applied to all wells in Sleipner Vest field to obtain

correct gas and condensate rates from the wells.

The GCR reconstruction algorithm proved to be robust and was easily incorporated into

production database. The reallocated gas and condensate volumes were then used to update the field

specific simulation models.

This paper quantifies the importance of regular PVT updates in Wet-gas meters when they are

used in allocation calculation routines. Further we show that GCR reconstruction algorithm can be

used as an alternative when PVT update is not possible in wet-gas meters in a gas-condensate field.

1. Introduction

Sleipner Vest field is located offshore Norway. The field produces from two different reservoirs,

Sleipner Beta and Alfa Nord. The fluid in both reservoirs is gas-condensate, but with different PVT

characteristics such as GCR. The production strategy in Sleipner Vest field is pressure depletion.

Wells drilled in Sleipner Beta are platform wells, with wellheads located on Sleipner B platform.

Sleipner B platform is the Normally Non Manned Platform (NNM) and all the wells can be remotely

routed to the test-separator to measure the gas, water and condensate streams from each well

periodically. Alfa Nord reservoir is produced as a subsea tie back to Sleipner T (see Figure 1)

without any test-separator options.

*Formerly with Statoil ASA

fltough

Stamp

Figure 1 – Production routes and measurements of various streams in Sleipner Vest Field.

1.1. Allocation methodology

Sleipner T (SLT) platform is the processing platform where CO2 is stripped off from the feed

gas from SLB platform and Alfa Nord subsea template. Also, fuel and flare is taken out from the

SLT platform and is measured. The gas and unstable condensate exported from SLT platform are

measured through fiscal metering and a monthly average compositional analysis is done on export

gas and export condensate.

The gas and condensate volumes are then back allocated to the wells on SLB and Alfa Nord.

The allocation factor for whole Sleipner Vest field is defined as a ratio of measured volume to the

sum of theoretical volumes from the wells. AF should be close to 1 for a system where the allocation

system is correctly set-up combined with good the measurements from test-separator and Wet-gas

meters.

wells

ltheoretica

measured

V

VAF

For wells producing towards the SLB platform, the gas and condensate rates are measured by

routing the wells to the test-separator. The measured gas and condensate rates from the test-separator

are then fed into a PVT algorithm inside the production database. The algorithm takes into account

the process description on SLT platform in addition to the gas and condensate measured from test-

Compressor Inlet separator

Pre - compressor

Gas treatment

Export compressor (155 bar )

Alfa Nord E - template

SLB platform

SLT platform

~54 bar

Test Sep P,T

Sampling

Metering

Metering Sampling

Q_cond

Q_gas

Q_cond

Q_gas

Fuel Flare

MPM

4 MPMs

CO2

Allocation Factor (AF) = Meas volume/ Theor volume

AF_ total = AF_SLB wells = AF_E wells

=> Same AF for all wells!

Q_water

separator and then performs a mathematical iteration which results in a process corrected gas-

condensate ratio (GCR).

For Alfa Nord wells, since there is no access to test-separator, the gas and condensate rates

measured directly by the subsea wet gas meters are used as theoretical rates. This means that PVT

algorithm built inside the wet-gas meters (based on initial fluid composition of Alfa Nord wells) is

supposed to calculate correct process corrected GCR.

2. Allocation problems

It was clear that the theoretical gas and condensate rates calculated in Alfa Nord wet-gas

meters might be source of error if the fluid compositions inside the wet-gas meters was not updated

regularly. A meeting was organized by the wet-gas meter vendor and it was found out that the fluid

composition in the wet-gas meters was not updated at all after initial installation. The reason stated

was the inability to have communication access with the subsea wet-gas meters.

In addition to subsea wet-gas meters, during the start-up of the Alfa Nord field, a topside wet-

gas meter was installed on the SLT platform. However, after a brief period of operation, the top side

wet-gas meter had malfunction in V-cone installed in the meter. Since then, the top-side meter has

not been in operation.

When looking at GCR output for Alfa Nord wells, it was found out that the development of GCR

with time, which has been used in allocation, has been steady with time (Figure 2). The GCR for a

typical gas-condensate reservoir does not follow this trend because the produced gas will become

leaner due to dropping of heavy-ends of fluid in the reservoir during pressure depletion. Therefore,

according to the allocation system, the allocated gas volumes were underestimated and allocated

condensate volumes were overestimated compared to the actual values.

Figure 2 – GCR output from WGM in the one of the Alfa Nord wells (E-1H)

2500

2600

2700

2800

2900

3000

3100

3200

3300

3400

Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12

GC

R (

Sm

3/S

m3

)

E-1H well, GCR from wet-gas meter

3. Development and implementation of new allocation routine

Since the source of wrong allocation was identified, a simple method for correct allocation was

worked out. Instead of using theoretical gas and condensate rates from the wet-gas meters, it was

decided to calculate the theoretical gas and condensate rates from wet-gas meters outside of

production database system.

3.1. Use of total hydrocarbon (HC) mass rates:

The first step was to use the hydrocarbon mass rates from the wet-gas meters. It was concluded

by study done by the wet-gas meter vendor that the hydrocarbon mass rates are reliable

measurements from the meter at the line (subsea) conditions. The hydrocarbon mass rates output

from the wet-gas meter depend on the Gas-volume fraction (GVF), Condensate-volume fraction

(CVF) and Water-volume fraction (WVF). The source of error in the theoretical rates from wet-gas

meter, as stated before in section 2, was the PVT package inside the wet-gas meter because the fluid

composition was not updated regularly.

3.2. Conversion of total HC mass rates to gas and condensate splits:

The PVT experts in Statoil were consulted on a methodology to split the total HC mass rate to

gas and condensate. The split into gas and condensate at process conditions was possible to be

calculated using the initial fluid compositions from each reservoir, monthly gas and condensate

compositional analysis of export gas and condensate streams from SLT platform and the history

matched black-oil reservoir simulation model.

Since the intrinsic permeability in the Sleipner Vest reservoirs has been estimated to be good, it

was assumed that the Constant Volume depletion (CVD) experiments represent the depletion of a

gas condensate reservoir. Simulation of CVD was used to define a relationship between the reservoir

pressure, GCR and well-stream composition, making it possible to estimate the composition of

production streams in a ‘black-oil’ reservoir simulator.

The separation of the production streams into export gas and condensate (i.e. re-constructed

GCR) was then calculated on the basis of monthly compositional analysis of export gas and

condensate streams on SLT platform.

Table 1 gives an example of this calculation based on the above described method.

3.3. Implementation of new allocation routine

Once the split of total HC mass into gas and condensate was done, the new method was easily

and robustly applied in the production database. A retroactive re-allocation was performed using the

re-constructed GCR for Alfa Nord wells. Figure 3 illustrates the GCR before and after the

application of new methodology.

Table 1 – Illustration of re-construction of GCR for month of October 2004

*: 71.64 tons of CO2 was removed from the gas stream on Sleipner T

**: C6+ fraction for gas (C6 fraction for condensate)

Figure 3 – GCR development for one of the Alfa Nord wells (before and after implementing

correction)

Component

Export gas Condensate Component split factor

Mol.weight Composition Composition Gas Condensate

(mol%) (mol%) (wt%) (wt%)

Nitrogen 28.013 0.766 1.113 0.00 1.000000 0.000000

CO2 * 44.01 2.929 6.690 0.46 0.978016 0.021984

Methane 16.043 84.237 70.138 0.53 0.997482 0.002518

Ethane 30.07 8.490 13.249 4.41 0.899931 0.100069

Propane 44.097 2.898 6.632 14.6 0.576220 0.423780

i-Butane 58.124 0.231 0.698 4.58 0.313307 0.686693

n-Butane 58.124 0.324 0.978 8.86 0.248326 0.751674

i-Pentane 72.151 0.048 0.181 4.13 0.116151 0.883849

n-Pentane 72.151 0.038 0.144 4.37 0.089687 0.910313

C6 fraction ** 90 ** 0.038 0.177 6.58 0.074617 0.925383

C7 fraction 8.78 0.000000 1.000000

C8 fraction 10.93 0.000000 1.000000

C9 fraction 6.89 0.000000 1.000000

C10+ fraction 24.88 0.000000 1.000000

Total mass (tons) 490807 163961

2500

2700

2900

3100

3300

3500

3700

3900

4100

4300

4500


GC

R (

Sm

3/S

m3

)

E-1H

Corrected GCR WGM GCR

4. Results and Discussion

Figures 4 and 5 show the theoretical gas volumes and theoretical condensate volumes,

respectively from well E-1H (Alfa Nord well) before and after implementing the new allocation

routine. The %age difference between correct and incorrect theoretical condensate production has

been much more than that of the gas production. As mentioned before, the original allocation routine

was underpredicting theoretical gas production and overpredicting condensate production.

With implementation of new allocation method, the allocation factor for gas and condensate at

Sleipner Vest improved (especially with respect to condensate, see figures 6 and 7). The AF for

condensate improved to acceptable error of ±5%. This shows that new allocation routine was able to

normalize the individual well production better to the overall fiscal metered and measured volumes

from the SLT platform.

The availability of compositional analysis of export gas and the condensate from SLT platform

and the relatively simple production drainage for Sleipner Vest reservoir (pressure depletion)

combined with the good reservoir quality sands of Sleipner Vest and Alfa Nord reservoir helped to

develop an alternative allocation methodology to correctly find theoretical well rates.

Figure 4 - Theoretical gas production from well E-1H before and after implementing the new

allocation routine

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

0

10

20

30

40

50

60

70

80

90


% e

rro

r fr

om

co

rre

ct

rate

s

Mo

nth

ly g

as

pro

du

cti

on

, M

Sm

3

E-1H (monthly gas production)

Correct theoretical rates WGM theoretical rates % error from correct rates

Figure 5 - Theoretical condensate production from well E-1H before and after implementing the new

allocation routine

Figure 6 – Allocation factor (AF) for gas and condensate with new allocation routine

-100

-80

-60

-40

-20

0

20

0

5

10

15

20

25

30


% e

rro

r fr

om

co

rre

ct

rate

s

Mo

nth

ly c

on

de

nsa

te p

rod

uc

tio

n, k

Sm

3

E-1H (monthly condensate production)

Correct theoretical rates WGM theoretical rates % error from correct rates

Figure 7 – Allocation factor (AF) for gas and condensate with old allocation routine

5. Conclusions

A relatively simple allocation methodology was developed to overcome the issue of poor allocation

at Sleipner Vest Field. It is shown that it is extremely important to update the fluid compositions

regularly in the wet-gas meters wherever they are used for allocation purposes. Our method described

here can be used fairly easily by any operator if the conditions (i.e reservoir, processing and

measurements) are similar to that described in this paper, if they face the difficulty of getting access to

wet-gas meter for PVT updating. The method is robust enough to be implemented in any production

allocation engine.

Acknowledgements

The authors would like to thank all the Sleipner team for their collaborative effort in this project.

Also, we would like to thank the Sleipner License partners, ExxonMobil, Total and Statoil (operator) to

let the results being published.

fltough

Stamp

North Sea Flow Measurement Workshop

22 – 25th

October 2013

1

FLOW MEASUREMENT OF HIGH VISCOSITY FLUIDS

Chris Mills, Craig Marshall, Andy Kay, Marc MacDonald NEL.

1 INTRODUCTION

The vast majority of the world’s remaining oil reserves are categorised as heavy /

unconventional oils (high viscosity). Due to diminishing conventional oil reserves and

the need to secure future energy supplies to a rising world population, the exploitation

of unconventional oils will increase. As the development of these viscous deposits

grows, so too will the requirement for accurate flow measurement of heavy crude oils

and other viscous products.

Unfortunately, the performance of conventional flowmeters when applied to viscous

fluids1 remains relatively poorly known. However, a number of technical challenges

are immediately identifiable. These include the higher viscous friction of the fluid

being metered, the possibility of extreme or varying velocity profiles, and the

increased susceptibility of viscous liquids to entrain secondary components such as

solids or gas. It is reasonable to predict that different metering devices will be affected

by these phenomena in different ways, but to date the most appropriate technologies

for viscous flow measurement are not yet well defined.

Well established flow measurement technologies such as differential pressure devices

are known to be sensitive to viscosity variations. However there is little published data

on their performance across a range of viscosities and Reynolds number. The same

can also be said for “newer” measurement technologies such as Coriolis and

ultrasonic devices. Confusion can arise when flowmeter manufacturer’s make specific

claims on performance with little independent and verifiable data published. This

follows partly from the scarcity of suitable test facilities capable of providing viscous

flow in combination with accurate and traceable reference instrumentation.

To improve this situation, NEL has completed an investigative programme into the

performance of two Coriolis, three ultrasonic, two Venturi tubes and two quadrant

edge orifice plates when evaluated across a range of high viscosity conditions. This

paper reports on the response of these devices when operated from nominally 100 –

1500 cSt at the UK National Standards Oil Flow Facility at NEL in Glasgow,

Scotland.

2 FLOW MEASUREMENT CHALLENGES

Flow measurement of ‘medium’ and ‘heavy’ crude oils present additional technical

challenges compared to ‘light’ crude oils due to their greater viscous friction.

Challenges such as irregular flow regimes and varying viscosity will be discussed in

more detail in the sections below.

1 For the purpose of this paper, ‘high’ viscosity in relation to hydrocarbon liquids, is

taken as a kinematic viscosity > 100 cSt.

fltough

Stamp


22 – 25th

October 2013

2

2.1 Characteristics of Viscous Fluids

2.1.1 Flow Regime

The level of force exerted due to viscous friction

can be characterised by its viscosity coefficient. The

absolute viscosity of a fluid can be expressed in

centi-Poise (cP). The higher the value of absolute

viscosity, the greater the frictional viscous forces on

the pipe wall. As an example, the viscosities of

some common fluids are listed in Table 1.

Another often quoted definition for viscosity is the kinematic viscosity. It is defined as

the ratio of the fluid’s dynamic viscosity to its density and is generally quoted in centi-

Stokes (cSt). The SI units are m2/s and the unit conversion is 1 m

2/s = 1 x 10

6 cSt.

For most liquids, an increase in temperature normally results in a decrease in fluid

viscosity. The decline in viscosity with increasing temperature is generally far greater

for highly viscous fluids and can pose several problems. Figure 1 displays the

kinematic viscosity of two NEL test fluids, one light and one heavy, plotted against

fluid temperature.

Problems can arise in the flow measurement of viscous fluids when small fluctuations

in temperature result in a significant

change in the fluid viscosity. If the

flowmeter has been calibrated at a

specific viscosity for its application,

any temperature and thus viscosity

fluctuation could potentially have a

notable effect on the flow

measurement.

In highly viscous fluids there can be

distinguishable variations in the

measured temperature due to thermal

gradients within the flow path. Thermal gradients are often present in laminar flow

due to the parabolic velocity profile that occurs. As there is no mixing taking place

between the layers of fluid, the fluid at the centre of the pipe will be at a different

temperature than the fluid at the pipe wall. These thermal gradients can make it

problematic to obtain a suitably representative mean fluid temperature. This increases

the uncertainty of any temperature based correction applied by the device.

The velocity profile of the fluid is also considerably altered by changes in the fluid

viscosity. The influence that fluid viscosity exerts on the velocity profile is best

defined using the Reynolds number (Re), the dimensionless ratio of inertial forces to

viscous forces in a flowing fluid. Reynolds number can be written as:

DUeR (1)

Table 1 – Fluid viscosity

Fluid Type Viscosity

at 20 OC (cP)

Water 1

Engine Oil 100

Gear Oil 1000

Honey 10000

Figure 1 NEL test fluid viscosities

Figure 1 – NEL test fluid viscosities


22 – 25th

October 2013

3

Where:

U = Average fluid velocity [m/s] D = Pipe diameter [m]

= Kinematic Viscosity [m²/s]

A flowing fluid travels in one of three different flow regimes. Low viscosity fluids

travelling at moderate velocities would normally have a high Reynolds number

(greater than ~10,000), leading to turbulent flow (Figure 2a). In this regime dynamic

forces dominate and the motion is parallel to the pipe axis with mixing occurring

between the different layers. When the Reynolds number is low (less than ~2,000) the

flow is laminar (Figure 2b). In this regime viscous forces dominate and there is no

mixing between the layers. The regime between laminar and turbulent flow is

described as ‘transitional’ and can be extremely unpredictable. The flow quickly

switches back and forth between laminar and turbulent behaviour and can cause

significant flow measurement challenges.

The flow regime has a direct impact on the shape of the flow profile within the pipe.

The velocity profile defines how quickly the liquid is travelling at various points

across the cross section of the pipe. Fully developed velocity profiles for laminar and

turbulent flow are shown in Figure 3.

In laminar flow, viscous forces dominate

causing substantial friction against the pipe

wall and the fluid. This results in drag

between the layers of the fluid with the fluid

velocity gradually increasing from the pipe

wall to the centre. The maximum velocity at

the centre of the pipe can be approximately

twice the average velocity of the flow which

results in a velocity profile that is parabolic

in shape.

In turbulent flow, the mixing action caused by the dynamic forces breaks up any

gradual transfer of drag from the pipe wall. This results in a well-mixed flow with a

relatively flat velocity profile. The central axis of fully developed turbulent flow

normally has a value of 1.1 to 1.3 times the average flow velocity.

2.2 Scope of Current Work

The effect that medium fluid viscosities have on the current generation of liquid

flowmeters (Coriolis, ultrasonic and turbine) has already been reported [1] [2]

. However

the effect of high viscosity fluids (> 300 cSt) on conventional flowmeters has not yet

been defined with independent and verifiable test data. This follows partly from the

Figure 3 Velocity Profiles

Figure 3 – Laminar and

turbulent velocity profiles.

(a) TURBULENT FLOW (b) LAMINAR FLOW

Ave

rage

vel

oci

ty o

ver

cro

ss

sect

ion

Distance from centre in units of pipe radius

Figure 2 (a) turbulent and (b) laminar flow conditions


22 – 25th

October 2013

4

scarcity of suitable test facilities capable of providing viscous flow in combination

with accurate and traceable reference instrumentation.

The scope of work for the test programme was to explore the performance of

conventional flowmeters at elevated liquid viscosities (> 100 cSt). The investigations

reported in this paper focus on the performance of differential pressure devices,

Coriolis devices and ultrasonic flowmeters. This paper also includes computational

fluid dynamics (CFD) results for the two 8 inch Venturi flowmeters evaluated.

3 TEST METERS

3.1 Test Meter Descriptions

The flowmeters evaluated in this test programme ranged from nominally 6 - 8 inch

(152.4 – 202.72 mm). This project was not structured as an evaluation of any

particular manufacturer or flowmeter model, but rather as a generic evaluation of

some of the effects of high viscosity on conventional flowmeter technology. As such,

the manufacturer of the flowmeters evaluated in this programme will not be named.

The specifications of the flowmeters evaluated are detailed in Table 2.

Table 2 – Test Meter Specifications

Meter Test Package Meter Type Characteristics

A 2 Venturi Nominal size

Internal diameter (entry pipe)

Throat diameter

:

:

:

8-inch, β = 0.4

202.72 mm

81.10 mm

B 2

Venturi Nominal size


Throat diameter

:

:

:

8-inch, β = 0.6

202.72 mm

121.64 mm

C 5

Quadrant

edge orifice

plate

Nominal size


Throat diameter

:

:

:

8-inch β = 0.45

193.8 mm

91.44 mm

D 5

Quadrant

edge orifice

plate

Nominal size


Throat diameter

:

:

:

8-inch β = 0.6

193.8 mm

121.92 mm

E 1

Ultrasonic Nominal size

Flowrate range (approx.)


:

:

:

6-inch

11 to 227 l/s

152.4 mm

F 3


Recommended Reynolds range


:

:

:

6-inch

> 10 000

152.4 mm

G 4


Flowrate range (approx.)


:

:

:

6-inch

20 to 200 l/s

150 mm

H

1

Twin tube

Coriolis

Nominal size

Maximum flowrate (approx.)


:

:

:

6-inch

222 kg/s

152.4 mm

I 3

Twin tube

Coriolis

Nominal size

Maximum flowrate (approx.)


:

:

:

6-inch

250 kg/s

152.4 mm


22 – 25th

October 2013

5

3.2 Differential Pressure Devices – Venturi & Quadrant Edge Orifice Plate

Differential pressure (DP) devices are a well established flow metering technology

and have been used in industry for decades. Many new flow metering technologies

have been developed in recent years, but differential pressure devices are still utilised

for flow measurement applications across the world.

This is because differential pressure devices are relatively simple and inexpensive to

construct, contain no moving parts and thus require very little maintenance. They can

be used in any orientation and are suitable for use in most liquids and gases. There is a

large amount of experience using these devices and their performance is well

documented. Certain differential pressure devices can be used without calibration by

calculating the discharge coefficient using the ISO 5167-1 standard [3].

However, the standard does not present discharge coefficients for Reynolds numbers

below 5,000 for orifice plates and 20,000 for Venturi tubes and numerous

experimental studies have shown a logarithmic relationship between Reynolds

number and discharge coefficient in laminar flows. As such, it is expected that

traditional DP devices will encounter significant errors when used in highly viscous

fluids.

As the ISO 5167 standard only includes limited data for Reynolds numbers down to

50000, we must slightly alter the standard equation by removing the discharge

coefficient. Thus, the mass flowrate can be calculated using the following equation:

41

2

fluid

tT

PAQ (2)

The discharge coefficient can be calculated by dividing the reference mass flowrate by

the calculated mass flowrate.

T

ref

DQ

QC (3)

Where:

ΔP = Dynamic Pressure Drop fluid

= Fluid Density

At = Throat Area CD = Discharge Coefficient

β = Diameter Ratio (throat ÷ pipe)

Quadrant edge orifice plates are believed to be more suitable for high viscosity

applications than standard orifice plates. The inlet edges of quadrant edge orifice

plates are rounded to quarter circles, which reduces pressure drop across the plate.

Quadrant edge orifice plates have higher discharge coefficients than standard

concentric edge types (typically 0.8 and 0.6 respectively for turbulent flows). Unlike

concentric orifice plates, quadrant edge devices are known for producing near-

constant discharge coefficients at very low Reynolds numbers. Thus in theory they

should be suitable for high viscosity flow applications.


22 – 25th

October 2013

6

Figure 4 8 inch β 0.6 Quadrant Edge Orifice Plate Inlet

ISO 5167-2[4]

provides discharge coefficients for orifice plates with various pressure

tapping locations, but does not provide guidance for quadrant edge orifice plates.

There are various sources that can provide estimates for quadrant edge discharge

coefficients despite the lack of an international standard.

The Shell Flowmeter Engineering Handbook presents the following equation for

quadrant edge orifice plates where discharge coefficient is a function of Beta (β)[5]

.

32 5084.11615.13309.073823.0 DC (4)

The equation has a stated uncertainty of ±2.00 % for Beta ratios greater than 0.316.

With the experimental data collected for these orifice plates, the predicted discharge

coefficient has been calculated and plotted on the graphs with 2.00 % uncertainty

bands.

One of the main advancements in flow measurement in the last five years has been the

introduction of the Prognosis [6]

measurement system by DP Diagnostics. One of the

main advantages of the Prognosis system is its simplicity. It utilises two additional

differential pressure measurements across the DP meter and can be explained in more

detail below.

Figure 5 shows a generic DP meter with a third pressure tap allowing the traditional

DP (ΔPt), a recovered DP (ΔPr), and a permanent pressure loss DP (ΔPppl) to be read.

This allows a full patented generic DP meter diagnostic suite to be available.

The sum of ΔPr and ΔPppl must equal ΔPt (equation 5). This fact allows a DP reading

check. Each DP offers an independent flowrate prediction, i.e. the traditional DP

meter flowrate prediction (equation 6), the expansion DP meter flowrate prediction

(equation 7), and the PPL DP meter flowrate prediction (equation 8).

Figure 5 Venturi meter instrumentation sketch and pressure fluctuation graph


22 – 25th

October 2013

7

PPLrt PPP

uncertainty ± θ% (5)

Traditional Flow Equation: tdt PCEAm 2

uncertainty ± x% (6)

Expansion Flow Equation: rrt PKEAm 2 uncertainty ± y% (7)

PPL Flow Equation: PPLppl PAKm 2

uncertainty ± z% (8)

These three flowrate predictions can be compared. The percentage difference between

any two flowrate predictions should not be greater than the root mean square of the

two flowrate prediction uncertainties. Table 3 displays the flowrate predictions.

Table 3 – Flowrate prediction pair diagnostics

Flow Prediction Pair % Actual

Difference

% Allowed

Difference Diagnostic Check

Traditional & PPL % % 1%%1

Traditional & Expansion % % 1%%1

PPL & Expansion % % 1%%1

With three DPs read, there are three DP ratios:

PPL to Traditional DP ratio (PLR): ( PPLP / tP)reference,

uncertainty ± a%

Recovered to Traditional DP ratio (PRR): ( rP / tP)reference,

uncertainty ± b%

Recovered to PPL DP ratio (RPR): ( rP / PPLP )reference, uncertainty ± c%

A DP meter’s DP ratios are characteristics of that meter. DP ratios found in service

can be compared to their expected values. The difference between a found and

expected value should not be greater than the reference DP ratio uncertainty. Table 4

displays the flowrate prediction pair diagnostics.

Table 4 – DP Ratio diagnostics

DP Ratio % Actual to Ref

Difference

% Reference

Uncertainty Diagnostic Check

PLR % %a 1%%1 a

PRR % %b 1%%1 b

RPR % %c 1%%1 c

Any inference that Equation 5 does not hold is a statement that there is a malfunction

in one or more of the DP transmitters. The sum of ΔPr and ΔPppl gives an ‘inferred’

ΔPt,inf. The inferred and directly read traditional DP should not be greater than the root

mean square of the combined DP transmitter uncertainties. Table 5 displays the DP

reading integrity diagnostics.

Table 5 – DP Reading Integrity Diagnostic

% Actual to Inferred

Traditional DP Difference

% RMS Combined DP

Reading Uncertainty

Diagnostic

Check

% % 1%%1


22 – 25th

October 2013

8

Table 6 displays the seven possible situations where these diagnostic would signal a

warning. For convenience we use the following naming convention:

Normalized flowrate inter-comparisons:

Normalized DP ratio comparisons:

Normalized DP sum comparison:

Table 6 – The DP meter possible diagnostic results

DP Pair No Warning WARNING No Warning WARNING

tP & pplP

-1 ≤ x1 1 -1< x1 or x1 1 1 ≤ y1 1 -1< y1 or y1 1

tP & rP -1 ≤ x2 1 -1< x2 or x2 1 1 ≤ y2 1 -1< y2 or y2 1

rP & pplP -1 ≤ x3 1 -1< x3 or x3 1 1 ≤ y3 1 -1< y3 or y3 1

readtP, & inf,tP

-1 ≤ x4 1 -1< x4 or x4 1 N/A N/A

Figure 6 Normalized diagnostic box (NDB) with diagnostic results & DP check

For practical use, a graphical representation is simple and effective. A box is drawn

centred on a graph’s origin. Four points are plotted representing the seven diagnostic

checks (Figure 6). If the meter is fully serviceable all points must be inside the box.

One or more points outside the box indicate a malfunction. The diagnostic pattern of

an alarm offers information on the source of the malfunction. Different malfunctions

can cause different patterns. Steven et al [6]

gives a review of these diagnostics.

3.3 Coriolis

Coriolis flowmeters provide a direct measurement of mass flowrate and product

density with stated uncertainties as low as 0.1% and 0.05 kg/m3 respectively for light

hydrocarbons. Advantages such as high accuracy, claimed insensitivity to installation

and direct measurement of mass flow have led to wide scale adoption across a number

of sectors, including the food, pharmaceutical and process industries.

Figure 7 Examples of Coriolis Flow Tube Configurations

x4 = %%

y1 = %% a , y2 = %% b , y3 = %% c

x1 = %% , x2 = %% , x3 = %%


22 – 25th

October 2013

9

Coriolis manufacturers have previously claimed to have negligible sensitivity to

increasing fluid viscosity. Some manufacturers now accept that Coriolis devices have

a sensitivity to flow profile / low Reynolds numbers with viscous fluids [7]

.

Coriolis meters are prone to “zero offset” errors, and so the meters were zeroed when

first installed in the test line, and after each temperature change. The Coriolis devices

were evaluated under these conditions for the duration of the test programme.

The performance of Coriolis flowmeters is detailed in the ISO standard 10790 [8]

.

However, the performance of Coriolis meters in high viscosity fluids and the potential

adverse effect of flow profile / low Reynolds numbers are not addressed.

3.4 Ultrasonic

Ultrasonic flowmeters are currently employed in a variety of custody transfer and

allocation measurement systems for conventional “light” oils. They have been known

to achieve measurement uncertainties of better than ±0.15 % over most of their

turndown range. However, one property that may adversely affect the performance of

these meters is the variation in velocity profile that occurs with pipe Re number.

To maintain an accurate estimate for volumetric flowrate, ultrasonic meters must

reliably determine the mean fluid velocity from discrete measurements of fluid

velocity along the ultrasonic paths. Many manufacturers will apply corrections to the

meters for the velocity profile of the flow.

Figure 8 Typical In-Line USM Transducer Set Up

Depending on the transducer frequency employed by the ultrasonic device, high

viscosity fluids can cause signal attenuation. This could potentially lead to substantial

errors in the measurement of the flow due to less “successful” measurements of the

flow velocity being recorded by the device. By utilising the signal diagnostics from

the ultrasonic device, it’s possible to ascertain whether signal attenuation has

occurred.

Full bore ultrasonic meters introduce no additional pressure drop compared to a

standard straight length of pipe. This may be of particular advantage over other meter

types for use in high viscosity fluids where pressure drops are considerably large.

However some manufacturers specify that their device should be installed with a flow

conditioner and 10 diameters of straight pipe lengths upstream and 5 diameters

downstream. Depending on the fluid viscosity and velocity, this could potentially be a

sizeable additional pressure drop. This could be a key consideration in high viscosity

applications.


22 – 25th

October 2013

10

The ISO standard for ultrasonic devices, ISO 12242, does not cover in depth the effect

that fluid viscosity and fluid Reynolds number has on the performance of ultrasonic

devices [9]

.

4 EXPERIMENTAL PROGRAMME

4.1 Oil Flow Facility

The experimental programme was completed in 2012 at the UK National Standards

Oil Flow Facility, located at NEL in Glasgow, Scotland. The facility consists of two

separate flow circuits (A and B), each with a high capacity and a low capacity flow

line. These can accommodate nominal pipe sizes from 0.5” to 10”, and can operate at

line pressures up to 10 bar. Test fluids can be delivered at flowrates up to 720 m3/hr.

Six test fluids are available in this facility – Kerosene, Gas Oil, Velocite, Primol,

Siptech and Aztec – covering liquid viscosities from 2 to 1500 cSt. Figure 9 displays

the kinematic viscosity of NEL’s test fluids for the Oil Flow Facility in 2013.

Figure 10 shows a schematic diagram

of the flow circuits. The oil for each

circuit is drawn from a 30 m3 supply

tank, from where it is discharged to the

test lines. A conditioning circuit,

linked to each tank, maintains the oil

temperature to within ± 0.5 ºC of a

pre-selected value (itself set in the

range 10 – 50 ºC).

Line temperature and pressure are monitored both upstream and downstream of the

test section. The flow lines share a common primary standard weighbridge system

consisting of four separate weigh tanks of 150, 600, 1500 and 6000 kg capacity. The

facility is fully traceable to National Standards and is accredited by the United

Kingdom Accreditation Service (UKAS) to ISO 17025.

Figure 10 Schematic diagram of the NEL oil flow test facility

Figure 9 Kin. Viscosity of NEL test fluids


22 – 25th

October 2013

11

4.2 Reference System

For “primary” calibrations, a gravimetric “standing-start-and-finish” method is used to

determine the quantity of fluid (volume or mass) which has passed through the

flowmeter under test and into the selected weigh tank. The gravimetric weigh tanks

constitute the primary reference standard of the NEL oil flow facility. Using the above

technique, the overall uncertainty in the quantity of fluid passed, expressed at the 95%

confidence limit is ± 0.03 % (k = 2). For a “secondary” calibration, the quantity of oil

passing through the test meter is measured using a pre-calibrated reference meter,

installed in series. The reference meters used at NEL have a history of previous

calibrations and typical uncertainties in the quantity of fluid passed of the order of ±

0.08 % (k = 2). This applies to oils with a kinematic viscosity between 2 – 30 cSt.

In the evaluation programme the oil flow facility was operated in ‘re-circulation’

mode and the test meter compared against secondary reference standards. The

reference meters used in the present test programme consisted of two 8-inch rotating-

vane Positive Displacement device. The “K-factor” for this type of PD meter can be

considered to be a function of three main parameters: the volumetric flowrate (Q), the

liquid viscosity () and the fluid temperature (T).

The PD meters were calibrated (as a function of flowrate only) for the fluid

temperature and fluid type detailed in the test matrix. This provided the most accurate

reference for testing and produced a K-factor curve of the form:

QfK TFTF ,, (5)

Where F and T denote the fluid type or test temperature respectively. The resultant

uncertainty of the PD meters in service was of the order of 0.15% at the 95%

confidence level.

4.3 Test Matrix

To investigate the sensitivity of the flowmeters to elevated fluid viscosity, a series of

tests were made on the flowmeters at a controlled and monitored rate. The test

installations were based around standard pipeline runs of 6 or 8-inch nominal bore

depending on the flowmeter dimensions. Tests were conducted at a range of flowrates

using Aztec as the test fluid. The nominal viscosity range covered was 100 – 1500 cSt.

To enable the effects of elevated viscosity to be evaluated, the nominal test matrix of

Table 7 was proposed. Figure 11 displays the corresponding pipe Reynolds numbers

covered in the test programme.

Table 7 – Nominal Test Matrix

Fluid Temperature

(oC)

Kin. Viscosity

(oC)

Flowrate (l/s)

Min Max

Aztec 12 - 45 1500 - 100 10 130


22 – 25th

October 2013

12

0

20

40

60

80

100

120

140

10 100 1000 10000 100000

Vo

lum

e F

low

[l/

s]

Reynolds Number

100 cSt (45degC)

200 cSt (40degC)

600 cSt (20degC)

1000 cSt (17degC)

1500 cSt (12degC)

Figure 11 Range of Pipe Reynolds numbers Covered by Test Matrix

5 TEST RESULTS

The test programme was completed in 2012 independently by NEL. The flowmeter

manufacturers were involved in setting up their own devices but did not influence the

test programme.

It should be noted that the test results are for high viscosity flow measurement using

conventional liquid flowmeters. This remains a problematic area for flow

measurement technologies and improving the uncertainty in this area requires further

investigation.

Differential Pressure Devices – Venturi Tubes

Meter A – 8” β = 0.4

The experimentally measured discharge coefficients for the Venturi are shown in

Figure 12 below. The calculated discharge coefficients were plotted against the throat

Reynolds number for a variety of temperatures, viscosities and flowrates, and were all

found to lie on a single curve.

The discharge coefficient conforms to what is described in ISO 5167-4 [10]

and

increases with Reynolds number. The trend tends towards horizontal as it moves into

the turbulent regime. Data points from the full range of viscosities form a single, well

defined curve. The minimum discharge coefficient observed is 0.638 at a Reynolds

number of 114. The maximum is 0.955 at a Reynolds number of 20568.

It is important to note that Reynolds numbers are plotted on a logarithmic scale, and it

can be seen that very sharp changes in discharge coefficient occur with only small

changes in Re in the laminar region.


22 – 25th

October 2013

13

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t

Throat Reynolds Number

Meter A - 8" β = 0.4 Venturi, Mar 2012

100 cSt

200 cSt

600 cSt

1000 cSt

1500 cSt

Figure 12 Venturi A – Discharge Coefficient vs. Throat Reynolds number

Meter B – 8” β = 0.6

The discharge coefficient curve for Meter B is well defined with data series from the

full range of viscosities overlapping closely. Changes in discharge coefficient are very

sudden at low Reynolds numbers but the curve tends towards horizontal as it moves

towards the turbulent regime.

Unlike Meter A, the data for Meter B features a small “hump” at Reynolds numbers of

approximately 2000. Literature sources typically describe discharge coefficient trends

for Venturi tubes as smooth curves with no mention of a “hump” in this range, but this

is a feature that has been observed previously at NEL [11]

and by others [12] [13]

. The

minimum discharge coefficient observed is 0.625 at a Reynolds number of 116. The

maximum is 0.951 at a Reynolds number of 15280.

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t


Meter B - 8" β = 0.6 Venturi, Mar 2012

100 cSt

200 cSt

600 cSt

1000 cSt

1500 cSt

Figure 13 Venturi B – Discharge Coefficient vs. Throat Reynolds number


22 – 25th

October 2013

14

The discharge coefficient curves for Meter A and Meter B are compared in Figure 14.

It can be seen that at higher Reynolds numbers, there is little difference in discharge

coefficients between the two Venturi tubes. Meter A has a discharge coefficient of

0.955 at ReT = 20568 and Meter B has a discharge coefficient of 0.951 at ReT =

15280.

As mentioned previously, a “hump” is observed for Meter B but not for Meter A. This

is likely because the smaller internal diameter of Meter A has a conditioning effect on

the flow, causing the flow regime to shift from laminar to turbulent more suddenly.

Meters A and B conform to the ISO 5167-4 [10]

definition of “Classical Venturi tube

with a machined convergent section”. A steady discharge coefficient of 0.995 should

be achieved for Reynolds numbers in the range 2 x 105 ≤ Re ≤ 1 x 10

6. However, the

guidelines in the standard do not apply to heavy oils operating predominantly in the

laminar and transitional flow regime.

The classic definitions for laminar and turbulent flow are Re < 2000 and Re > 4000

respectively, with transitional flow occurring between the two regimes. However, it is

also acknowledged that the transition point can occur at higher Reynolds and can be

influenced by upstream installation conditions, pipe incline and surface roughness.

At ReT = 5000 both Venturi tubes showed discharge coefficients of approximately

0.93. If compared to the discharge coefficient of 0.995 stated in the standard, this

represents a flow error of greater than 6.5 %. At Re = 1000, flow error would be

greater than 9.5%.

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t


Comparison of Meter A (β = 0.4) and Meter B (β = 0.6)

Meter A

Meter B

Figure 14 Comparison of Venturi Tubes

This study has shown that in the laminar or transition region the measured value of the

discharge coefficient deviates significantly from the value given in the standard of

0.995. The possible reasons for this deviation are discussed in the CFD Study

completed by NEL.


22 – 25th

October 2013

15

NEL compared the experimental test results for the two 8 inch Venturi meters to those

determined from CFD simulations over a range of flowrates and fluid viscosities. The

purpose of the study was to determine the capability of computational fluid dynamics

(CFD) to accurately predict the flow of heavy oils through a Venturi meter.

The CFD code, ANSYS FLUENT 13.0 was used to model the flow through two

Venturi meters. For simplicity and speed of convergence, the geometry of each

Venturi was modelled using a two-dimensional asymmetric domain with a structured

grid. The meters correspond to Meter A and Meter B described in the main body of

this report. The meters are designed in accordance with the industry standard [10]

.

Figure 15 and Figure 16 show the modelled and experimentally obtained Venturi

discharge coefficient plotted against the Venturi throat Reynolds number for Meter A

and Meter B, respectively. Despite investigating oils of varying viscosities and

flowrates, both plots show the modelled CD values falling on a single curve when

plotted against throat Reynolds number. This observation is mirrored by that

witnessed in the test data. This reinforces the assumption that CD is a function of

throat Reynolds number only. Figure 15 indicates excellent agreement between the

CFD simulations and the test data across the complete Reynolds number range

examined. The curvature of the trend is predicted very well. With heavy oils, the flow

is more often laminar due to the high viscosity; this region on the curve is predicted

especially well using the laminar model in Fluent for ReT < 2300.

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000


Dis

ch

arg

e C

oe

ffic

ien

t

8 inch B = 0.4 (TEST DATA)

8 inch B = 0.4 (LAMINAR MODEL)

8 inch B = 0.4 (TRANS k-kl-ω MODEL)

8 inch B = 0.4 (k-ω MODEL)

Figure 15 Meter A – Experimental & CFD Results for CD vs. ReT

In Figure 16 there is excellent agreement between CFD and the test data up to ReT =

3449. The model again performs well within the laminar regime expected for most

heavy oil flows. At some point above ReT = 3449, transition to turbulent flow occurs

within the boundary layer formed on the pipe wall; this is observed by the sudden

decrease in the discharge coefficient, known as the “hump”, associated with laminar

to turbulent transition occurring through the meter. The k-kl-ω transition model

accurately predicts the onset of the “hump” at ReT = 3449; however, the gradient of

the “hump” is too severe in comparison to the test data. As a result, CD values are


22 – 25th

October 2013

16

under predicted up to ReT = 6581. At turbulent Reynolds numbers above ReT = 6581,

as the discharge coefficient recovers, the gradient of the CFD data is again slightly

more severe than in test data, resulting in over predicted values of CD.

The presence of a “hump” in the modelled and experimental data for Meter B is

suggested in literature to be a result of laminar-turbulent transition occurring through

the meter. Shlichting offers an explanation of experimental results on transition from

laminar to turbulent flow.

Shlichting observed transition to cause a notable change in the velocity distribution

over the pipe cross section [14]

. In laminar flow, the velocity distribution over the pipe

cross section is parabolic; however, in turbulent flow, the velocity distribution will

become more uniform and flat. Figure 17 shows plots of the velocity profile at the

Venturi throat pressure tap determined from the CFD simulations of Meter B. The y-

axis shows the throat radius where 0 represents the centre of the throat. The velocity

profile is shown clearly evolving from a parabolic to flattened shape with increasing

throat Reynolds number. Figure 16 shows the “hump” beginning at around ReT =

3500. Figure 18 shows the difference between the velocity profile at ReT = 3449 and

sometime after at ReT = 4004, as the discharge coefficient starts to decrease. The

abrupt jump from a parabolic to uniform velocity distribution over such a small range

of ReT is indication that transition has occurred.

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000


Dis

ch

arg

e C

oe

ffic

ien

t

8 inch B = 0.6 (TEST DATA)

8 inch B = 0.6 (LAMINAR MODEL)

8 inch B = 0.6 (TRANS k-kl-ω MODEL)

8 inch B = 0.6 (k-ω MODEL)

Figure 16 Meter B – Experimental & CFD Results for CD vs. ReT

O. Reynolds [15]

determined from early investigations that transition from laminar to

turbulent flow always takes place at about the same critical Reynolds number, found

to be Recrit = 2300. He later stated that the critical Reynolds number can increase as

the disturbances in the flow are decreased. The CFD simulations were run assuming a

smooth pipe; as both the modelled and experimental data predict the onset to the

“hump” at ReT ~ 3500; this is a good indication that flow disturbances in the flow

both leading up to, and through the meter, were minimal during the experiments.


22 – 25th

October 2013

17

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Normalised Velocity (U/UT)

y

Re = 224

Re = 337

Re = 553

Re = 1484

Re = 2390

Re = 3449

Re = 4004

Re = 4521

Re = 6581

Re = 15358

Figure 17 Meter B – Velocity Profiles at the Venturi Throat Pressure Tap over a

Range of Throat Reynolds numbers

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Normalised Velocity (U/UT)

y

Re = 3449

Re = 4004

Figure 18 Meter B – Velocity Profiles at the Venturi Throat Pressure Tap at two

Throat Reynolds numbers

Shlichting goes on to state that transition from laminar to turbulent flow is

accompanied by a large change in the resistance to flow [14]

. For flow through pipes

this equates to a large change in skin friction. This change in friction has a noticeable

influence on the longitudinal pressure gradient and therefore, the Venturi pressure

drop. For laminar flow, the longitudinal pressure gradient is proportional to the first

power of velocity [14]

. In turbulent flow the pressure gradient becomes nearly

proportional to the square mean velocity of the flow [14]

. This influence on the Venturi

pressure drop may explain why the discharge coefficient appears to follow two

separate curves in Figure 16: one for laminar flow (Re = 235 to 3500) and one for

turbulent flow (Re = 5000 to 10820).

Shlichtings’ final observation states that around the transition period there is a sudden

increase in the boundary layer thickness [14]

; Hall [16]

later explained the shape of the

Venturi calibration curve by defining the discharge coefficient as being the ratio of the

“actual” flow area to the geometrical cross sectional area of the throat. Here he


22 – 25th

October 2013

18

postulated that the displacement thickness of the boundary layer behaves as a flow

reducer that diminishes in its effect with increasing Reynolds number.

Combining these two assumptions, the following attempts to describe the shape of

calibration curve for Meter B. As the flow regime becomes increasingly turbulent

through the throat, the velocity distribution becomes more and more uniform (flat),

decreasing the boundary layer thickness and increasing the “actual” flow area seen by

the flow. The reduced effect of the throat to restrict the flow results in an increase in

the static pressure at the throat tapping, causing the discharge coefficient to increase

with increasing ReT.

Around transition, the boundary layer thickens suddenly, as postulated by Shlichting,

causing the “actual” flow area to reduce, restricting the flow and decreasing the value

of CD. In Figure 16, it has been shown that between ReT = 3500 and 5000, values of

CD decrease in the experimental data, contributing to the “hump” in the calibration

curve. At some point thereafter, accompanied with a change in the friction resistance,

the velocity distribution becomes uniform in turbulent flow. Here the “actual” flow

area begins to closely approach that of the geometrical cross sectional area of the

throat, resulting in a recovery of CD to values close to unity, as seen in Figure 16.

Differential Pressure Devices – Quadrant Edge Orifice Plates

Meter C – 8” β = 0.45

Data points from the full range of viscosities form a well defined curve with a

reasonably tight overlap. The discharge coefficient increases with pipe Reynolds

number until a maximum “hump” is reached. The discharge coefficient then drops and

becomes linear as Reynolds numbers increase towards the turbulent regime.

The minimum discharge coefficient observed is 0.7554 at a pipe Reynolds number of

139. The maximum CD is 0.815 at a Reynolds number of 2193. After the “hump” (Re

~ 4000), discharge coefficient is relatively constant at approximately 0.802. The

“predicted” discharge coefficient is 0.789, which means that the experimentally

determined discharge coefficient has a 1.58 % discrepancy from this reference, which

is within the stated 2.00 % uncertainty of the equation. The linearity of the orifice

plate in this range is approximately 0.142 %.

It is important to note that the pipe Reynolds numbers are plotted on a logarithmic

scale, and so it can be seen that very sharp changes in discharge coefficient occur with

only small changes in Re in the laminar region. There are also very sharp changes in

the transitional region due to the “hump”.


22 – 25th

October 2013

19

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t

Pipe Reynolds Number

Meter C - 8" β = 0.45 Orifice Plate, Nov 2012

100 cSt

200 cSt

500 cSt

Predicted Cd

CD Uncertainty

Figure 19 Orifice Plate C – Discharge Coefficient vs. Reynolds number

Meter D – 8” β = 0.6

Data points from the full range of viscosities form a well defined curve with very tight

overlap. The discharge coefficient increases with Reynolds number until a maximum

“hump” is reached. The discharge coefficient then drops and becomes linear as

Reynolds numbers increase towards the turbulent region.

The minimum discharge coefficient observed is 0.7566 at a Reynolds number of 82.

The maximum is 0.8782 at a Reynolds number of 1832. After the “hump” (Re ~

4000), discharge coefficient is relatively constant at approximately 0.8373. The

“predicted” discharge coefficient is 0.844, which means that the experimentally

determined discharge coefficient has a 0.794 % discrepancy from this reference,

which is within the 2.00 % uncertainty of the equation. The linearity of the orifice

plate in this range is approximately 0.078 %.

Meter D features a greater beta ratio than Meter C, and it is known that a greater throat

bore increases the height of the discharge coefficient curve “hump” [21]

.


22 – 25th

October 2013

20

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t


Meter D - 8" β = 0.6 Orifice Plate, Nov 2012

100 cSt

200 cSt

500 cSt

750 cSt

Predicted CD

CD Uncertainty

Figure 20 Orifice Plate D – Discharge Coefficient vs. Reynolds number

The discharge coefficient curves for both orifice plates are displayed in Figure 21.

Meters C and D show the same general trend, but Meter D displays greater discharge

coefficients overall (maximum of 0.8782 compared to 0.8147) and at near turbulent

conditions (0.8373 compared to 0.8015). Meter D also displays a linear discharge

coefficient for 4000 ≤ Re ≤ 10000.

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

10 100 1000 10000 100000

Dis

ch

arg

e C

oe

ffic

ien

t


Comparison of Meter C (β = 0.45) and Meter D (β = 0.6)

100 cSt

200 cSt

500 cSt

750 cSt

Meter D

Meter C

Figure 21 Comparison of Quadrant Edge Orifice Plates

Ultrasonic Flowmeters

Meter E

Meter E was equipped with a 6-inch tube bundle with 10 diameters of straight pipe

upstream. The device was originally operated uncorrected. The volumetric flow errors

are plotted against reference flowrate in Figure 22.


22 – 25th

October 2013

21

The uncorrected results for Meter E display moderately high errors, with a maximum

of approximately 3%. Two distinct trends can be observed; errors are greater at higher

viscosities, and at lower flowrates.

The greatest errors occur for the 1500 cSt tests, and the minimum for the 200 cSt tests.

At low flowrates the errors range from 1–3%, and at higher flowrates they range from

approximately 1–2 %. As the flowrates increase, the influence of viscosity on errors

becomes less prominent. The greatest errors are still found in the 1500 cSt data series,

but the smallest errors are found for one of the 600 cSt series rather than the 200 cSt.

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 20 40 60 80 100 120

Vo

lum

e F

low

Err

or

[%]

Ref. Volume Flow [l/s]

Meter E - 6" Ultrasonic, Feb 2012

Volume Flow Error (uncorrected)

200 cSt

600 cSt

600 cSt (2)

1000 cSt

1500 cSt

Figure 22 Ultrasonic E – Vol. Flow Error vs. Vol. Flow

Errors from the uncorrected data form a clearer trend when plotted against Reynolds

number, as seen in Figure 23. The trends for viscosity and flowrate can be observed as

a Reynolds number effect. As Reynolds number decreases, error increases sharply.

Above Re ~ 2000, errors increase. It is likely that this sudden increase in error is due

to the transition region where the flow regime changes from laminar to turbulent.

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

10 100 1000 10000

Vo

lum

e F

low

Err

or

[%]



Volume Flow Error (uncorrected)

200 cSt

600 cSt

600 cSt (2)

1000 cSt

1500 cSt

Figure 23 Ultrasonic E – Vol. Flow Error vs. Reynolds number


22 – 25th

October 2013

22

A second set of test data was taken for Meter E, this time with a flowrate-based

correction applied to the meter. A different correction factor was used for each

viscosity and the resulting volumetric flow errors are plotted in Figure 24.

The corrected data shows significant improvement over uncorrected, the maximum

error is 1.209% and for most of the range, it is less than 0.5%. Unlike the uncorrected

data, the greatest errors occur at the higher flowrates, which is unusual for an

ultrasonic flowmeter. This is likely due to the fact that the correction applied was a

third order polynomial or greater and is under-compensating at high flowrates as a

result of the inflection point of the polynomial curve.

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 20 40 60 80 100 120

Vo

lum

e F

low

Err

or

[%]



Volume Flow Error (corrected)

600 cSt

1000 cSt

1500 cSt

Figure 24 Ultrasonic E – Vol. Flow Error vs. Vol. Flow (corrected)

The corrected data for meter E was plotted against Reynolds number in Figure 25. The

greatest errors occur at Reynolds numbers less than 100 and at approximately 1000.

The smallest errors occur for Reynolds numbers between 100 to 500.

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

10 100 1000 10000

Vo

lum

e F

low

Err

or

[%]



Volume Flow Error (corrected)

600 cSt

1000 cSt

1500 cSt

Figure 25 Ultrasonic E – Vol. Flow Error vs. Reynolds number (corrected)


22 – 25th

October 2013

23

Meter F

The vendor for Meter F decided the device should not be installed with a flow

conditioner. However, 30 diameters of straight pipe were in place immediately

upstream of the meter. The volumetric flow errors for Meter F are plotted against

reference flowrate in Figure 26. The meter was operated with no correction applied to

the device throughout the tests.

The meter shows spurious readings with lots of scatter for the 1000 cSt test condition.

This effect may be due to signal attenuation at the higher viscosity. The errors are

more negative at low flowrates and more positive at higher flowrates and range from -

8.35 to 4.5 % for this series.

Although the 1000 cSt readings are highly scattered, the greatest error for the 200 cSt

and 600 cSt tests is -1.335%. The majority of the 200 cSt and 600 cSt data points are

within ±1 % error. Similar to the 1000 cSt series, errors are more negative at low

flowrates and more positive at high flowrate for these two series, although the trend is

less pronounced.

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

0 20 40 60 80 100 120 140

Vo

lum

e F

low

Err

or

[%]


Meter F - 6" Ultrasonic, June 2012

Volumetric Flow Error

200 cSt

600 cSt

1000 cSt

Figure 26 Ultrasonic F – Vol. Flow Error vs. Vol. Flow

The error data for Meter F is plotted against Reynolds number in Figure 27 and shows

a tendency for Meter F to under-read at low Reynolds numbers. The 200 cSt data

appears to display a positive bias at Reynolds numbers greater than 2000.


22 – 25th

October 2013

24

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

10 100 1000 10000

Vo

lum

e F

low

Err

or

[%]


Meter F - 6" Ultrasonic, June 2012

Volumetric Flow Error

200 cSt

600 cSt

1000 cSt

Figure 27 Ultrasonic F – Vol. Flow Error vs. Reynolds number

Meter G

Meter G was equipped with a flow conditioner and 10 diameters of straight pipe

immediately upstream. It was originally operated uncorrected.

The volumetric flow errors for Meter G are plotted against reference flowrate in

Figure 28. Errors are relatively high, ranging from -2.14 to 2.1 %. There is little

correlation of error and viscosity, the highest errors occur for the 200 cSt and 600 cSt

(2)2 series, while the maximum error for the 1000 cSt trend is 0.71 %. There is also no

clear trend of errors with flowrate. There is little agreement in errors for the two 600

cSt data series, which suggests that reproducibility is poor for the meter in this case.

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

0 20 40 60 80 100 120 140

Vo

lum

e F

low

Err

or

[%]


Meter G - 6" Ultrasonic, Oct 2012Volume Flow Error (uncorrected)

100 cSt

200 cSt

600 cSt

600 cSt (2)

1000 cSt

Figure 28 Ultrasonic G – Vol. Flow Error vs. Vol. Flow

2 (2) corresponds to a repeat of the fluid temperature and viscosity for a different test

file.


22 – 25th

October 2013

25

The uncorrected data for Meter G is plotted against Reynolds number in Figure 29. No

clear trends can be observed with regards to viscosity. Individual series seem to

display their own trends with Reynolds number. As observed in Figure 28, the two

600 cSt trends show no agreement which suggests poor reproducibility at the test

conditions.

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

10 100 1000 10000 100000

Vo

lum

e F

low

Err

or

[%]


Meter G - 6" Ultrasonic, Oct 2012Volume Flow Error (uncorrected)

100 cSt

200 cSt

600 cSt

600 cSt (2)

1000 cSt

Figure 29 Ultrasonic G – Vol. Flow Error vs. Reynolds number

Corrections were applied to Meter G for Reynolds number effects and the data is

shown below in Figure 30. As before, a flow conditioner was installed upstream of the

device. Errors are notably lower with the corrections applied. They range from -1 to

1.1 % with most of the data falling within ±0.5 % error. There may be improved

reproducibility for the meter with the corrections applied, there are repeat data series

for 100 cSt and 200 cSt which show much closer agreement than the two 600 cSt

series featured in Figure 28.

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

0 20 40 60 80 100 120 140

Vo

lum

e F

low

Err

or

[%]


Meter G - 6" Ultrasonic, Oct 2012Volume Flow Error (corrected)

100 cSt

100 cSt (2)

100 cSt (3)

200 cSt

200 cSt (2)

200 cSt (3)

Figure 30 Ultrasonic G – Vol. Flow Error vs. Vol. Flow (corrected)


22 – 25th

October 2013

26

Figure 31 shows the corrected data with the flow conditioner (FC) plotted against

Reynolds number. The maximum errors observed are -0.969 % at Re = 9798 for 100

cSt and 1.136 % at Re = 1174 for 200 cSt. The overall performance is greatly

improved over the uncorrected data, the range of errors is lower and the data is less

scattered.

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

10 100 1000 10000 100000

Vo

lum

e F

low

Err

or

[%]


Meter G - 6" Ultrasonic, Oct 2012Volume Flow Error (corrected)

100 cSt

100 cSt (2)

100 cSt (3)

200 cSt

200 cSt (2)

200 cSt (3)

Figure 31 Ultrasonic G – Vol. Flow Error vs. Reynolds number (corrected)

Coriolis Flowmeters

Meter H

Meter H was originally tested with no correction applied. To ensure optimum

performance of the device, the meter was zeroed for each temperature and thus

viscosity.

The Mass flowrate error data for Meter H is plotted against reference flowrate in

Figure 32. Meter H shows errors of approximately -0.4% for most of the flowrates,

with a sharp increase to nearly -1% at low flowrates of less than 20 l/s.

There is no clear trend for viscosity differences, but this is limited by the presence of

only two sets of data series. The Coriolis under-reading at low Reynolds numbers has

been previously reported by NEL [18]

and others [7]

.


22 – 25th

October 2013

27

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 20 40 60 80 100

Ma

ss

Flo

w E

rro

r [%

]

Ref. Mass Flow [kg/s]

Meter H - 6" Coriolis, Feb 2012

Mass Flow Error (uncorrected)

600 cSt

1000 cSt

Figure 32 Coriolis H – Mass Flow Error vs. Mass Flow

The uncorrected data for Meter H was plotted against Reynolds number in Figure 33.

Most data points have an error between -0.25 and -0.5%, but errors become more

negative very sharply as Re decreases below 200. The maximum error of -0.96%

occurs at Re of approximately 90. There is close overlap of the two data series despite

the large difference in viscosity.

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

10 100 1000 10000

Ma

ss

Flo

w E

rro

r [%

]

Reynolds Number


Mass Flow Error (uncorrected)

600 cSt

1000 cSt

Figure 33 Coriolis H – Mass Flow Error vs. Reynolds number

A Reynolds-based correction was applied to Meter H. As before, the device was

zeroed for each temperature and thus viscosity change. The results are shown in

Figure 34. The maximum error decreased from -0.96% to -0.55% and for most of the

data, errors range from -0.25 to 0.25%.


22 – 25th

October 2013

28

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 20 40 60 80 100

Ma

ss

Flo

w E

rro

r [%

]



Mass Flow Error (corrected)

200 cSt

600 cSt

1000 cSt

1500 cSt

Figure 34 Coriolis H – Mass Flow Error vs. Mass Flow (corrected)

The corrected data for the Meter H mass flow errors has been plotted against Reynolds

number in Figure 35. The graph looks largely similar to that plotted against flowrate.

Most points have an error between -0.25 and 0.25%, with a maximum error of -0.55%

for the 200 cSt condition at Re of approximately 400.

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

10 100 1000 10000

Ma

ss

Flo

w E

rro

r [%

]

Reynolds Number


Mass Flow Error (corrected)

200 cSt

600 cSt

1000 cSt

1500 cSt

Figure 35 Coriolis H – Mass Flow Error vs. Reynolds number (corrected)

The density errors for Meter H are plotted against reference flowrate in Figure 36.

Meter H shows increasingly negative errors in density as flowrate increases. A

maximum error of - 0.177 % is observed at approximately 90 kg/s. There is close

overlap of the two data series despite the large difference in viscosity.


22 – 25th

October 2013

29

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80 100

De

ns

ity E

rro

r [%

]



Density Error (uncorrected)

600 cSt

1000 cSt

Figure 36 Coriolis H – Density Error vs. Mass Flow

As mentioned previously, a Reynolds based correction was applied to mass flowrate

for Meter H. No corrections were applied for density, but additional density data was

collected in this period. The “corrected” data for density error is shown in Figure 37.

The greatest error in this case was - 0.176 % at 80.7 kg/s. Figure 36 and Figure 37

show that the density measurements of Meter H are repeatable and insensitive to

viscosity changes.

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80 100

De

ns

ity E

rro

r [%

]



Density Error (corrected)

200 cSt

600 cSt

1000 cSt

1500 cSt

Figure 37 Coriolis H – Density Error vs. Mass Flow (corrected)

Meter I

The mass flow errors for Meter I are plotted against reference flowrate in Figure 38.

The greatest error occurs at low flowrates, approximately -0.4% for the 200 cSt test

condition, but for most flowrates and viscosities, errors are less than 0.25% in

magnitude. It can also be observed that at low flowrates, there is clear separation of

data series with different viscosities. This separation is not found as flowrates


22 – 25th

October 2013

30

increase, and the range of errors decreases from approximately -0.43 and -0.08% to

between -0.02 and 0.074%.

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 20 40 60 80 100 120

Ma

ss

Flo

w E

rro

r [%

]


Meter I - 6" Coriolis, June 2012

Mass Flow Error

200 cSt

600 cSt

1000 cSt

Figure 38 Coriolis I – Mass Flow Error vs. Mass Flow

The mass flow errors for the Meter I were plotted against Reynolds number in Figure

39. The magnitude of the errors decreases as the Reynolds number increases, and a

separation of data series based on viscosity differences is observed for the full range

of data. Similar to Figure 38, the greatest errors are observed for the 200 cSt data and

the smallest errors occur at the 1000 cSt data. The greatest error observed is -0.418%

at Re of 430 for the 200 cSt test condition.

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

10 100 1000 10000 100000

Ma

ss

Flo

w E

rro

r [%

]



Mass Flow Error

200 cSt

600 cSt

1000 cSt

Figure 39 Coriolis I – Mass Flow Error vs. Reynolds number

Density errors for Meter I are plotted against reference flowrate in Figure 40. Density

errors for Meter I increase as flowrates increase. The greatest errors are found in the

1000 cSt data series and the smallest errors are found for 200 cSt. The maximum error

of 0.454 % is observed at 101 kg/s.


22 – 25th

October 2013

31

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80 100 120

De

ns

ity E

rro

r [%

]



Density Error

200 cSt

600 cSt

1000 cSt

Figure 40 Coriolis I – Density Error vs. Mass Flow

6 SUMMARY AND CONCLUSIONS

A range of commercially available conventional liquid flowmeters have been operated

across a range of kinematic viscosities to investigate some of the technical issues

likely to be faced as the demand for accurate heavy oil flow measurement grows.

When assessing the suitability of a flowmeter for a particularly high viscosity

application, the results presented here show that it will be extremely important to

calibrate the device in similar conditions as it will encounter in service.

The response of the flowmeters evaluated in this test programme displayed a

significant dependence on the liquid viscosity / flow profile. The measurements

exhibited distinct trends for each measurement device with increasing fluid viscosity

at a given flowrate. The results also clearly demonstrated a relationship with the flow

profile. As the Reynolds number of the flow decreased, the response of the device

varied significantly.

The discharge coefficients of the Venturi tubes were strongly influenced by Reynolds

number. The slope of the discharge coefficient tended towards horizontal at high

Reynolds numbers. In the transitional and laminar regions, the CD appears to be

strongly dependent on Reynolds number. The performance of the Venturi tubes can be

characterised by Reynolds number for the full viscosity range. A steady discharge

coefficient of 0.995 would typically be expected for turbulent conditions. At Re =

3000 both Venturi tubes showed discharge coefficients of approximately 0.93,

representing a flow error of greater than 6.5 % relative to this reference. At Re = 1000,

flow error would be greater than 9.5%.

The discharge coefficients of the Quadrant edge orifice plates were strongly

influenced by the Reynolds number. A near constant discharge coefficient was

achieved for Re ≥ 4000 for both sizes. The performance of the orifice plates can be

characterised by Reynolds number for the full viscosity range. The “near constant”


22 – 25th

October 2013

32

experimentally determined discharge coefficients were compared to those calculated

from the equation provided in the Shell Flowmeter Engineering Handbook.

Discrepancies of 1.58 % and 0.794 % were observed for Meters C and D respectively,

which is within the stated 2 % uncertainty of the equation.

Ultrasonic flowmeters generally had high errors when operated uncorrected in the

transitional and laminar flow regions. Meter E had a maximum error of approximately

3% with a mean error of approximately 1.5%. Meter F produced spurious readings for

the 1000 cSt data set, with errors that were as large as -8%. Meter G had errors

ranging from -2.14 to 2.1 %. Corrections applied to Meter E and Meter G improved

performance considerably, the maximum error for Meter E decreased to 1.209% and

less than 0.5 % error for most of the range of data. Errors for Meter G with corrections

applied ranged from -1 to 1.1 % with most of the data falling within ±0.5 %. The

behaviour of Meter E changed greatly between the laminar and transition regions.

The Coriolis flowmeters had low errors and errors were typically less than 0.75%,

even at Reynolds numbers as low as 200. The Coriolis flowmeters seemed unaffected

by the laminar-turbulent transition although Meter H showed significant under-read at

low Reynolds numbers before the device’s correction was applied. The maximum

errors were -0.964 and -0.55% for meters H and I respectively. Meter I showed

separation of data series of differing viscosities even when plotted against Reynolds

number, Meter H did not. With corrections applied, the maximum error observed for

Coriolis meters was -0.56%, and the majority of the data was within 0.25% error.

Overall the results reported here reinforce the notion that conventional liquid

flowmeters cannot simply be relocated from low viscosity to high viscosity service

without suitable consideration, characterisation or modification. The results also show

that the performances of devices of the same technology (i.e. ultrasonic) are not

necessarily similar as there are many other variables.

7 FUTURE WORK

The test programme in this research project was focussed on testing conventional

liquid flowmeters with high viscosity fluids up to 1500 cSt. The next stage of this

project could be to research the effects of installation effects (restrictions, upstream

bends), temperature gradients within the flow stream under laminar conditions,

sensor-signal attenuation. Another area worth exploring is two phase oil & gas flow

using a high viscosity fluid. New advancements and the utilisation of multiple

technologies might enable accurate determination of the gas volume fraction. It might

then be possible to correct for the presence of a second phase within the flow stream.

A future paper by the authors will investigate the pressure drop of various metering

technologies when applied to viscous flow. The paper will include calculations for the

pressure drop cost for each technology and will also contain an uncertainty analysis

for each device to demonstrate the potential financial exposure of each metering

technology in viscous flow.


22 – 25th

October 2013

33

8 REFERENCES

[1] Miller, G. and Belshaw, R. “An investigation into the performance of Coriolis

and Ultrasonic Meter at Liquid Viscosities up to 300 cSt”, Paper 1.4, 26th

International North Sea Flow Measurement Workshop, 21 – 24 October 2008.

[2] Mills, C. and Belshaw, R. “Measurement of Flow in Viscous Fluids using a

Helical Blade Turbine”, Paper 16, 29th


Measurement Workshop, 25 – 28 October 2011.

[3] BS EN 5167-1:2003, Measurement of fluid flow by means of pressure

differential devices inserted in circular cross-section conduits running full -

Part 2: Orifice plates. London, BSI



Part 2: Orifice plates. London, BSI

[5] Danen, G. and W. A., Shell Flowmeter Engineering Handbook. New York,

McGraw Hill, 1985.

[6] Steven, R. “Significantly Improved Capabilities of DP Meter Diagnostics

Methodologies”, Paper 1.1, 27th

International North Sea Flow Measurement

Workshop, 20 – 23 October 2008

[7] Tschabold, P. Kumar, V. and Anklin, M. “Influence and Compensation of

Process Parameters on Coriolis Meters with a View to Custody Transfer of

Hydrocarbon Products”. Paper 6.3, 9th

South East Asia Hydrocarbon Flow

Measurement Workshop, 2 – 4 March 2010.

[8] Coriolis ISO reference BS EN 10790:1999, Measurement of fluid flow in

closed conduits -- Guidance to the selection, installation and use of Coriolis

meters (mass flow, density and volume flow measurements). London, BSI

[9] Ultrasonic ISO BS EN 12242:2012, Measurement of fluid flow in closed

conduits -- Ultrasonic transit-time meters for liquid. London, BSI



Part 2: Venturi tubes. London, BSI

[11] Mills, C. An Investigation into the effects of high viscosity fluids on

conventional liquid flowmeters: Venturi, NEL Report No: 2010/197, 2010.

[12] Stobie, G. Hart, R, Svedeman, S. and Zanker, K. “Erosion in a Venturi Meter

with Laminar and Turbulent Flow and Low Reynolds Number Discharge

Coefficient Measurements”, Paper 15, 25th


Measurement Workshop, 16 – 19 October 2007.

[13] Benedict, R. P., and J. S. Wyler. A Generalized Discharge Coefficient for

Differential Pressure Type Fluid Meters. ASME Journal of Engineering for

Power, Vol. 96, 1974.

[14] Shlichting, H. Boundary Layer Theory. New York: McGraw Hill, 1960.

[15] Reynolds, O. On the Dynamical theory of Incompressible Viscous Fluids and

the Determination of the Criterion, London: Phil. Trans. Roy. Soc, 1895.

[16] Hall, G.W. Application of Boundary Layer Theory to Explain Some Nozzle

and Venturi Flow Peculiarities, Proc.Inst.Mech.Eng, Vol. 173, No.36, 1959.

[17] Linford, A. Flow Measurement & Meters, Taylor & Francis, 1961.

[18] Mills, C. “Measurement of Flow in Viscous Fluids”. Oil Sands and Heavy Oil

Technologies Conference 2011, 19 – 21 July 2011.

fltough

Stamp

31st North Sea Flow Measurement Workshop 2013 Page 1 of 21

Qualification of Fiscal Liquid Ultrasonic Meter for

Operation on Extended Viscosity Range

Øyvind Nesse and Tore Bratten

Statoil

fltough

Stamp

Qualification of Fiscal Liquid Ultrasonic Meters for Operation on Extended Viscosity Range


Contents 1 Introduction .................................................................................................................................................... 3

2 Background .................................................................................................................................................... 3

3 Technology Qualification Program in Statoil .............................................................................................. 5

3.1 Introduction to Technology Readiness Level ................................................................................................... 7

3.2 Technology Readiness Level for ultrasonic liquid meter ................................................................................. 7

4 Technology Qualification Activities and Acceptance Criteria ................................................................... 7

5 Semantics ....................................................................................................................................................... 8

6 Test of 12’’ultrasonic 5 paths reduced bore meter................................................................................... 10

7 Test of 8 paths full bore meter ................................................................................................................... 15

8 Test of 6 path full bore meter ..................................................................................................................... 19

9 Summary ...................................................................................................................................................... 20



1 Introduction

Statoil is developing the Mariner and Bressay fields which are heavy oil discoveries in the UK sector of the North

Sea. The Mariner project includes one platform called the PDQ with process, drilling and living quarters and one

ship called the FSU with oil storage, oil export and diluent import. A jack-up drill rig will be temporary connected to

the PDQ. The oil production is dependent on continuous injection of a diluent - a light oil from other offshore fields

- in each production well. The diluent will be imported in batches from shuttle tankers to the permanently installed

floating storage units (FSU). Heavy crude oil shall be exported from the same storage unit. The diluent import and

the heavy crude export shall be fiscally metered by a common batch metering station. The metering station must

handle low viscosity diluent and various high viscosity diluted crude oil qualities. The resultant liquid flows will

represent turbulent flow, transition flow and laminar flow through the ultrasonic flow meters.

Figure 1.1 Mariner field development concept.

A Technical Qualification Program (TQP) for liquid multipath ultrasonic meters used on an extended Reynolds

number range was initiated by Statoil in 2012. Three different multipath ultrasonic liquid meters were tested in the

first and third quarter of 2013 at three ISO certified flow test facilities; SPSE in Marseille, Trapil in Paris, both in

France, and FMC Technologies Flow Research and Test Center, Erie PA, USA. The tests fluids ranged from 0,8

cSt to 350 cSt which correspond to the actual design limits for the Mariner metering application.

This paper will share the results and experience gained during testing and characterising of the ultrasonic crude oil

meters, as well as give a brief introduction to Statoil’s Technical Qualification Program.

2 Background

Fiscal batch metering of the export to shuttle tankers is a proven concept within Statoil and amongst several other

offshore operators. However, fiscal metering at the Mariner and Bressay storage units involves operational

procedures and challenges different from the commonly known and well-proven offshore batch metering operations.

The shuttle tankers have to be modified for the reversed transfer from the shuttle tanker bow via the loading hose to

the floating storage unit. The main challenge with regards to metering at the storage unit is the operating conditions

at the metering station. Unstable pressure and potential risk of flashing during the operation is a challenge which

has to be overcome by operational procedures.

During the Mariner Field development phase several metering principles and metering station concepts were

evaluated. Installing dedicated and separate metering stations for diluted crude oil and diluent represented a “worst

case” scenario, and was abandoned due to the space requirements, investment and maintenance costs. The

PDQ

FSU Shuttle

Gas import from Vesterled

Jack-up drill rig



concept chosen was a common fiscal/custody transfer batch metering system for diluted crude and diluent using

ultrasonic flow meters in combination with a bi-directional prover. The flow capacity of the metering system shall be

7000 m3/h for export diluted crude from storage tanks to Shuttle Tanker, and 8000 m3/h for import of diluent from

Shuttle Tanker. The Mariner metering design data are given in Table 2.1 and 2.2

Table 2.1 Mariner export fiscal metering design data per meter run.

Minimum Maximum Units

Requested Flow 300 1750 m3/h

Pressure 10 12 Barg

Temperature 40 50 °C

Density 930 985 kg/m3

Viscosity 200 350 cSt

Reynolds number (12’’ pipe)

1000 10 000

Table 2.2 Mariner import fiscal metering design data per meter run.

Minimum Maximum Units

Requested Flow 300 2000 m3/h

Pressure 4 5 Barg

Temperature 20 55 °C

Density 700 854 kg/m3

Viscosity 0.8 1,5 cSt

Reynolds number (12’’ pipe)

250 000 3000 000

Statoil has gained considerable experience using ultrasonic flow meters in fiscal liquid metering systems, but not on

any application similar to the ones at Mariner Floating Storage Unit (FSU). Furthermore, Statoil has with varying

success experienced the challenge of using ultrasonic meters with velocity profile correction in the transmission

area between laminar and turbulent flow. An example is from the New Oseberg Blend metering station where there

was a large deviation between the K-factors achieved under the calibration at TRAPIL and the first 1.5 years in

operation, see Fig. 2.1.

Figure 2.1 Ultrasonic liquid meter with velocity profile correction.

The calibration in Trapil was performed on a fluid with viscosity of 4.72 cSt, while at operational conditions the

viscosity is approximately 200 cSt. The K-factor achieved in TRAPIL was 5712 [pulses/m3]. The average K-factor

achieved in operation was 6040.8 [pulses/m3]. It is worth noting that if the K-factor achieved under the calibration

at Trapil calibration facility had been used during operation, a systematic error of approximately 5% would have

NOB - USM 1

Before adjusting path weights

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

sep. 02 mai. 05 feb. 08 nov. 10

Date

KJ

-fa

cto

r %

Dif

fere

nc

e f

rom

av

era

ge

K-factor Difference from

average

AVG + 2 St dev = +1.63%

AVG - 2 St dev =-1.63%

Profile correction on

Trapil calibration



been the result. Optimisations of the meters were then performed ref: “Experience with ultrasonic meters on high

viscosity oil” [2] presented by Øyvind Nesse at the North Sea Flow Measurement Workshop in Tønsberg 2007.

However, testing and optimisation in the field shall be minimised because of its inherent risk and cost. The fiscal

liquid meters for Mariner must go through a technology qualification program before installation. The ultrasonic

meters must be tested over an extended Reynolds number range and with different liquids reflecting the design

parameters in Tables 2.1-2.2.

3 Technology Qualification Program in Statoil

Statoil proven technology: Documented track record for a defined environment, operating window and fluid, with

respect to the ability of the technology to meet the specified requirements. In general, a technology shall have a

successful or acceptable track record from Statoil portfolio in order to be classified as proven.

New technology in Statoil is a technology that is not proven according to the definition above. This also implies

that the first application of proven technology in a new environment or an unproven technology in a known

environment, are both by definition new technology, which must go through a technology qualification program

before integration into intended operating system.

All technology elements within the new technical solution shall be classified according to a Technology

Assessment based on:

1. No new technical uncertainties

2. New technical uncertainties

3. New technical challenges

4. Demanding new technical challenges

This classification can be used for the totality of the application of the technical solution, as well as parts of it,

functionality and part systems. Establishment of classification category is based on degree of new technology and

application area, as shown in the following matrix:

Table 3.1 Classification categories

Application area Technology Maturity

Proven Limited field history New or unproven

Previous experience 1 2 3

No experience in Statoil 2 3 4

No industry experience 2 4 4

All new technologies in Statoil shall be approved according to this procedure.

Some ultrasonic flow meter manufacturers would claim their liquid flow meter is ready for accurate measurement

on an extended Reynolds number range. Statoil has yet to verify this statement. In other words, we need to test

ultrasonic liquid flow meter with increasing Reynolds number. Table 3.2 summarizes the Technology Assessment

for Ultrasonic liquid flow meters.



Table 3.2 Classification of ultrasonic liquid meter

Item

Application Area (AA) Technology Maturity (TM)

Cat

Remark

Prev

exp.

No exp.

Statoil

No

industr.

exp.

Proven Limited

history

New/

Unproven

Design

Manufacture

Installation

X X 1

AA: Ultrasonic liquid flow meters

are widely used in Statoil on oil

flow.

TM: There are several

manufacturers with decades of

experience. The uncertainty of

ultrasonic liquid meters is traceable

to accredited laboratories.

Extended

Reynolds

number

range

X X 3

AA: Ultrasonic liquid meters have

been used to measure oil flow over

a rather limited Reynolds number

range, and mainly in the turbulent

area. TM: Little independent data

available on the performance of

ultrasonic liquid meters applied

over an extended Reynolds

number range. Measurements in

the transition area are known to be

a challenge.

As can be seen from the assessment there is a technical challenge for ultrasonic liquid flow technology. That is to

identify measurement uncertainty and/or systematic error of the meters when applied across the transition area

from laminar flow to turbulent flow. This challenge will be addressed as main part of the technology qualification

program to ensure that the installation will lead to a proven technology.



3.1 Introduction to Technology Readiness Level

The Technology Readiness Level (TRL) of a technology shall be considered according to Table 3.3.

Table 3.3 Technology Readiness Level

3.2 Technology Readiness Level for ultrasonic liquid meter

Ultrasonic oil meters have been tested at full scale with low and high viscos oil and in laboratory environment by

third part, and Statoil has broad experience with use of these meters on limited Reynolds number applications.

These meters are thus regarded currently to be at TRL 3. Next section describes the required activities to be able

to move the ultrasonic liquid flow meter technology to TRL 5. First step is to test the meters at the intended

environment at a full scale laboratory based on Mariner design parameters (TRL 4) and thereafter install ultrasonic

meters at the import/export metering station at Mariner.

The next step after successful integration and test period at Mariner will be to develop sufficient operating

experience with the ultrasonic meters to reach TRL 6 and further TRL 7.

4 Technology Qualification Activities and Acceptance Criteria

This Chapter describes in general the activities and acceptance criteria found necessary to qualify ultrasonic liquid

meter for fiscal metering on an extended Reynolds number range. The meter must fulfil the general allowable

measurement uncertainty required to satisfy custody transfer liquid metering requirements. In the UK sector, by

consensus the total combined uncertainty is ±0.25% (dry mass) for liquid. On the Norwegian sector oil metering for

sale and allocation purposes has allowable expanded uncertainty of 0.30 % of standard volume. The acceptance

criteria chosen for the current TQP are linearity band 0,3 % and proving repeatability uncertainty of 0,027% .

Table 4.1 summarizes the general technology uncertainties, but also the required qualification activities including

the acceptance criteria, for qualifying ultrasonic liquid meter. Table 4.2 describes how these qualification activities

will lift the technology from TRL 3 and to TRL7. However, this paper covers TRL4.



Table 4.1 Required qualification activities and acceptance criteria for qualifying fiscal liquid ultrasonic meters for operation

on extended Reynolds number range.

No Technology Uncertainty Qualification Activity (QA) Acceptance Criteria (AC)

TRL 4

1 Key test parameters

Viscosity

Liquid flow rate

Density

Carry out flow tests of ultrasonic

meters at a recognized laboratory.

Test matrix shall be based on

Mariner design parameters.

Working range:

Linearity band 0,3 % (same band as defined

by NPD)

Repeatability uncertainty of 0,027%

o Maximum 20 repeats

2 Ultrasonic design Test ultrasonic meters from

minimum two different

manufacturers.

Verify the performance of the meters

TRL 5

3 Functionality test Factory Acceptance Test (FAT) of

metering control system.

Fulfil standard FAT accept criteria for metering control

system

4 Flow FAT each of ultrasonic flow meter at

accredited test facility

Fulfil DECC’s requirements to oil metering for fiscal

purposes.

TRL 6

5 Batch measurements Install 5 ultrasonic flow meters in

parallel. The import and export

metering station shall comprise ball

prover and redundant

instrumentation.

The meters shall perform within uncertainty

limits required by DECC.

Fulfil the historical limit of 0,3% band for the

last 30 accepted k-factors.

TRL 7:

6 Final evaluation After10% of lifetime an evaluation

of the ultrasonic meters will be

performed. The evaluation shall

consider individual proving results

and long term K-factor stability.

All the above acceptance criteria to be fulfilled

5 Semantics

One of the experiences made during testing of the ultrasonic meters is that the manufacturer and customer do not

speak the same “language”. Before presenting the results there is a need to explain the following concepts:

calibration, verification, characterisation, simulation and optimisation, see Table 5.1. In this paper we use the

concepts of the manufacturer.

Table 5.1 Concepts used by manufacturer and operator

Manufacturer Customer

Calibration Characterisation

Verification Calibration

Simulation Optimisation

Calibration: When the ultrasonic meter leaves the factory it is configured with specific weights per ultrasonic path.

At the calibration site the meter is corrected relative to the reference using a method called Velocity Profile

Correction (VPC). The correction factors are based off of velocities on the inner (A) and outer paths (B) of the



meter along with the bulk average velocity. Through calibration on multiple liquids a lookup table (A, B, Correction)

is generated for the ultrasonic meter’s intended operating process range. This process is frequently called

Reynolds number correction, but note that the flow profile at a given Reynolds number is not necessarily unique.

The lookup table is stored in the meter. The updated configuration is traceable through a belonging checksum

value.

To our knowledge there are no limitations to how much the meter can deviate from reference before application of

correction. This is in contrast to ultrasonic gas meters which in order to be accepted and considered to be of

acceptable quality the maximum deviation from the reference during flow calibration shall be less than ± 1,5 %

[Norsok I-104]. There are not any standard requirements to the manufacturer’s calibration procedure.

Factory setup

Flow measurement

A/B factors

(Velocity Profile Corrections)

Check sum

Laboratory reference

Figure 5.1 Calibration procedure

Verification: After the meter has been calibrated and the corrections factors have been implemented the meter

performance needs to be verified at an accredited laboratory. From the customers point of view this is the Factory

Acceptance Test (FAT) where the liquid ultrasonic meter shall be tested to verify the accuracy (Linearity) and

repeatability (uncertainty) requirements according to an agreed standard or guideline e.g. API MPMS ch. 5.8. The

FAT is documented by a calibration report and a “COFRAC Certificate of Calibration” showing that the calibration,

hence verification, was performed in compliance to the framework of the accreditation of the laboratory. The meter

is usually tested on a single fluid.

Figure 5.2 Verification procedure

Simulation: It’s all about the mathematics. Additional calibration may be used to improve previous verification. By

using new and old calibration data the manufacturer can optimise path weights and velocity profile corrections to

minimise the deviation between corrected flow measurement and reference. This results in a new configuration of

the meter, hence new checksum.

During verification the manufacturer may propose to further improve the correction factors by simulation to

enhance the performance of the meter. In other words the manufacturer continues his calibration during

verification. The FAT program normally has a tight schedule and there are limited possibilities to do extra flow

tests. The customer may then face a dilemma. Should he accept an improved meter where some flow rates are

not repeated with the new checksum, hence the calibration certificate will cover two different setups, or should he

Checksum

Flow measurementDeviation

(API requirements)Check sum

Laboratory reference

Certificate



only accept a meter which has gone through a strict verification with fixed checksum throughout the

FAT/verification? Is it acceptable that the manufacturer tampers with the meter during verification? We

recommend simulation only if the meter shall be installed in a system with prover.

Check sum

NewCalibration/verification

New path weights

New checksum

NewA/B factors

Oldcalibration

Simulated verification

Figure 5.3 Simulation procedure

6 Test of 12’’ultrasonic 5 paths reduced bore meter

The first test was carried out at SPSE test site in Fos sur mer in France, where the Krohne Altosonic V flow meter

was calibrated and verified against an uni-directional prover station with a volume of approximately 15m3 between

detector switches. The piping and prover in the test loop had no heat insulation and weather protection. The test

was carried out in early February, week 7, in order to minimize the temperature instability which may cause

problems obtaining good repeatability and reliable results, particularly for high vicious oil.

The project meter run installed was the following configuration:

Inlet with ISO Tube bundle Flow Conditioner 12”, overall length 10D, 150 lbs RF flanges / Flow meter 12”, 150lbs

RF flanges / SPSE system outlet, length min. 5D, 12”, 150lbs RF, see Fig. 6.1.

Figure 6.1 Meter run with Krohne Altosonic V installed at SPSE. Tube bundle and 10D spool upstream, and 5 D

spool downstream flow meter.

A test plan was set up for week 7 in 2013 by the manufacturer and SPSE. The flow meter was going to be

calibrated and verified with the products in Table 6.1. The preferred test points are: 2500 m3/hr – 1925 m3/hr –

1350 m3/hr – 775 m3/hr and 200 m3/hr (200 m³/hr is the minimum possible test flow rate at SPSE prover loop).

Table 6.1 Oil products planned for the first test at SPSE

Product Viscosity (cSt)

at 20 oC and Patm

Density (kg/m3)

at 15oC and Patm

Nafta 1.3 750

Oural 5,5 800

Arabian heavy 25 850

Heavy fuel2 350 929

The 3 first days were intended for the manufacturer’s standard calibration tests and then the two last days were

intended for verification tests. Due to delivery of incorrect heavy oil quality (the viscosity was 450 cSt and the oil

minimum pour point was far above the current temperature of 20 oC) to SPSE test site at the beginning of week 7,

the complete test schedule had to be shifted and it became impossible to complete within the 5 days available at

SPSE. The incorrect heavy oil was replaced with heavy fuel with viscosity of about 200 cSt. At the end of week 7

successful calibration tests on 4 fluids had been accomplished and a preliminary correction curve and a check

sum were established. A final calibration test on heavy oil, about 350 cSt, was done early week 8.



The heavy oil calibration in week 8 resulted in modified calibration factors (in low end of Re-scale) and a new

check sum. A verification test on this heavy oil was done.

Temperature stability is always a challenge during calibration and verification. The viscosity of the heaviest oil

depends strongly on the temperature. Fig. 6.2 shows this relationship for heavy fuel2.

Figure 6.2 Relationship between viscosity and temperature for Heavy fuel2.

The first calibration with original factory setup is shown in Fig. 6.3. Each calibration point is a 3 points calibration

(three repeats) with no requirement to repeatability. The Reynolds number range is from 630 Re to 2.2 MRe, and

deviation span 2.3% relative reference, the SPSE prover. There is overlap between the calibrations on different

liquids for Re > 10 000. This is in agreement with a common understanding that meter performance at a given

Reynolds number is the same no matter the combination of flow, viscosity, and meter size. However, this may not

be the case in the transition area where the flow changes continuously between laminar and turbulent flow. In the

lower Re range, < 10 000 Re, there is a significant deviation between the calibrations, see Fig. 6.3.

Figure 6.3 First SPSE calibration with original weighting factors.

Verification based on the first calibration is shown in Fig. 6.4. The measurements are within the NPD linearity

requirement of 0,3% band. Measurements with different liquids overlap at the same Reynolds number except the

heavy fuel with viscosity of 290 cSt. The manufacturer suggested improving the meter performance by using all

calibration data and introducing new weighting factors to generate a simulated calibration curve, see Fig. 6.5. In

this case measurements with different liquids overlap at the same Reynolds number. Compared to the first



calibration curve the simulated one takes an opposite shape at Re below 10 000 Re and ending with a correction

of -1,25% at Re equal to 600.

Figure 6.4 Verification with original configuration of the meter, i.e. with the first checksum.

Figure 6.5 Simulated SPSE calibration (correction curve) established by use of modified weighting factors.

A simulated verification of all SPSE data is generated by applying the correction curve in Figure 6.5. The data are

processed through Krohne calibration tool and corrected. The resulting simulated verification is shown in Figure

6.6. The corrected data are within the NPD requirements of +/- 0,15%. Despite a significant improvement of the

correction curve in Fig. 6.5, with Reynolds number overlap, the new weighting factors and correction curve did not

improve the simulated corrected data. The Reynolds number non overlap between different liquids have been

moved from low Re area to about Re 8000. The maximum error span between the different liquids is about of

0,2%.



Figure 6.6 Simulated verification based on new weighting factors.

The results so far are within the acceptance criteria for this TQP. However, measurements at lower Re are a

challenge and the non-overlap between different liquids called for further testing. A verification with the simulated

correction curve, hence the new checksum, test took place at Trapil, France, in week 35 2013. The calibration

setup and prover volume are equal to the oils used are listed in Table 6.2. The temperature range was 21-31 oC

which resulted in a broad viscosity range per liquid.

All consecutive verification measurements are shown in Fig. 6.7 and are not sorted as a function of increasing Re.

The meter does not fulfil the linearity requirements. It is below the limit at low Re and above at high Re. The result

does not resemble the verification obtained at SPSE.

A new calibration, hence third checksum, was established at Trapil. Calibration curve is not shown, but the

consectutive verification points are shown in Fig. 6.8. There is a single outlier at low Re for the heavy fuel. No

plausible explanation has been found. At higher Re, measurements with gasoline, the meter factor increases

gradually and it continues outside the NPD linearity limits. This is also seen in Fig. 6.9 where each data point is a

result of accepted proving with minimum 5 repeats. Further investigation revealed coating particles in the oil. The

contamination stems from the new coating of the pipeline at Trapil. The coating obviously did not tolerate low

viscous oil. Picture of the tube bundle with coating particles seen at the bottom of each tube is shown in Fig. 6.10.

In week 38 Trapil had installed a filter to filter out the paint particles that were found in week 35. When Krohne

checked what the filter contained after 2 hours of filtering, it was found that there was hardly any residue. This was

mainly due to the fact that the filter had a mesh of approximately 6-7 mm. So the functionality of the filter turned

out negligible. The amount of particles found in the pipe in week 38 was less because the liquid was exchanged,

but estimated at 50% less than in week 35. Krohne will continue the research on the meter.

Table 6.2 Oil products used at Trapil

Oil Viscosity (cSt)

Heavy fuel 151-247

Heavy crude 34-52

Domestic 3.5-4.4

Gasoline 0.7-0.8



Figure 6.7 Consecutive verification measurements in Trapil with the second checksum, i.e. the correction

curve in Fig. 6.5.

Figure 6.8 Consecutive verification measurements with a third checksum established at Trapil.

Figure 6.9 Verification in Trapil with third checksum



Figure 6.10 ISO tube bundle with coating particles.

7 Test of 8 paths full bore meter

The second 12’’ meter to go through the technology qualification program is the Smith Meter Ultra8 Liquid

Flowmeter from FMC. The meter has 8 ultrasonic paths as shown in Fig. 7.1. The test setup is identical to the 5

paths meter from Krohne with 10D spool upstream and 5D spool downstream the meter. However, in this case the

ISO tube bundle has been replaced by a high performance flow conditioner shown in Fig.7.2

Figure 7.1 8 paths full bore meter.



Figure 7.2 High performance flow conditioner.

The flow tests were carried out at FMC Technologies Flow Research and Test Center, Erie, Pa, USA. The

calibration laboratory received ISO/IEC 17025:2005 certification August 2012. Traditional unidirectional ball prover

is not used. Instead the reference is a master meter prover. Its uncertainty is traceable to an unidirectional fixed

piston displacement prover. The latter is calibrated by onsite gravimetric calibration facility. The number of pulses

from the master meter per proving run corresponded to a prover volume of 14.1 m3 (89 bbl). According to earlier

version of API MPMS Ch. 5.8 this should give 0,027%repeatability from 10 runs for an ultrasonic meter, see Table

7.1. Each flow rate was verified according to the acceptance criteria in Table 4.1.

Table 7.1 Prover volume for USMs, with 0,027% uncertainty from repeatability. Table B-2 from API MPMS Ch. 5.8.

A calibration curve, hence a correction lookup table, was established prior the testing witnessed by Statoil. The

calibration curve, shown in Fig. 7.3, has a non-overlap between different liquids at low Re area from 5000-15000.

The manufacturer did not respond to this as nonconformity because the meter correction is based off of velocities

on the inner and outer paths of the meter along with the bulk average velocity. The non-overlap in Fig. 7.3 may

then demonstrate that the velocity profile at a given RE is not unique. The velocity profile correction for the meter

in question is shown in Fig. 7.4. This correction curve is based on the same calibration data. The non-overlaps

between the liquids are not near as apparent and the “gaps” are minimized. The fine black line is a 3rd

order

polynomial fit curve.



Figure 7.3 Erie calibration of 8 path meter with original weighting factors.

Figure 7.4 Velocity profile correction (VPC) for the 8 paths meter.

Verification of the 8 paths meter was carried out using four different liquids using four liquids with the following

viscosity: 2, 11, 35, and 225 cSt. A functional test was performed at a viscosity of 350 cSt. But due to limited

chilling capacities, and hence temperature instability at the higher viscosities, the calibration and the following

verification with the 350 cSt oil did not fulfil the linearity requirement. A new chiller system is currently being

installed which may provide the opportunity to test to uncertainty at higher viscosities.

The verification in terms of accepted provings and in the sequence the tests were done are shown in Fig. 7.5. At

11 cSt, the second liquid used in the verification, the meter span the whole linearity band. The results was

unexpected because the measurements with the 11 cSt liquid covers a Re range 30 000-300 000 which is

supposed to not be a challenge. The results with the three other liquids are well within the required linearity limits.



Figure 7.5 Consecutive verification measurements of 8 path meter

Verification of the 8 path meter as function of flow rate and Re are shown in Fig. 7.6 and 7.7 respectively. With respect to

Re the verification extends the calibration range. In this case the extrapolation of the VPC was successful. It is not clear if

it is the black 3rd

order polynomial fit or the red line in Fig. 7.4 that is used at the higher Re.

Figure 7.6 Verification of 8 path meter as function of flow rate



Figure 7.7 Verification of 8 path meter as function of Re.

Verification with the liquids 222 and 11 cSt were repeated with a transducer pair disconnected. Path 3 being close to

centre of the pipe was chosen. These measurements are just for demonstration and are not subject to the acceptance

criteria in Table 4.1. The meter performance is mainly maintained and follows the trends of the verification. A maximum

change of about 0,1% is found at Re=31 000.

Figure 7.7 Verification of 8 path meter and demonstration of transducer failure, path 3 disconnected.

8 Test of 6 path full bore meter

The Smith Meter Ultra6 Liquid Flowmeter is identical to Ultra8 except that path 6 and 7 are not used, see Fig. 7.1

for path configuration. For this qualification program Ultra6 uses the same calibration (velocity profile correction

lookup table) as established for Ultra8, see Fig. 7.4. As expected the verification of Ultra6 follows the main

characteristics Ultra8, but Ultra6 is outside the NPD linearity limits and thus failed the qualification. Fig. 8.1 shows

Ultra6 and Ultra8 verifications. The results suggest the Ultra6 does not control measurements in the transition

area between laminar and turbulent flow in a linear way. Below 5000 Re the meter factor drops significantly.

However, the meter has acceptable performance at higher Re and is there within the NPD linearity band of 0,3%,



see Fig. 8.2. Above 5000 Re Ultra6 also has a Re overlap between the different liquids, but the overlaps are not

as close as the ones found for Ultra8.

Figure 8.1 Consecutive verification measurements of Ultra6 and Ultra8 liquid meters.

Figure 8.2 Verification of 6 path meter

9 Summary

To carry out a technical qualification program like this one done for Mariner and Bressay project involves access

to one or several flow test facilities in order to cover predefined test conditions. There is no known accredited test

site which can run continuous and stable testing at the maximum viscosity and flow rates we needed. A common

flow test would be ideal so the different flow meters could be tested under exact same conditions. An ultrasonic

meter intended for a wide Reynolds range, should be calibrated and verified on more than one liquid and on more

than the regular 5 or 6 test flow rates normally required by a customer.

Using a multipath liquid ultrasonic meter in the transient area is possible, but extensive calibration (many test

points) is required in order to fit the flow meters for the operating Reynolds ranges. In this project two 12’’

ultrasonic liquid for use on extended viscosity range are qualified for technology readiness level 5. A third tested

meter did not fulfil the linearity requirement of 0.3% band over the whole operating range.



The results from this technology qualification program show that a common liquid metering station for heavy crude

oil and diluent on Mariner FSU and Bressay FSU is a feasible metering concept.

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22 – 25 October 2013

1

A new measurement program for VOC emission during

offshore oil tanking of shuttle tankers

Kjell-Eivind Frøysa and

Stian H. Stavland,

Christian Michelsen Research AS, Bergen, Norway

ABSTRACT

When shuttle tankers are loaded with oil from offshore installations, the atmosphere

originally present in the empty cargo tanks will be evacuated as the oil is filled. The

evacuated gas contains VOC-gas originally in the tanks prior to filling and VOC-gas

that is evaporated from the loaded oil. A considerable effort has been made in

reducing the possibility for such evacuated VOC-gas to be emitted to atmosphere.

This has resulted in several technologies implemented for VOC treatments on the

ships. Such active VOC treatment technologies include condensation and separation

in order to retrieve the VOC-gas, and passive systems for reducing the amount of

VOC-evaporation from the filled oil (KVOC). In the Norwegian sector about 20 ships

are used. Each ship is equipped with a VOC treatment system. The selected system

varies from ship to ship.

In order to control the overall VOC emissions during offshore loading activities,

Norwegian authorities have required that a measurement and calculation program is

implemented for VOC emissions from shuttle tankers. The Norwegian industry

cooperation VOCIC is responsible for this implementation, on behalf of the oil field

operators. The program involves installation of metering systems on a representative

selection of ships covering the various VOC treatment technologies and also covering

the main oil fields. The present work describes the background, the challenges and

the selected measurement program that is implemented on the selected ships. It will

also discuss the implementation of the flow meters and quality measurements

including some operational challenges.

1 INTRODUCTION

The emission of volatile organic compounds (VOC) is regulated by the Gothenburg

Protocol. In addition, the Gothenburg Protocol also regulates a series of other

emissions. The Protocol sets emission ceilings for 2010 for four pollutants: sulphur,

NOx, VOCs and ammonia. According to this protocol, Europe’s VOC emissions

should be cut by 40% compared to 1990.

The Protocol was amended in 2012 to include national emission reduction

commitments to be achieved in 2020 and beyond. For Norway, the new emission

commitments for NMVOC (Non-Methane VOC) are a 40 % emission reduction

compared to the level in 2005.

In 1990, the Norwegian emissions of NMVOC were according to Statistics Norway

(Statistisk sentralbyrå) 292 600 tons, increasing from 173 800 tons in 1980. In 2000,

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22 – 25 October 2013

2

the emissions had increased further to 380 200 tons. After 2000, the emissions have

been reduced significantly, in 2012 down to 132 000 tons, which means a reduction of

54.8 % compared to the 1990 emission data.

Out of the 132 000 tons NMVOC emission in 2012, about 30 000 tons were related to

the oil and gas production. Emissions from shuttle tankers are a significant part of this

emission.

In Section 2 of this paper, the background with respect to VOC emissions from shuttle

tankers is given, including VOC handling equipment at the tankers, and the

requirements with respect to emission limits and measurements.

Section 3 outlines a new measurement and calculation program with respect to

NMVOC emissions from shuttle tankers, and section 4 presents operational

experiences with respect to this new program.

2 BACKGROUND

When a shuttle tanker is off-loaded on an on-shore terminal, the oil is replaced by

inert gas (exhaust gas), in order to prevent the possible safety risk of the appearance

of air (oxygen) in the tanks. This means that the oil tanks consists of a mixture of inert

gas and remaining VOC gas from the previous deliverance, when the ship heads

towards an off-shore installation with empty tanks for collection of a new load of oil.

During loading of the shuttle tanker at the offshore installation, the gas originally in

the tank (a mixture of inert gas and VOC gas) is evacuated as the oil is loaded. In

addition, more VOC gas is generated during the loading process, from flashing due to

pressure reduction as the oil is flowing into the shuttle tanker and due to evaporation

from the oil on the tanks. VOC gas originating from of all of these three sources (from

previous load, flashing and evaporation), represents a potential emission of VOC gas

to the atmosphere.

This means that the gas that is emitted is a mixture of inert gas (exhaust) and VOC

gas. The inert gas consists typically of 80 % or more of nitrogen (molar fraction).

Most of the remaining part of the gas is carbon dioxide. There are also small amounts

of oxygen and carbon monoxide. The molar mass of the inert gas is typically close to

30 g/mole. The major component of the VOC gas is propane. There are also much

ethane and butane, in addition to smaller amounts (some per cents) of methane and

C5+. The molar mass of the VOC gas is typically around 45 – 50 g/mol. In some

cases it can be higher or lower.

In order to reduce VOC emissions from shuttle tankers during loading, Norwegian

authorities have required that the shuttle tankers have VOC treatment equipment for

reduction of emissions. This has led to a significant reduction in the Norwegian VOC

emissions from year 2000 when the emission was at maximum. On the industrial side,

a Norwegian VOC Industry Cooperation (VOCIC) is established in order to handle

the VOC issues on behalf of oil companies with interests in Norwegian oil fields.


22 – 25 October 2013

3

Currently most of the shuttle tankers have either an active condensation system or a

passive KVOC system.

In the active condensation system, the VOC gas goes through a separator where

hydrocarbons are condensed to condensate, and the remaining gas goes back into the

tanks. When the condensation system is in function, there will be no emission of VOC

gas to the atmosphere. Such a system is commonly denoted as VOC recovery unit

(VRU). However, such a VOC system is expensive in CAPEX and OPEX. In

addition, the condensate that is regenerated is not necessarily easy to deliver to

onshore plants.

In the passive KVOC system, the assumption is that most of the emitted VOC comes

from the flashing process (with gas release from oil) in the flowing oil during loading.

The inlet design is established so that the flashing should be minimized. This can also

be combined with increasing the tank pressure from around 1.1 bara to maybe 1.6

bara in order to reduce the amount of evaporation of VOC gas from the oil in the

storage tanks.

In addition to these VOC treatment methods, there are ships with VOC plants based

on absorption or adsorption.

From 2012, the Norwegian authorities changed the requirement from a specific

requirement on VOC treatment equipment to a quantification of the VOC emission.

This new requirement specified that the maximum emission of NMVOC should not

be larger than 0.45 kg per standard cubic meter of loaded oil. The requirement is

given per oil field. However, the agreed interpretation of this requirement is that if

emission is 0.45 kg/Sm³ or lower as a total number for the whole Norwegian Sector

over one year, the requirement is met. This means that for some ships or for some

fields, the emissions may be higher as long as the emission limit is obtained in total.

As a consequence of the new requirements, the Norwegian pollution authorities also

required that VOCIC should initiate a new measurement and calculation program for

NMVOC emissions from loading of oil on shuttle tankers in the Norwegian sector.

This program shall establish the emissions from each emission point with sufficient

accuracy and compare with the emission limit. It is not specified what sufficient

accuracy (or sufficiently low uncertainty) means. In the work of developing the

program, a sufficiently low uncertainty for the total annual emission for Norwegian

sector as a whole is assumed to be about 10 - 20 % (relative expanded uncertainty

with 95 % confidence level).

3 MEASUREMENT AND CALCULATION PROGRAM

3.1 Flow meter

Prior to the development of a new measurement and calculation program for NMVOC

emissions, selected ships carried out emission estimated based on a mass balance

principle. There were several assumptions related to the mass balance estimate, and

the uncertainty was expected to be higher than what could be accepted as sufficient.

Therefore, in this work alternative methods were searched for. Focus in the


22 – 25 October 2013

4

development of the measurement and calculation program was therefore to measure

the emission directly (i.e. the outflow to the atmosphere) instead of a more indirect

mass balance method.

The flow conditions at the measurement point are typically as follows:

Pressure: 1 - 2 bara

Temperature: 0 - 40 °C

Molar mass of flowing gas: 30 - 50 g/mol

Flow velocity: 1 - 30 m/s

Some wetness (unspecified) of the gas can be expected

As a part of the mass balance method, ultrasonic flare gas flow meters for

measurement of the gas vented to atmosphere already were installed on selected ships,

and operational experience with this type of equipment was already gained. After a

short evaluation, it was decided to continue with this type of flow meters.

3.2 Gas composition equations

The gas flowing through the flow meter will be a mixture of inert gas and VOC gas,

and will contain the following components:

Inert gas:

Nitrogen, molar mass mN2, molar fraction N2

Carbon dioxide, molar mass mCO2, molar fraction CO2

Carbon monoxide, molar mass mCO, molar fraction CO

Oxygen, molar mass mO2, molar fraction O2

VOC-gas:

Methane, molar mass mC1, molar fraction C1

Ethane, molar mass mC2, molar fraction C2

Propane, molar mass mC3, molar fraction C3

Butane (both isomers), molar mass mC4, molar fraction C4

Pentane (all isomers), molar mass mC5, molar fraction C5

Hexanes, molar mass mC6, molar fraction C6

Higher alcanes, molar mass mC7, etc molar fraction C7, etc

Here, the sum of all molar fractions is one:

11

222

n

i

CiOCOCON (1)

The total molar fraction of inert gas will be

222 OCOCONinert (2)

Similarly, the molar fraction of VOC ( VOC ) and NMVOC ( NMVOC ) will be

n

i

CiVOC

1

(3)


22 – 25 October 2013

5

and

n

i

CiNMVOC

2

(4)

The molar mass of inert gas ( inertm ), VOC gas ( VOCm ) and NMVOC ( NMVOCm ) gas are

given as

222

222222

OCOCON

OOCOCOCOCONNinert

mmmmm

(5)

n

i

Ci

n

i

CiCi

VOC

m

m

1

1

(6)

n

i

Ci

n

i

CiCi

NMVOC

m

m

2

2

(7)

The molar fractions of the various gas components will change over a loading.

Typically, the four molar fractions related to the inert gas (nitrogen, carbon dioxide,

carbon monoxide and oxygen) will be reduced and the molar fractions related to the

VOC gas (methane, ethane, etc) will be increased, due to VOC generation from

flashing and evaporation, during oil loading. This means that both the molar mass and

the molar fraction of inert gas and of VOC and NMVOC gas can change over the

loading period.

3.3 VOC mass flow rate

3.3.1 General

When knowing the flow velocity (v) and the gas composition, in addition to line

pressure (P), line absolute temperature (T) and inner diameter (d) of the pipe, the mass

flow rate of NMVOC ( NMVOC

mq ) can be found as follows:

n

i

CiCi

NMVOC

m mRT

Pdvq

2

2

4

(8)

In principle, the NMVOC emission could therefore be found if an online gas

chromatograph was installed. However, this has been evaluated to be a costly solution

(both CAPEX and OPEX). Therefore, more simplified solutions have been

considered. In such a more simplified solution, it was decided first to measure the

total VOC emission (including methane), and thereafter subtract methane from the


22 – 25 October 2013

6

final results based on previous knowledge on how large percentage of the total VOC

emission that is methane.

The mass flow rate of VOC (including methane) ( VOC

mq ) can be found as follows:

n

i

CiCi

VOC

m mRT

Pdvq

1

2

4

(9)

By using Eqs. (3) and (6), the mass flow rate of VOC can be written as follows:

VOCVOC

VOC

m mRT

Pdvq

4

2

(10)

The challenge is then to establish the product of the molar fraction and the molar mass

of VOC. In the evaluation, two different approaches were considered. These are based

on (i) density measurement and (ii) measurement of the velocity of sound. Both these

measurements are gas quality measurements and it is therefore possible to extract

some information on the gas composition/gas quality from them.

3.3.2 Density to determine VOC fraction

If the density ( ) is measured, the molar mass (m) of the gas can be found as follows:

d

d

P

RTm

(11)

Subscript d for the pressure and temperature means here that the pressure and

temperature is measured at the place where the density measurement is carried out. It

should here be noted that within the uncertainty limits for this work, it is sufficient to

use the ideal gas equation of state, instead of a more complex equation of state like

e.g. the AGA 8 equation.

From Eqs. (1), (2), (3), (5) and (6), the molar mass of the measured gas can be written

as follows:

inertVOCVOCVOC mmm 1 (12)

This equation can be turned into

VOCinert

inertVOCVOC

mm

mmm

1 (13)

In sum, this means that the VOC mass flowrate can be written as follows:

VOCinert

inertP

RT

VOC

mmm

m

RT

Pdvq d

d

14

2

(14)


22 – 25 October 2013

7

In order to apply this method, where flow velocity and density is measured, in

addition to pressure, temperature and inner diameter, the molar mass of the VOC gas

and the inert gas have to be quantified.

3.3.3 Velocity of sound to determine VOC fraction

As stated above, it has been decided to use ultrasonic flare gas meters for the flow

measurement. Such flow meters also measure the velocity of sound of the gas. In an

ordinary flare gas installation, it is usual to use this velocity of sound measurement in

combination with pressure and temperature measurements to estimate the density of

the flare gas. This is done using a vendor specific algorithm for calculation of the

density from the velocity of sound.

The principle of using the measured velocity of sound can also be applied here, for

estimation of the molar fraction of VOC gas needed as input in Eq. (9). However, the

algorithms implemented in the flare gas meters for calculation of the density cannot

be used in the application presented here. The flare gas density algorithm is based on

an assumption that the flare gas mostly consists of hydrocarbon gas. In addition, there

may be either small amounts or a predefined amount of nitrogen and carbon dioxide

in the flare gas in order to apply these vendor specific algorithms. Here, the

relationship between the velocity of sound and the density is different, due to a

changing content of inert gas in the gas mixture.

This is illustrated in Figure 1, where the density is plotted as a function of the velocity

of sound for an ethane-propane-mixture, with propane molar fractions from 0 % to

100 % (blue curve). The calculations are carried out under a pressure of 1 bara and a

temperature of 20 °C. This blue curve is typically how the flarge gas meters estimate

density based on the velocity of sound. The red curve is the density as a function of

velocity of sound for a mixture of propane and inert gas (87.5 % nitrogen and 12.5 %

carbon dioxide), with propane molar fractions from 0 % to 100 %. These curves (red

and blue) do not overlap except for at the left end, where both curves represent 100 %

propane. This lack of overlap indicates that the standard density estimation from

velocity of sound that is implemented in the flare gas meters cannot be used for the

application in question here.

In order to deal with this, a velocity of sound to density relationship valid for inert gas

– VOC mixtures (0-100 % of VOC gas in the mixture), where the molar mass of the

inert gas and the VOC gas are known, is established. This is used to estimate the

molar fraction of the VOC gas for input into Eq. (9).

In order to apply this method, where flow velocity and velocity of sound is measured,

in addition to pressure, temperature and inner diameter, the molar mass of the VOC

gas and the inert gas have to be quantified. This means that this method and the

method where density is measured basically have the same assumptions.


22 – 25 October 2013

8

Figure 1 Density as a function of velocity of sound for hydrocarbon mixtures (ethan-

propane mixture), blue curve, and a mixture of propane and inert gas, red

curve. Pressure of 1 bara and temperature of 20 °C.

3.4 Uncertainty related to the methods in question

Three methods for measurement of the VOC mass flow rate have been outlined

above. They are all based on an ultrasonic flow meter, in addition to pressure and

temperature measurements. In addition, measurements are needed for determining

how much of the flow that is VOC gas. The rest will be inert gas. The three methods

provides this information based on

Alternative 1: gas chromatography

Alternative 2: density measurement

Alternative 3: velocity of sound measurement

As part of the evaluation process between the alternatives, an uncertainty analysis was

carried out for each alternative.

The basis for the uncertainty analysis was the synthetic, but realistic example of a

mixture of a VOC gas with molar mass close to 45 g/mole and an inert gas with molar

mass close to 30 g/mole. Pressure was selected to 1.3 bara and temperature to 20 °C,

which also are realistic values. The details of the selected values are given in Table 1.

The uncertainty of each variable where uncertainty is needed as input to the

uncertainty analysis, is also given.

For the gas components, a relative expanded uncertainty of 3 % for each component

was selected. There is no detailed uncertainty analysis behind this number. Instead it

is chosen based on an expected level of how large the uncertainty will be.

The molar mass of VOC gas is an input when density or velocity of sound is used. In

a blind situation (where no specific information on the VOC molar mass

representative for the field in question is present), the uncertainty will be quite high. It


22 – 25 October 2013

9

is here set to 10 % (relative expanded uncertainty with 95 % confidence level) as an

illustrative example. The molar mass of inert gas does not change that much.

Therefore, the relative expanded uncertainty with

95 % confidence level is here set to 1.5 %.

The relative expanded uncertainty with 95 % confidence level for the pressure,

temperature, density and velocity of sound are all set to 1 %. These are expected to be

realistic or conservative values for the measurements in questions. The volumetric

flow rate from an ultrasonic flare gas is typically specified to 2 - 5 %. With this in

mind, the relative expanded uncertainty with 95 % confidence level of the flow

velocity is selected as 3 % in the example here. For the diameter, 0.2 % is selected,

based on previous experience.

Uncertainty models for the three alternatives have been developed, and the data from

Table 1 have been applied. The results from this process are the uncertainty budgets

in Table 2, Table 3 and Table 4. In Figure 2, Figure 3 and Figure 4 it is shown how

large the contributions from each of the input uncertainties are on the total

uncertainty.

The uncertainty budget in Table 2 covers the case where an on-line gas

chromatograph is used. That is not expected to be a realistic alternative, due to cost-

benefit issues. However, it can be viewed as a base-case that can represent the lowest

possible uncertainty that realistically can be achieved. It is seen in that case that the

flow velocity is the dominating uncertainty contribution. The total relative expanded

uncertainty will be somewhat larger than the relative expanded uncertainty of the flow

velocity, here 3 %.

The uncertainty budget in Table 3 covers the case where a densitometer is used. In

this uncertainty analysis it is assumed that the same measurements of pressure and

temperature can be used both for the density to molar mass calculation and for the

conversion to standard volume. This means that in Eq. (14), we have assumed that Td

= T and Pd = P. The dominating uncertainty contribution in this case is due to the

fixed and pre-estimated value of the molar mass of the VOC gas. Altogether, the

relative expanded uncertainty of the mass flow rate of VOC will be around 20 %. This

is quite similar to the case where measurement of velocity of sound is used, see Table

4.

It should, however, be mentioned that for both methods (using densitometer or using

measured velocity of sound) the relative expanded uncertainty will depend on the

concentration of VOC in the flowing gas. The higher this concentration is, the lower

is the uncertainty. As the VOC concentration typically increases over a loading

period, this means that the relative expanded uncertainty will be reduced. However,

the uncertainty analysis presented above with a relative expanded uncertainty of the

mass flow rate of VOC is considered to be realistic as an average over a loading

period.


22 – 25 October 2013

10

Table 1 Input values and uncertainties for the uncertainty analyses for the three

alternative ways of measuring mass flow rate of VOC.

Value Rel. expanded uncertainty

(95 % confidence level)

Molar fractions

VOC-gas 45 %

Methane 5 %

Ethane 9 % 3 % rel

Propane 15 % 3 % rel

Iso-Buthane 6 % 3 % rel

n-Buthane 5 % 3 % rel

Iso-Pentane 2 % 3 % rel

n-Pentane 2 % 3 % rel

Hexane+ 1 % 3 % rel

Inert gas 55 %

Nitrogen 48.125 %

Carbon dioxide 6.875 %

Molar mass

VOC-gas 45 g/mole 10 %

Inert gas 30 g/mole 1.5 %

Total gas 36.8 g/mole

Other measurements

Pressure 1.3 bara 1 %

Temperature 20 °C 1 % of Kelvin temperature

Flow velocity 3 %

Density 1.98 kg/m³ 1 %

Velocity of sound 280.4 m/s 1 %

Inner pipe diameter 0.45 m 0.2 %


22 – 25 October 2013

11

Table 2 Uncertainty budget for the mass flow rate of NMVOC-gas, in the

alternative where a gas chromatograph is in use.

Uncertainty contribution Input uncertainty Combined uncertainty

Rel.

expanded

uncertainty

Confidence

interval and

distribution

Rel.

standard

uncertainty

Rel.

sensitivity

coefficient

Rel.

variance

Flow velocity 3 % 95 % (norm) 1.5 % 1.0000 2.2500 %²

Inner diameter 0.2 % 95 % (norm) 0.1 % 2.0000 0.0400 %²

Pressure 1 % 95 % (norm) 0.5 % 1.0000 0.2500 %²

Temperature 1 % 95 % (norm) 0.5 % 1.0000 0.2500 %²

Molar fraction - C2 3 % 95 % (norm) 1.5 % 0.1390 0.0435 %²

Molar fraction - C3 3 % 95 % (norm) 1.5 % 0.3399 0.2599 %²

Molar fraction - iC4 3 % 95 % (norm) 1.5 % 0.1792 0.0723 %²

Molar fraction - nC4 3 % 95 % (norm) 1.5 % 0.1493 0.0502 %²

Molar fraction - iC5 3 % 95 % (norm) 1.5 % 0.0742 0.0124 %²

Molar fraction - nC5 3 % 95 % (norm) 1.5 % 0.0742 0.0124 %²

Molar fraction - C6+ 3 % 95 % (norm) 1.5 % 0.0443 0.0044 %²

Sum of variances 3.2450 %²

Combined relative standard uncertainty 1.80 %

Relative expanded uncertainty (95 % confidence level) 3.6 %

Figure 2 Uncertainty contributions to the mass flow rate of NMVOC-gas, in the

alternative where a gas chromatograph is in use.


22 – 25 October 2013

12

Table 3 Uncertainty budget for the mass flow rate of VOC-gas, in the alternative

where a densitometer is in use.


Rel.

expanded

uncertainty

Confidence

interval and

distribution

Rel.

standard

uncertainty

Rel.

sensitivity

coefficient

Rel.

variance



Pressure 1 % 95 % (norm) 0.5 % 4.2114 4.4341 %²


Density 1 % 95 % (norm) 0.5 % 5.2114 6.7898 %²

Molar mass – inert gas 1.5 % 95 % (norm) 0.75 % 2.2566 2.8643 %²

Molar mass – VOC gas 10 % 95 % (norm) 5 % 1.9417 94.2596 %²




Figure 3 Uncertainty contributions to the mass flow rate of VOC-gas, in the

alternative where a densitometer is in use.


22 – 25 October 2013

13

Table 4 Uncertainty budget for the mass flow rate of VOC-gas, in the alternative

where a velocity of sound is used.


Rel.

expanded

uncertainty

Confidence

interval and

distribution

Rel.

standard

uncertainty

Rel.

sensitivity

coefficient

Rel.

variance



Pressure 1 % 95 % (norm) 0.5 % 1.0000 0.2500 %²


Velocity of sound (VOS) 1 % 95 % (norm) 0.5 % 7.2507 13.1433 %²

Model unc, VOS of inert 1 % 95 % (norm) 0.5 % 0.2266 0.0128 %²

Model unc, VOS of VOC 1 % 95 % (norm) 0.5 % 0.4924 0.0606 %²

Molar mass – inert gas 1.5 % 95 % (norm) 0.75 % 1.8374 1.8991 %²

Molar mass – VOC gas 10 % 95 % (norm) 5 % 1.5693 61.5666 %²




Figure 4 Uncertainty contributions to the mass flow rate of VOC-gas, in the

alternative where velocity of sound is used.


22 – 25 October 2013

14

3.5 Recommended program

Based on the evaluations presented above, it was recommended that the measurement

program should be based on measured flow velocity, pressure, temperature and

density. As a back-up-system, the emissions can also be found from the velocity of

sound instead of the density. In this way, there is redundancy because two emission

calculations can be compared, and it is possible to carry out a consistency test

between the measured density and velocity of sound.

This measurement system is recommended to be installed on 8 ships in this initiating

phase. Based on the measurement on these ships, the emissions from the other ships

(with no such measurement system) will be estimated using emission factors. The

uncertainty of the measured emission from one single loading is then estimated to be

around 20 % (relative expanded uncertainty with 95 % confidence level). The similar

uncertainty for the total emissions from one ship under loading over a whole year will

be reduced significantly. Similarly, combination of emission data from several ships

will reduce the uncertainty further. This means that it is expected that the relative

expanded uncertainty for the total emission from all ships over one year will be in

around 10 – 12 %.

4 OPERATIONAL EXPERIENCES

The measurement system has in 2012 and 2013 been installed on 8 shuttle tankers. Of

these, 3 are equipped with a condensation VOC recovery unit (VRU). These 3 ships

have a double set of instruments, one for gas emission out of loading tanks and into

the VRU and one out of the VRU and out to the emission point to atmosphere. This

double instrumentation is used in order to obtain more generic data for general use on

the ships with no measurement system.

Prior to full installations, components of the systems were tested on the ship under

realistic harsh conditions, in order to ensure successful operation. Especially, the

density measurements were tested. Some operational experiences with respect to this

issue will be presented below. After installation of the total measurement system, a

period of testing and qualifying the system was initiated. This phase is at present in

the completion stage.

All 8 ships are logging the measured data (flow velocity, velocity of sound, density,

pressure, temperature, etc.) locally during loading. After loading, the logged data are

transferred to a central data serve on shore. Thereafter, the emission flow rates and the

accumulated amount of emission per loading are calculated and reported.

4.1 Data quality control

The fact that it is also possible to calculate the VOC emission based on the velocity of

sound of the emission gas and the volumetric flow measurements means that this can

be used as a quality check of the density based VOC emission estimation.

One way of checking the result is to compare the measured velocity of sound (VOS)

and molar mass with expected values as shown in Figure 5. Here the green dots


22 – 25 October 2013

15

represent measurements out of loading tanks and into the VOC recovery unit (VRU)

and blue dots represents measurements out of VRU. The violet, red and blue lines

represent the expected VOS as a function of the molar mass of the emission gas for

different molar masses of the VOC gas. In the case shown here, the measurements of

density and velocity of sound out of the VRU (green dots) appear to be consistent.

However, the measurements of density and velocity of sound into the VRU (green

dots) appear to be inconsistent. In this case either the measured velocity of sound or

the measured density is too high (or both). Subsequently, calibrations have to be

performed on the instruments in question.

Figure 5 Velocity of sound vs. molar mass of emission gas out of loading tanks (into

VOC recovery unit (VRU)) and out of VRU.

4.2 Operational challenges

4.2.1 Pipe geometry

Normally, there is an installation requirement of 20 diameters of straight length of

pipe upstream a flow meter to ensure a fully developed flow profile at the flow meter.

This proved to be difficult without a major redesign of the piping onboard the ships.

To overcome this problem, all flow meters were installed with a perforated plate flow

conditioner upstream in order to reduce the effect of bends and other installation

conditions generating asymmetric and transversal flow.

4.2.2 Liquid droplets in gas flow

In the first installation the densitometer was mounted with measurement head into the

process pipe. However, during the initial testing of the measurement system it became

apparent that the densitometer did not perform as expected. After analyzing the setup

it was discovered that in contrast to the initial expectations, significant amounts of


22 – 25 October 2013

16

liquid droplets were present in the gas flow (mostly water). The densitometer, being

very sensitive to liquid, could therefore not be installed directly into the process pipe.

The solution to this problem was to install the densitometer in a bypass line with

appropriate gas drying measures. To adjust the density to process condition a pressure

and a temperature measurement had to be included at the densitometer. This,

however, added some uncertainty to the estimation of VOC emission, since the

uncertainty of the extra pressure and temperature measurement at the densitometer

location, must be accounted for.

4.2.3 Velocity limitations of flow meter

During the test and installation stage the flow meter on some of the ships had a

significant drop out (reported measurement error). After careful investigation, the

drop out was found to be correlated to a flow velocity in excess of about 25 m/s. For

adjusting the loading tank pressure some of the shuttle tankers use an off and on

regulation of the riser valve. The riser valve is then opened fully when the pressure

rises to a certain level and then closed again when it falls below a second pressure.

This lead to a series of shorter burst of gas with a flow velocity that is higher than 25

m/s. Figure 6 shows the flow velocity measurement and Figure 7 the signal strength

of the ultrasound transducers and error rate of the flow meter on a ship with an off and

on regulation of the riser valve.

In order to overcome this it was decided to regulate the pressure in a way that resulted

in a more steady flow with a flow velocity lower than 25 m/s. First, this meant that the

riser valve had to be controlled manually, but eventually the ships will switch to a

continuous tank pressure regulation which will result in a more steady flow.

Figure 6 Flow velocity from loading tanks when on-off regulated by a riser

valve.

Figure 7 Signal strength of ultrasound transducers and errors in flow meter. Same

case as in Figure 6.


22 – 25 October 2013

17

4.3 Verification tests

During the spring and summer of 2013 a series of verification test were carried out on

two of the ships having the VOC emission measurement system, one ship with VOC

recovery unit (VRU) and one without such a unit. The tests were performed by

SINTEF Marintek and were carried out by the use of a gas chromatograph for

measuring the gas composition. The same flow meter was used by both the gas

chromatograph measurement and the VOC emission measurement system. In total the

verification tests were performed on five loadings, three on the ship without VRU and

two on the ship with VRU.

The VOC emission measurement system has a relatively large uncertainty for

measurements on single loadings, see Section 3.4. This is due to the fact that it uses

an estimated average molar mass for all VOC-emissions on the Norwegian

continental shelf. However, the uncertainty can be reduced for verification tests if the

emission is calculated with mean molar mass from the gas chromatograph

measurement for the same loading. In addition uncertainty contributions from the

flow measurement can be ignored when comparing the two methods, since the same

flow meter is used both for the gas chromatograph and the VOC emission

measurement system. The remaining uncertainty contributions are then reduced to

about 5 % (relative expanded uncertainty with 95 % confidence level). The gas

chromatograph measurement has a relative expanded uncertainty with 95 %

confidence level of 4 %, according to Marintek.

The results from the verification tests are shown in Table 5. There the difference in

estimated emissions between the method based on density and the reference method

based on gas chromatography is shown. The emissions using the measured density are

calculated both using the pre-estimated molar mass of VOC gas as input, and using a

molar mass value measured by the gas chromatograph during loading. The deviation

in estimated VOC emission is fairly large for the measurements out of the loading

tanks, when using pre-estimated molar mass of VOC gas. The reason for this is that

the pre-estimated molar mass of VOC deviates significantly from the similar molar

Table 5 Deviations in measured VOC emission by measurement system based on

density and a reference system based on gas chromatography. The molar

mass of the VOC gas is either set to a pre-estimated value, or to a value

measured by the gas chromatograph during the loading.

Difference from GC verification measurements

From loading tanks From VRU

Vessel

#

Cargo

#

With pre-

estimated

molar mass

of VOC gas

With molar mass of

VOC gas measured

by GC

With pre-

estimated

molar mass

of VOC gas

With molar mass

of VOC gas

measured by GC

1 1 39,0 % 8,7 %

1 2 23,2 % 3,6 %

1 3 24,5 % 4,6 %

2 4 21,0 % 3,3 % 1,4 % -0,8 %

2 5 24,4 % 2,4 % 7,5 % -2,1 %


22 – 25 October 2013

18

mass measured by the gas chromatograph. For the measurements out of the VRU, the

pre-estimated molar mass of VOC matched much better with the one measured by the

gas chromatograph. Therefore, the deviations were lower in this case. When the VOC

gas molar mass measured by the gas chromatograph is used, the agreement with the

verification measurements were within a few per cents, and thus within what can be

expected taking the uncertainties into account. Therefore, these results are promising

with respect to ensuring that the measurement system operates as expected under the

harsh conditions on the ship.

4.4 Estimation of average molar masses

The estimation of average molar mass of VOC gas emission for the Norwegian

continental shelf is based on historic gas chromatograph measurements from different

production fields. An average field specific molar mass is estimated for each field and

these are weigthed against produced volume for each field to estimate the average

molar mass for the VOC gas emission for the Norwegian continental shelf. Since the

produced volumes cannot be known exactly in advance the exact total emission from

the Norwegian continental shelf cannot be reported officially until all production data

are available. Another drawback when using an average molar mass of VOC gas

emission for the whole Norwegian continental shelf is that the uncertainty for each

individual loading is relatively high, as shown in chapter 3.4. A possible

improvement, on the accuracy of measurements on individual loadings, could be to

use field specific molar masses in the calculation of VOC emissions. An added benefit

of this would be an increased accuracy in the reported VOC emissions before total oil

production data is available.

5 CONCLUSIONS

In this paper, a new measurement and calculation program for VOC emissions during

loading of shuttle tankers is presented. The background for the program is new

requirements from Norwegian authorities motivated by the international agreements

on reduction of VOC emissions.

The measurements of the VOC emissions are based on an ultrasonic flare gas flow

meter in combination with density measurements. As a back-up, the velocity of sound

measurement from the ultrasonic flow meter is also in use. The molar mass of the

inert gas and the VOC gas flowing through the ultrasonic meter has to be quantified.

By using a pre-estimated value for the molar mass of the VOC gas, the relative

expanded uncertainty with 95 % confidence level for the emission of VOC gas during

one loading is in the order of 20 %. This can be reduced by using field specific

coefficients. However, it is expected that the relative expanded uncertainty of the

annual emission from the Norwegian Sector as a whole, will be in the range 10 –

12 %.

The system is now installed on 8 ships, and the first operational experiences are

gained. After some initial challenges, the measurement systems are now stable. A

verification program where the emission measurements are compared to a reference

measurement using gas chromatography is now in its final stage. This indicates that

the measurements are consistent.


22 – 25 October 2013

19

6 ACKNOWLEDGEMENT

The authors wish to gratitude Norwegian VOC Industry Cooperation (VOCIC) for the

acceptance of publishing the content in this paper. Important discussions with Egil

Tveit, Sigbjørn Oaland and Egil Moe, Teekay Shipping Norway, and with Ole

Oldervik, Marintek (SINTEF) are also acknowledged. Finally, the data analysis

carried out by Astrid Marie Skålvik, CMR Instrumentation, has been of great value

for the results presented in the paper.

REFERENCES

The 1999 Gothenburg protocol to Abate Acidification and Ground-level Ozone, with

amendment from 2012, United Nations Economic Commission for Europe (UNECE),

2012.

fltough

Stamp

North Sea Flow Measurement Workshop October 2013

1

Flow Disturbance Cone Meter Testing

Gordon Stobie, GS Flow Ltd., formerly ConocoPhillips Company

Richard Steven, CEESI

Kim Lewis, DP Diagnostics Ltd

Bob Peebles, ConocoPhillips Company

1. Introduction

The cone meter is a generic Differential Pressure (DP) meter design. It operates

according to the same physical principles as other DP meters, such as orifice, nozzle and

Venturi meters etc. A cone meter is shown in Fig 1, with a cut away to reveal the DP

producing cone ‘primary element’.

Figure 1: Sectioned view of a Cone Meter (flow is left to right)

Piping components can induce asymmetry and swirl in flow. This ‘disturbed flow’, i.e.

asymmetrical and swirling flow, is known to induce flow rate prediction biases in many

flow meter outputs. Most flow meters have required minimum upstream/downstream

straight pipe lengths to mitigate disturbed flow. A flow conditioner mitigates disturbed

flow and reduces the minimum upstream and downstream straight pipe lengths required

by many flow meter designs.

Cone meters have grown in popularity due to their claimed immunity to flow

disturbances. Cone meters are said to require no flow conditioning and little upstream

and downstream straight pipe lengths. If this is true, cone meters can be installed in

many locations where no other flow meter could operate satisfactorily. A meter that is

immune to flow disturbances is of significant importance to industry. Hence,

independent proof of cone meter resistance to flow disturbances is important. However,

there is little literature in the public domain discussing cone meter performance in

disturbed flows.

2. Background to Cone Meter Reaction to Flow Disturbances

The cone meter patent expired in 2004 and the generic cone meter design is now offered

by several suppliers. Some manufacturers claim that the meters immunity to flow

disturbances stems from the cone acting as a flow conditioner. That is, the meter is said

to have inbuilt flow conditioning.

Figures 2 & 2a show sample diagrams from cone meter manufacturer’s literature. In both

cases, the literature states that the cone “flattens the velocity profile”, i.e. mitigates

asymmetric flow, upstream of the cone. It is certainly true that flow acceleration is

fltough

Stamp


2

Figure 2. Manufacturer 1 Diagram. Figure 2a. Manufacturer 2 Diagram.

known to be a good mechanism for flattening a velocity profile, i.e. mitigating

asymmetric flow effects. However, there has yet to be a rigorous scientific explanation

on why a cone element would be significantly more efficient at mitigating asymmetrical

flow effects than other primary element shapes. Furthermore, the public literature or

debate does not offer any rigorous explanation (other than occasional non-detailed verbal

comments about conservation of angular momentum) as to why a cone meter would

perhaps be more resistant to swirl than other DP meter design. Nevertheless, regardless

of why a cone meter is resistant to flow disturbances, there is independent research by

various parties which shows that cone meters are resistant to flow disturbances.

In 2004 McCrometer [1] showed 4”, 0.6 beta ratio (β) cone meter resistance to flow

disturbances by testing a cone meter with the moderate flow disturbance tests required by

API MPMS 22.2 [2]. In 2009 DP Diagnostics [3] showed 4”, 0.63β cone meter

resistance to flow disturbances by testing the cone meter with various moderate and

extreme flow disturbances. In 2010 SolartronISA [4] discussed cone and Venturi meter

resistance to moderate flow disturbances. SolartronISA showed the flow disturbance

resistance capabilities of both 0.6β and 0.85β cone meters. This was the first time a high

beta ratio cone meter (i.e. β > 0.63) had been tested with flow disturbances and the results

publicly released. Whereas the three independent mid-size cone / beta ratio data sets

showed the cone meter to be immune to flow disturbances, the SolartronISA 6”, 0.85β

cone meter results hinted at a beta ratio effect. The larger beta ratio (i.e. the smaller cone

relative to the pipe size) appeared to have a slightly degraded resistance to flow

disturbances.

Cone meter resistance to flow disturbances being dependent on beta ratio would be in line

with Venturi meter performance. ISO 5167–Part 4 [5] shows a table of minimum

upstream lengths for a Venturi meter with various upstream components (i.e. different

disturbances). The ISO indicate that a Venturi meter’s resistance to flow disturbance is

beta ratio dependent. The higher the Venturi meter beta ratio, the longer the required

upstream straight pipe length, i.e. the more susceptible the Venturi meter is to flow

disturbance. Furthermore, if the cone meter does obtain a high level of flow disturbance

immunity through the cone acting as a flow conditioner, it would stand to reason that the

smaller the cone relative to the meter body size (i.e. the larger the beta ratio) the less

conditioning, and the less resistance the cone meter would have to upstream disturbances.

In 2012 ConocoPhillips (COP) approached CEESI enquiring about high beta ratio cone

meter flow disturbance tests. CEESI owned a standard design 4”, sch 80, 0.75β cone


3

meter (see Figure 22). As this meter was readily available for use, the beta ratio was

suitably high to investigate high beta ratio cone meter disturbed flow resistance

characteristics, and the 0.75β cone meter size was very popular in industry, COP decided

to utilize this meter. Although testing this 0.75β advanced knowledge of cone meter flow

disturbance characteristics, COP was and is aware that some cone meter manufacturers

offer β ≤ 0.85, and claim flow disturbance immunity across the entire beta ratio range.

Therefore, the following test results are not to be considered conclusive. Further

investigation is required before a comprehensive understanding of cone meter resistance

to flow disturbances is achieved.

As the 4”, 0.75β cone meter was manufactured by DP Diagnostics, it had a downstream

pressure tap on the meter body (e.g. see Figure 1). The patented DP meter diagnostic

system (‘Prognosis’) was potentially available. This allowed COP to test both the cone

meters resistance to flow disturbances and the diagnostic systems operation when the

cone meter was subjected to flow disturbances.

3. Cone Meters and DP Diagnostics Self-Diagnostic Operating Principles

Figure 3 shows a cone meter with instrumentation and the (simplified) pressure

fluctuation (or “pressure field”) through the meter body. Traditional DP meters read the

inlet pressure (P1), the downstream temperature (T) and the differential pressure (∆Pt)

between the inlet pressure tap (P1) and a pressure tap positioned in the vicinity of the

point of low pressure (Pt). That is, traditional DP meter technology only takes a single

DP measurement from the pressure field.

Fig 3: Cone Meter with Instrumentation and Pressure Fluctuation Graph.

A pressure tap (Pd) downstream of the cone allows extra pressure field information to be

read. The DP between the downstream (Pd) and the low (Pt) pressure taps (or “recovered”

DP, ∆Pr), and the DP between the inlet (P1) and the downstream (Pd) pressure taps (i.e.

the permanent pressure loss, ‘PPL’, ∆PPPL) can be read. The sum of the recovered DP

and the PPL must equal the traditional differential pressure (equation 1).

PPLrt PPP --- (1)

The traditional flow rate equation is shown as equation 2. The additional downstream

pressure tap allows an extra two flow rate equations to be produced. The recovered DP

can be used to find the flow rate with an “expansion” flow equation (see equation 3) and

the PPL can be used to find the flow rate with a “PPL” flow equation (see equation 4).

Note tm.

, rm.

and PPLm.

represents the traditional, expansion and PPL mass flow rate


4

Traditional Flow Equation: tdtt PCEAm 2.

, uncertainty x% --- (2)

Expansion Flow Equation: rrtr PKEAm 2.

, uncertainty y% --- (3)

PPL Flow Equation: PPLPPLppl PAKm 2

.

, uncertainty z% --- (4)

equation predictions of the actual mass flow rate (.

m ) respectively. The symbol

represents the inlet fluid density. Symbols E , A and tA represent the geometric

constants of the velocity of approach, the inlet cross sectional area and the minimum (or

“throat”) cross sectional area through the meter respectively. The parameter is an

expansion factor accounting for gas density fluctuation. (For liquids =1.) The terms Cd ,

Kr & KPPL are the discharge coefficient, expansion coefficient and PPL coefficients

respectively.

These three flow coefficients can be found by calibration. Each can be set as a constant

with a set uncertainty rating, or, each may be fitted to the Reynolds number, usually at a

lower uncertainty rating. The Reynolds number is expressed as equation 5. Note that is the fluid viscosity and D is the inlet diameter. In the case of a flow coefficient being

fitted to the Reynolds number, as the Reynolds number (Re) is flow rate dependent, each

of the three flow rate predictions must be independently obtained by an iterative method.

A detailed derivation of these three flow rate equations is given by Steven [6].

D

m

.

4Re --- (5)

Every cone meter body is in effect three flow meters. There are three flow rate equations

predicting the same flow rate. Thus there are now effectively two check meters in series

with the traditional flow meter. The flow rates can be inter-compared to create

diagnostics. Naturally, all three flow rate equations have individual uncertainty ratings

(say x%, y% & z% as shown in equations 2 through 4). Therefore, even if a cone meter

was operating correctly, the flow predictions would not match precisely. However, a

correctly operating meter should have no discrepancy between any two flow rate

predictions greater than the root mean square value of the two flow prediction

uncertainties. The maximum allowable difference between any two flow rate equations,

i.e. % , % & % is shown in equation set 6a to 6c.

Traditional & PPL Meters % allowable difference 22%%% zx -- (6a)

Traditional & Expansion Meters % allowable difference: 22%%% yx -- (6b)

Expansion & PPL Meters % allowable difference: 22%%% zy -- (6c)

If the percentage difference between any two flow rate predictions is less than the

allowable uncertainties, then no potential problem is found. If the percentage difference

between any two flow rate equations is greater than the allowable uncertainties, then this


5

Traditional to PPL Meter Comparison : %100*%...

ttPPL mmm -- (7a)

Traditional to Expansion Meter Comparison: %100*%...

ttr mmm -- (7b)

PPL to Expansion Meter Comparison: %100*%...

PPLPPLr mmm

-- (7c)

indicates a metering problem and the flow rate predictions should not be trusted. The

three flow rate percentage differences are calculated by equations 7a to 7c.

The three DP ratios can be used directly for diagnostics purposes. The Pressure Loss

Ratio (or “PLR”) is the ratio of the PPL to the traditional DP. The PLR value is a

characteristic for cone meters operating with single phase homogenous flow. It can be

expressed as a constant value, or related to the Reynolds number. We can rewrite

Equation 1:

1

t

PPL

t

r

P

P

P

P --- (1a) where

t

PPL

P

P

is the PLR.

PPL to Traditional DP ratio (PLR): ( PPLP / tP )calibration , uncertainty a%

Recovered to Traditional DP ratio (PRR): ( rP / tP )calibration , uncertainty b%

Recovered to PPL DP ratio (RPR): ( rP / PPLP )calibration , uncertainty c%

From equation 1a, if PLR is a set value (for any given Reynolds number) then both the

Pressure Recovery Ratio or “PRR”, (i.e. the ratio of the recovered DP to traditional DP)

and the Recovered DP to PPL Ratio, or “RPR” must also be set values. All DP ratios

available from the three DP pairs are constant values for any given cone meter geometry

and Reynolds number. These three DP ratios can be found by calibrating the DP meter.

DP ratios found in service can be compared to expected values. The expected values are

obtained from the meter calibration. Let us denote the percentage difference between the

actual PLR and the expected value as % , the difference between the actual PRR and

the expected value as % , and the difference between the actual RPR and the corrected

value as % . These values are found by equations 8a to 8c.

% {[ PLR actual - PLR calibration ] / PLR calibration} %100* --- (8a)

% {[ PRR actual - PRR calibration ] / PRR calibration} %100* --- (8b)

% {[ RPR actual - RPR calibration ] / RPR calibration} %100* --- (8c)

If the percentage difference between the in-service and expected DP ratio is less than the

stated uncertainty of that expected DP ratio value, then no potential problem is found. If

the percentage difference between the in-service and expected DP ratio is greater than the

stated uncertainty of that expected DP ratio value, then a potential problem is found, and


6

the flow rate predictions should not be trusted. With three DP ratios, there are three DP

ratio diagnostic checks.

Equation 1 holds true for all generic DP meters (even after physical damage) allowing a

dedicated DP reading diagnostics check. Therefore, any result suggesting that it does not

hold true is an indication of false DP readings (regardless of whether the meter body is

serviceable or not). The traditional DP (ΔPt) can be inferred by summing the read

recovery DP (ΔPr) and permanent pressure loss (ΔPPPL). This gives an inferred

traditional DP (ΔPt,inf) that can be compared to the directly read traditional DP (ΔPt,read).

Whereas theoretically these values are the same, due to the uncertainties of the three DP

transmitters, even for correctly read DPs, they will be slightly different. The percentage

difference ( % ) can be calculated as seen in equation 9.

% {( readtt PP ,inf, ) / readtP , } %100* --- (9)

The uncertainty rating of each DP reading will be known. A maximum allowable

percentage difference ( % ) between the directly read and inferred traditional DP values

can be assigned. If the percentage difference between the directly read and inferred

traditional DP values ( % ) is less than the allowable percentage difference ( % ), then

no potential problem is found. However, if this percentage difference ( % ) is greater

than the allowable percentage difference ( % ), then a problem with the DP

measurements is confirmed and the flow rate predictions cannot be trusted.

Table 1 shows the seven situations that would signal a cone meter system warning. For

convenience we use the following naming convention:

Normalized flow rate inter-comparisons:

Normalized DP ratio comparisons:

Normalized DP sum comparison:


tP & pplP -1 ≤ x1

1 -1< x1 or x1 1 1 ≤ y1

1 -1< y1 or y1 1

tP & rP -1 ≤ x2

1 -1< x2 or x2 1 1 ≤ y2

1 -1< y2 or y2 1

rP & pplP -1 ≤ x3

1 -1< x3 or x3 1 1 ≤ y3

1 -1< y3 or y3 1

readtP, & inf,tP -1 ≤ x4

1 -1< x4 or x4 1 N/A N/A

Table 1. DP meter - possible diagnostic results.

For practical real time (or historical auditing) use, a graphical representation of the

diagnostics continually updated on a PC screen (while being archived) can be simple and

effective. A graph can be created with a normalized diagnostic box (or “NDB”) with

corner co-ordinates: (1, 1), (1, -1), (-1, -1) & (-1, 1). On such a graph, meter diagnostic

points can be plotted, i.e. (x1, y1), (x2, y2), (x3, y3) & (x4, 0), as shown in Figure 4.

If all points are within the NDB the operator sees no metering problem and the traditional

meters flow rate prediction can be trusted. However, if one or more of the points falls

x4 = %%

y1 = %% a , y2 = %% b , y3 = %% c

x1 = %% , x2 = %% , x3 = %%


7

Figure 4. Normalized diagnostic box with diagnostic results

outside the NDB, the operator has an indication that the meter is not operating correctly

and that the meters traditional (or any) flow rate prediction cannot be trusted. If the point

(x4, 0) falls out with the NDB, regardless of the other three diagnostic point locations, this

is a statement that there is a DP reading problem. If one or more of the (x1, y1), (x2, y2) &

(x3,y3) points fall outside the NDB, while (x4, 0) remains within the NDB, this infers that

there is a meter body malfunction. The particular pattern of a diagnostic warning in some

cases indicates a particular problem, and in other cases short-lists the problems that

produce such a diagnostic pattern.

Although these diagnostics have been described with respect to cone meters they are

applicable to, and have been applied to other DP meters. The Intellectual Property

holders, DP Diagnostics have partnered with Swinton Technology to create the

commercial product ‘Prognosis’.

3a. Discussion on a Common Misperception Regarding ‘Prognosis’

Prognosis operates by comparing DP meters ‘found’ to ‘expected’ performances.

Equally, it could be said that Prognosis operates in reverse by comparing the ‘expected’

to ‘found’ performances. The expected performance is set by the DP meter calibration

(or, in the case of an orifice meter, from information derivable from statements in the ISO

standards). The diagnostics indicate a problem when there is a significant mis-match

between the “found- to-expected” performance (or “expected-to-found” performance).

DP Diagnostics has become aware that some engineers have mistakenly assumed that the

integrity of Prognosis is dependent on the correctness of the calibrated performance

criteria entered into the diagnostic system. They have assumed that an incorrect entered

calibration / expected performance will compromise the integrity of the diagnostic

system. This is not true.

Prognosis does not make any limiting assumption that either the ‘expected’ performance

or the ‘found’ performance must be the correct performance with which the other can be

compared and judged. This diagnostic method considers neither the ‘found’ performance

nor the ‘expected’ performance to be automatically trustworthy. If there is a significant

mis-match between the found-to-expected (or expected-to-found) performance,

regardless of the reason for the mis-match, the diagnostics correctly indicate that a

problem exists.

For a DP meter system to operate correctly two conditions must be met:


8

1) The correct meter geometry and performance characteristics (e.g. discharge

coefficient) must be used with equation 2, i.e. the traditional flow rate equation.

2) The DP meter system must be fully serviceable, i.e. the DPs must be read

correctly and the meter body must be free of any performance affecting problems.

When a DP meter system is physically fully serviceable and that meters performance

criteria and geometry is correctly entered into the calculations, the expected and found

meter performance will overlap and agree. Only when the found and expected meter

performances agree within allowable uncertainties does the diagnostic system give the

meter a ‘clean bill of health’.

If either (or both) of these two conditions are not met then the DP meter will mis-

measure the flow rate. Prognosis monitors for both these flow rate prediction bias

producing scenarios. The diagnostic system is not dependent on the correctness of the

expected (i.e. calibrated) performance criteria. The diagnostic system uses:

the expected performance to judge the correctness of the found performance, and

the found performance to judge the correctness of the expected performance!

Prognosis does not contain any inherent unproven assumption where the expected meter

performance criteria are fixed ‘trusted’ values with which the ‘questionable’ found meter

performance must overlap. On the contrary, the diagnostic system treats both the

expected and found meter performance as equally questionable until it is shown that the

expected and found meter performances agree with each other.

If the DP meter system is physically serviceable, and that meters performance

criteria and geometry are correctly entered, then the expected and found meter

performances will agree, and the diagnostics correctly gives no alarm.

If the meter is not physically serviceable, and the serviceable meters performance

criteria and geometry are correctly entered, then the expected and found meter

performance will not agree, and the diagnostics correctly gives an alarm.

If the meter is physically serviceable, and the meters performance criteria and

geometry are incorrectly entered, then the expected and found meter performance

will not agree, and the diagnostics correctly gives an alarm.

If the meter is not physically serviceable, and the meters performance criteria and

geometry are incorrectly entered, then the expected and found meter performance

will not agree (expect in the extremely unlikely and freakish coincidence where

the two independent problems would need to combine to neutralize all seven

different diagnostic checks), and the diagnostics correctly gives an alarm.

Therefore, only when the questionable ‘found’ DP meter performance and the equally

questionable ‘expected’ DP meter performance agree with each other (within allowable

uncertainties) is it shown that the meter system is serviceable and the flow rate prediction

is trustworthy. Either scenario of an erroneous expected performance or an unserviceable


9

meter system (or both together) will trigger Prognosis to correctly produce a warning that

the flow rate prediction is untrustworthy.

4. Disturbed Flow Cone Meter Performance Tests & Results

In 2009 DP Diagnostics tested a 4”, sch 80, 0.63β cone meter (with a downstream

pressure tap) with straight pipe runs and then disturbances. Calibrating the meter for

diagnostics took no more effort or expense than a standard calibration. The meter is

shown in Figures 5 thru 12. In order to put the 2012 COP 4”, sch 80, 0.75β cone meter

flow test results in context this earlier 2009 test series will be discussed first.

4a. DP Diagnostics 2009 4”, sch 80, 0.63β Cone Meter Tests

To appreciate the position of the cone to the exit of the component generating flow

disturbances, the upstream pressure port is 1.5D downstream of the meters inlet flange

face and 2.125” (≈ 0.5D) upstream of the cone apex. Thus a distance of ‘0D’

corresponds to the exit of the disturbance generating device being 2D upstream of the

cone. Cone meter manufacturers can (and do) vary the position of the cone within the

meter body thus producing some straight length pipe run while on paper it can look like

there is none.

The straight pipe run (‘baseline’) calibration set up is shown in Figure 5. The resulting

calibration parameters were checked against a variety of typical real world installations.

Figures 6 thru 12 show the various flow disturbance tests conducted on this meter, i.e.:

Figure 6: Double Out of Plane Bend (‘DOPB’) at 0D upstream.

Figure 7: DOPB at 0D upstream with Half Moon Plate (‘HMP’) at 2D downstream.

Figure 8. DOPB at 0D upstream & Triple Out of Plane (TOPB) downstream.

Figure 9. HMP 6.7D upstream.

Figure 10. HMP 8.7D upstream.

Figure 11. HMP 2D downstream.

Figure 12. 3”(540) Swirl Generator upstream of an 4” Pipe 9D Expansion upstream.

The DOPB test (Figure 6) was also conducted at 2D & 5D (not shown). The half moon

orifice plate (HMP) blocked the top half of the cross sectional area and models a gate

valve at 50% closed. A real gate valve has the gate centered on the valve seat with

flanges at either side to connect it to the pipe system. Thus typically the gate is 1.5D to

2D from the adjacent flange. The HMP sandwiched between two flanges was given 2D

on either side of the plate to mimic a gate valve installation at 0D. As such the upstream

HMP installed at 6.7D and 8.7 D upstream models a gate valve 50% closed at

approximately 5D and 7D upstream of the cone meter inlet flange. The downstream HMP

installed at 2D (Fig. 11) models a gate valve 50% closed at approximately 0D

downstream of the cone meter. (There is 3D between the cone and the meter exit.)

Cone meters require individual calibration, as discussed by Hodges et al [7]. The

baseline calibration results are shown in Figures 13 & 14 along with the data fit

uncertainties. The baseline tests were carried out at two pressures (17 & 41 Bar). The

sonic nozzle reference had a 0.35% uncertainty and 0.1% repeatability. Figure 13 shows

the baseline flow coefficients. A constant discharge coefficient gave an uncertainty


10

Figure 5: Baseline Installation Figure 6: DOPB, 0D up

Figure 7: DOPB 0D up & HMP 2D down Figure 8: DOPB 0D up & TOPB down

Figure 9: HMP 6.7D up Figure10: HMP 8.7D up

of 0.5%. The expansion and PPL flow coefficient linear Reynolds number fits both give

1.1% uncertainty. Figure 14 shows that the DP ratios, the constant value data fits and

associated uncertainties.

Due to the extensive testing, and the fact that that pressure does not affect the parameters,

all flow disturbance tests were carried out at one nominal pressure of 17 Bara. Figures

15, 16 & 17 show the calibrated discharge, expansion & PPL flow coefficients across all

the subsequent disturbances tested. Figure 15 shows that this cone meter is extremely


11

Figure 11: HMP 2D down Figure 12: 3” Swirl Generator+9D up Exp

Cd = 0.8026

+/-0.5%

Kr =1.211 + (-5E-09 * Re)

+/- 1.1%

Kppl = 0.464 + (-1.6E-09*Re)

+/- 1.1%

0.2

0.4

0.6

0.8

1

1.2

1.4

0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Reynolds Number

Flo

w C

oeff

icie

nts

Cd

Kr

Kppl

Figure 13: 4”, 0.63β cone meter baseline flow coefficient results.

PLR = 0.559, +/- 1%

PRR = 0.4409, +/- 1.5%

RPR = 0.7851, +/- 1.7%

0.3

0.4

0.5

0.6

0.7

0.8

0.9

500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Reynolds Number

DP

Ra

tio

s

PLR

PRR

RPR

Figure 14: 4”, 0.63β cone meter baseline DP ratio results.

resistant to disturbed flow. Only two installations caused the predicted discharge

coefficient to vary beyond the baseline 0.5% uncertainty, i.e. the HMP upstream

installations and the swirl generator with expander upstream installation. Both

installations are extreme, and rare in the real world.


12

Cd = 0.803,+/-1% to 95% confidence

+0.5%

-0.5%

+1%

-1%

0.76

0.77

0.78

0.79

0.8

0.81

0.82

0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Reynolds Number

Dis

ch

arg

e C

oe

ffic

ien

t

Baseline

Double Out of Plane Bend 0D upstream



Double Out of Plane Bend 0D upstream, Half Moon Plate 2D dow nstream

Double Out of Plane Bend 0D upstream, Triple Out of Plane Bend 0D dow nstream

Half Moon Plate 6.7D upstream

Half Moon Plate 8.7D upstream

Half Moon Plate 2D dow nstream

Sw irl Generator w ith 3" to 4" Expansion 9D upstream

Figure 15: 4”, 0.63β cone meter disturbed flow discharge coefficient results.

Kr = 1.211+(-5E-9*Re)

+/- 2.5% to 95% confidence

+1.1%

-1.1%

+2.5%

-2.5%

1

1.05

1.1

1.15

1.2

1.25

1.3

500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Reynolds Number

Ex

pa

ns

ion

Flo

w C

oe

ffic

ien

t

BaselineDouble Out of Plane Bend 0D upstreamDouble Out of Plane Bend 2D upstreamDouble Out of Plane Bend 5D upstreamDouble Out of Plane Bend 0D upstream, Half Moon Plate 2D dow nstreamDouble Out of Plane Bend 0D upstream, Triple Out of Plane Bend 0D dow nstreamHalf Moon Plate 6.7D upstreamHalf Moon Plate 8.7D upstreamHalf Moon Plate 2D dow nstreamSw irl Generator w ith 3" to 4" Expansion 9D upstream

Figure 16: 4”, 0.63β cone meter disturbed flow expansion coefficient results.

The HMP at 6.7D, i.e. gate valve at 5D upstream is a short upstream distance for such an

extreme disturbance. This disturbance produced a slight discharge coefficient bias

averaging +0.8%. By 8.7D, i.e. a gate valve at 7D upstream, the meter performance was

within the baseline calibration uncertainty (when allowing for reference meter

repeatability and 95% confidence in the data).

The extreme swirl with a 9D expansion upstream is an extreme disturbance. This

disturbance produced a slight discharge coefficient bias averaging -0.6 % which

deteriorated to -1% at low flow.

Figures 16 & 17 show the disturbance effects on the expansion and PPL flow

coefficients. These parameters’ resistance to disturbed flow is critical to the practical

applicability of the diagnostic methodology for cone meters. The disturbed flow has a

greater adverse effect on both these coefficients than it does on the discharge coefficient,

but, crucially they are also both relatively immune to the disturbances in the flow. The


13

Kppl = 0.464 + (-1.6E-09*Re)

+/- 2.5% to 95% confidence

+1.1%

-1.1%

+2.5%

-2.5%

0.41

0.42

0.43

0.44

0.45

0.46

0.47

0.48

500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Reynolds Number

PP

L C

oeff

icie

nt

BaselineDouble Out of Plane Bend 0D upstreamDouble Out of Plane Bend 2D upstreamDouble Out of Plane Bend 5D upstreamDouble Out of Plane Bend 0D, Half Moon Plate 2D downstreamDouble Out of Plane Bend 0D, Triple Out of Plane Bend 0D downstreamHalf Moon Plate 6.7D upstreamHalf Moon Plate 8.7D upstreamHalf Moon Plate 2D downstreamSwirl Generator with 3" to 4" Expansion 9D upstream

Figure 17: 4”, 0.63β cone meter disturbed flow PPL coefficient results.

+4.5%

-4.5%

-20

-15

-10

-5

0

5

10

0 500000 1000000 1500000 2000000 2500000 3000000 3500000

Reynolds Number

% P

LR

Sh

ift

Double Out of Plane Bend, 0D upstreamDouble Out of Plane Bend, 2D upstreamDouble Out of Plane Bend, 5D upstreamDouble Out of Plane Bend 0D upstream + Half Moon Plate 2D downstreamDouble Out of Plane Bend 0D upstream + Triple Out of Plane Bend 0D downstreamHalf Moon Plate 6.7D upstreamHalf Moon Plate 8.7D upstreamHalf Moon Plate 2D downstreamSwirl Generator then 3" to 4", 9D upstream

Figure 18: 4”, 0.63β cone meter disturbed flow PLR results.

+6%

-6%

-25

-20

-15

-10

-5

0

5

10

0 500000 1000000 1500000 2000000 2500000 3000000 3500000

Reynolds Number

% P

RR

Sh

ift

Double Out of Plane Bend, 0D upstreamDouble Out of Plane Bend, 2D upstreamDouble Out of Plane Bend, 5D upstreamDouble Out of Plane Bend 0D upstream + Half Moon Plate 2D downstreamDouble Out of Plane Bend 0D upstream + Triple Out of Plane Bend 0D downstreamHalf Moon Plate 6.7D upstreamHalf Moon Plate 8.7D upstreamHalf Moon Plate 2D DownstreamSwirl Generator then 3" to 4", 9D upstream

Figure 19: 4”, 0.63β cone meter disturbed flow PRR results.


14

+10%

-10%

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

0 500000 1000000 1500000 2000000 2500000 3000000 3500000

Reynolds Number

% R

PR

Sh

ift

Double Out of Plane Bend, 0D upstreamDouble Out of Plane Bend, 2D upstreamDouble Out of Plane Bend, 5D upstreamDouble Out of Plane Bend 0D upstream + Half Moon Plate 2D downstreamDouble Out of Plane Bend 0D upstream + Triple Out of Plane Bend 0D downstreamHalf Moon Plate 6.7D upstreamHalf Moon Plate 8.7D upstreamHalf Moon Plate 2D downstreamSwirl Generator then 3" to 4", 9D upstream

Figure 20: 4”, 0.63β cone meter disturbed flow RPR results.

different disturbances cause the spread of data around both the expansion & PPL

coefficients baseline data fits to increase from ±1.1% to ±2.5%.

Figures 18, 19 & 20 show the PLR, PRR & RPR respectively, across all the disturbances

tested. The DP ratio uncertainty increase due to the disturbances was significantly larger

than for the flow coefficients. The PLR uncertainty was increased to 4.5%, the PRR

uncertainty was increased to 6%, and the RPR uncertainty was increased to 10%.

However, it is clear that much of this increase is solely due to the extreme case of the

swirl generator with the expander 9D upstream. It could look like these large DP ratio

uncertainty increases could adversely affect the practicality of Prognosis. However, it

will be shown in Section 4 that the DP ratios can be so greatly affected by common cone

meter malfunctions that these uncertainty limits are still very much of practical use.

When assigning diagnostic parameter uncertainties, as the cone meters are likely to be

exposed to disturbed flow, it is prudent to expand the uncertainties to account for

disturbed flow. Therefore, the prudent 4”, 0.63β cone meter parameter uncertainties are:

803.0dC , ±1% (i.e. ±x%) PLR = 0.5591, ±4.5% (i.e. ±a%)

Re)*95(211.1 EKr, ±2.5% (i.e. ±y%) PRR = 0.4409, ±6.0% (i.e. ±b%)

Re)*96.1(464.0 EKPPL, ±2.5% (i.e. ±z%) RPR = 0.7851, ±10.0% (i.e. ±c%)

Traditional & PPL Meters max % rms %7.2%5.2%1%22

Traditional & Expansion Meters max % rms %7.2%5.2%1%22

Expansion & PPL Meters max % rms, %5.3%5.2%5.2%22

Expanded calibration uncertainties allows Prognosis to account for real world installation

effects, thereby avoiding false alarms triggered by disturbed flows when the cone meters

primary flow rate prediction is still operating within the assigned uncertainty. This is of


15

help in current oil field operations – as excessive - false alarms – are nuisance alarms –

mitigating the Prognosis use in operations.

Figure 21: NDB for all data recorded from 4”, 0.63β cone meter.

Figure 21 shows the 2009 4”, 0.63β cone meter data (with expanded uncertainties)

plotted on the diagnostic NDB graph. The DP summation test, i.e. (x4 , 0), is absent from

Figure 21 as it was added to the graphical display in 2010. (During all these tests the

equation 1 DP check held true as required.) Figure 21 looks cluttered, but this is due to

multiple test data being superimposed on the NDB. In practice there are only four points

shown at any one time making the diagnostic result clear (e.g. see Figure 4).

4b. COP 2012 4”, sch 80, 0.75β Cone Meter Tests

In 2012 COP tested the 4”, sch 80, 0.75β cone meter at CEESI to investigate a higher

beta ratio cone meters level of resistance to flow disturbances. COP does not advocate

cone meter beta ratios exceeding 0.75. The meter installations are shown in Figures 22

thru 28. Again, the upstream pressure port is 1.5D downstream of the meters inlet flange

face and 2.125” (≈ 0.5D) upstream of the cone apex. Therefore, a distance of ‘0D’

corresponds to the disturbance device outlet being 2D upstream of the cone. Figure 22

shows the baseline calibration installation. The meter was then tested with the COP

chosen following installations:

Figures 23 & 24: 900 bend at 5D and 0D upstream respectively

Figure 25: Double out of plane bend (DOPB) 0D upstream

Figure 26: 3” to 4” expansion at 2D upstream

Figure 27: HMP at 5D upstream

Figure 28: 3” swirl generator upstream of a 3” to 4” expansion at 9D upstream.

The reference meter was a sonic nozzle with an uncertainty of 0.35% and a repeatability

of 0.1%. The baseline results, conducted at 20 Bara for the calibration parameters are

shown in Figures 29 & 30 with the data fit uncertainties. Figure 29 shows the flow

coefficients. A constant discharge coefficient was fitted to 0.5% uncertainty. Although

the expansion and PPL flow coefficients here could be fitted to the Reynolds number (to

the same 1.1% uncertainty as the 0.63β cone meter) constant value fits were chosen, at

1.5% uncertainty for the expansion flow coefficient and 2% uncertainty for the PPL flow

coefficient. Figure 30 shows that the DP ratios, the constant value data fits and


16

associated uncertainties. The DP ratio uncertainties are similar to the 4”, 0.63 beta ratio

cone meter (see Figure 14).

Figure 22. COP 4”, 0.75β cone meter straight pipe run (baseline) installation.

Figure 23: 900 bend at 5D upstream Figure 24: 900 bend at 0D upstream

Figure 25: DOPB 0D upstream Figure 26: 3” to 4” at 2D Upstream


17

Figure 27: HMP 5D upstream Figure 28: 3” Swirl Generator+9D up Exp

Figure 29. 4”, 0.75β cone meter baseline flow coefficient results.

Figure 30. 4”, 0.75β cone meter baseline DP ratio results.

All flow disturbance tests were conducted at 20 Bara. Figures 31, 32 & 33 show the

calibrated discharge, expansion & PPL flow coefficients across the disturbance tests.

Figure 31 shows that this cone meter is very resistant to disturbed flow. Only two

installations caused the predicted discharge coefficient to vary beyond the baseline ±0.5%

uncertainty (except marginally at the very lowest flow rate only). This is the 900 bend at


18

0D upstream installation and the swirl generator with expander 9D upstream installation.

Both installations are extreme. The first installation is a relatively common suggestion for

applications with limited straight pipe run, while the second is a very rare scenario in the

real world. The discharge coefficient for the 900 bend at 0D upstream is marginally out-

with the 1% uncertainty at the lowest flow rate tested. The swirl generator with expander

9D upstream produces a very significant shift in meter performance.

Figure 31 shows that the 4”, 0.75β cone meter with a single 900 bend at 5D had the same

performance as the baseline calibration. It therefore appears prudent to supply some

straight length pipe between a single 900 bend and a 0.75β cone meter.

It may seem surprising that the meter is immune to a DOPB at 0D but not a single bend at

0D. A DOPB is often perceived as a more extreme disturbance. However, a single 900

bend and a DOPB do not produce the same type of flow disturbance. A DOPB does not

produce a more extreme version of the disturbance induced by a single 900 bend. A

DOPB and a single bend produce different levels of asymmetric flow and swirl.

Figure 31 shows that the 0.75β meter is adversely affected by the swirl generator with

expander 9D upstream. This is the one significant difference between the two different

beta ratio cone meter test results. The 0.75β cone meter is not affected by the expansion

at 2D upstream. Therefore, the extreme swirl appears to be the issue. Whereas the

DOPB produces moderate and realistic swirl, the swirl generator’s 540 of swirl is far

more extreme than the vast majority of real world applications. It is therefore suggested

here that these results should be taken in context and not dwelled upon unduly. It appears

that a cone meter, like all flow meters, should not be used with very severe swirl flows.

It was found from the single test conducted that the 0.75β cone meter seems slightly more

resistant to the HMP than the 0.63β cone meter. A HMP 5D (i.e. gate valve at 3D)

upstream appears to have no significant adverse effect on the 0.75β cone meter. It took

the 0.63β cone meter an upstream distance from the HMP of 8.7D (i.e. a gate valve at

7D) for there to be no significant adverse effect. There isn’t enough repeat data to make

any defensible conclusions. However, it would be prudent to allow at least 7D between a

gate valve and a cone meter.

Figures 32 & 33 show the disturbance effects on the expansion and PPL flow

coefficients. As with the 0.63β cone meter, both parameters are more affected than the

discharge coefficient, but, crucially they are also both relatively immune to the

disturbances in the flow. The only major shift of flow coefficients is from the extreme

test of the swirl generator with expander 9D upstream. Ignoring this unrealistic test to

concentrate on the more realistic real world examples, the different disturbances cause

the spread of data around the expansion coefficient baseline data fit to increase from

1.5% to 3.0%, while the PPL coefficient uncertainty remains at 2.0%.

Figures 34, 35 & 36 show the PLR, PRR & RPR respectively, across all the 0.75β cone

meter extreme disturbances tested. Ignoring the unrealistic swirl generator with expander

9D upstream tests it can be seen that the flow disturbances impose a moderate increase in

the DP ratio uncertainties. The PLR and PRR uncertainties can be set to 3%, and the

RPR set to 6% uncertainty.


19

Figure 31: 4”, 0.75β cone meter disturbed flow discharge coefficient results.

Figure 32: 4”, 0.75β cone meter disturbed flow expansion coefficient results.

Figure 33. 4”, 0.75β cone meter disturbed flow PPL coefficient results


20

Figure 34. 4”, 0.75β cone meter disturbed flow PLR results.

Figure 35. 4”, 0.63β cone meter disturbed flow PRR results.

Figure 36. 4”, 0.63β cone meter disturbed flow RPR results.


21

It is worth noting that when the unrealistic swirl generator with expander 9D upstream

test data sets are removed, the two different beta ratio cone meters have very similar

diagnostic parameter uncertainties. The 4”, sch 80, 0.75 beta ratio calibration results for

the diagnostic system are:

788.0dC , ±1% (i.e. ±x%) PLR = 0.561, ±3% (i.e. ±a%)

177.1rK , ±3% (i.e. ±y%) PRR = 0.442, ±3% (i.e. ±b%)

713.0PPLK , ±2% (i.e. ±z%) RPR = 0.787, ±6% (i.e. ±c%)

Traditional & PPL Meters max % rms %3.2%2%1%22

Traditional & Expansion Meters max % rms %2.3%3%1%22

Expansion & PPL Meters max % rms, %6.3%3%2%22

Figure 37 shows, the diagnostic results when using these uncertainties with sample data

(i.e. the highest flow rates) for each flow test configuration.

Figure 37. NDB for all disturbed flow recorded from 4”, 0.75β cone meter

(except the 540 swirl upstream of a 3” to 4” expansion).

Future cone meters could be calibrated with straight pipe inlets to determine baseline

flow parameters, and then larger diagnostic parameter uncertainties can be applied if the

meter is to be in service with disturbed flow. This practice minimizes the chance of

disturbed flows which are metered correctly causing false alarms.

5. Cone Meter Performance in Abnormal Operating Conditions

Flow meters may encounter various problems during service. Using either the 4”, 0.63β

or 0.75β cone meter test results the following section gives a few examples of the

diagnostics in operation.

5a. Flow Rate Prediction Bias Due to Extremely Disturbed Flow

The flow disturbance of the swirl generator and expansion 9D upstream of the 4”, 0.75β

cone meter (see Figure 38) was so extreme it induced a significant flow rate bias of

+7.8%. Traditionally, there is no accepted method for a DP meter to self-diagnose it has a

problem. However, with this meter’s diagnostic parameter uncertainties set to the

expanded values discussed in page 21, and the standard DP summation uncertainty of 1%


22

used, Figure 38 shows Prognosis warning the operator that there is a flow meter

problem. A mid-Reynolds number of 2.17e6 is used in this example. The DP check

shows the DP readings trustworthy indicating that the problem lies with the performance

of the meter body. In this case, the flow disturbance is skewing the meter’s performance.

This is an example of the ‘found’ meter performance being the problem when compared

to the ‘expected’ performance.

Figure 38. 4”, 0.75β cone meter with extreme flow disturbance & diagnostic result.

5b. Cone Meter Performance with a Partially Blocked Minimum Flow Area

DP meter primary elements are intrusive to the flow. The cone element can act as a trap

to debris, which will cause flow metering errors. Figure 39 shows a partial blockage with

a small nut trapped by the 4”, 0.63 beta ratio cone. For realism, this blockage was

applied when the meter was installed in a typically challenging cone meter application,

i.e. a DOPB at 0D upstream and a HMP installed 2D downstream (see Figure 7). The

flow rate prediction recorded a +5% bias induced by the trapped nut. Traditionally, there

is no accepted method for a DP meter to self-diagnose it has a problem.

Figure 39: Trapped nut looking downstream & NDB diagnostic result.

Figure 39 also shows the diagnostic result for the trapped nut in this installation. The

data for a mid-range flow rate is shown. The expanded diagnostic uncertainties shown in

page 14 were used, plus the standard DP summation uncertainty of 1%. When the meter

had no malfunction, there was no diagnostic warning (see Figure 21), but with the

trapped nut causing a +5% bias the diagnostics clearly indicated a malfunction. This is


23

an example of the ‘found’ meter system performance being the problem when compared

to the ‘expected’ meter performance.

5c. DP Transmitter Problems

DP transmitters can malfunction for various reasons, including being over-ranged (i.e.

‘saturated’), drifting, or being incorrectly calibrated. An erroneous traditional DP

measurement means an erroneous flow rate prediction. Traditionally, there is no accepted

method for a DP meter to diagnose it has a DP reading problem.

Figure 40: 4”, 0.75β cone meter DOPB 0D upstream with DP saturation.

In this example, for realism regards typical cone meter installations, take the 4”, 0.75β

cone meter installed with a DOPB at 0D (see Figures 25 & 40). At 20 Bar, the air flow

Reynolds number of 22.2e6 produced a correctly measured traditional DP of 51.06”WC

(12,696 Pa), the meter predicted the correct flow rate to within 0.5%, and the diagnostic

system correctly indicated no problem existed.

As an example, consider what would have happened if the DP transmitter became

saturated at 50”WC (12,432 Pa). The DP error is approximately -2% and the

corresponding flow rate error is -1%. Figure 40 shows the diagnostic result. The

diagnostic parameter uncertainties used were those stated in page 21, plus the standard

DP summation uncertainty of 1%. Prognosis correctly shows a system malfunction. The

DP check diagnostic shows that the problem is with a DP reading by the fact that the DP

reading warning is given.

5d.1. Incorrect Geometry Inputs – Inlet Diameter

Incorrect geometry keypad entries are a relatively common problem. They produce flow

rate prediction biases. This scenario is an example of the metering systems hardware as

‘found’ operating correctly, whereas the ‘expected’ performance of the meter is

erroneous, as the flow computer calculation expects the performance of a different

geometry meter.

As cone meters are not always used in ‘tight spaces’ with disturbed flow at the inlet,

consider a Reynolds number 3.15e6 in the 4”, 0.63β cone meter straight pipe run

calibration (i.e. Figure 22). The true inlet diameter of this meter is 3.826”. However, if an

incorrect keypad entry of the inlet diameter is used - the inlet diameter of a 4”, sch 40, i.e.

4.026” - a positive flow rate prediction bias of 24.6% is induced. Figure 21 included this

test data with the correct geometry entered. Figure 41 now shows the same data when this


24

wrong inlet diameter is entered. The diagnostic parameter uncertainties used were those

stated in page 14, plus the standard DP summation uncertainty of 1%. The diagnostics

indicate a problem. The DP check shows the DP readings are trustworthy and hence, the

problem is with the meter body (which is correct as the expected and found meter

geometries are different).

Figure 41. 4”, sch 80, 0.63β cone meter

with incorrectly entered sch 40 inlet diameter value.

The 4”, 0.63β cone meter flow rate prediction bias of +24.6% induced by the use of this

+5.23% incorrect diameter could be surprising to many engineers. A 4”, 0.63β Venturi,

nozzle or orifice meter with a +5.23% incorrect diameter input has a -1.7% flow rate

prediction bias. The cone meters prediction bias is in the opposite direction and a

different order of magnitude. The reason the cone meter is far more sensitive to inlet

diameter biases is due to the difference in geometry between a cone meter and these other

DP meters, combined with how the respective geometry values are used in the flow rate

calculation.

Venturi, nozzle & orifice meter geometry is described via the inlet (D) and throat

diameter (d), i.e. by the size of the inlet and throat flow diameters. From this information

these DP meter designs product of velocity of approach (E) and throat area (At) is

calculated in the DP meter traditional flow rate equation (i.e. equation 2), as shown in

equation 2a. However, cone meter geometry is described via the inlet diameter (D) and

cone diameter (dc), i.e. by the size of the inlet flow diameter and the cone blockage

diameter. From this subtly different information the cone meters product of velocity of

approach (E) and throat area (At) is calculated in the DP meter traditional flow rate

equation as shown in equation 2b.

A consequence of this is that whereas with Venturi, nozzle & orifice meter geometries

the flow rate prediction is relatively insensitive to the inlet diameter, the cone meter flow

rate prediction is very sensitive to the inlet diameter. Figure 42 shows the percentage

flow rate prediction biases induced on Venturi, nozzle & orifice meter flow rate

predictions, and then on a cone meter flow rate predictions, for percentage diameter

biases. Note that this relationship is beta ratio dependent – Figure 42 is only applicable to

this particular 0.63 beta ratio example.


25

Figure 42. Relative Sensitivity of 4”, 0.63β DP Meter Designs to Inlet Diameter Biases.

Venturi, orifice meter: tdtdt PC

D

d

dPCEAm

2

14

24

2.

--- (2a)

Cone meter:

td

c

ctdt PC

D

d

dDPCEAm

2

11

42

42

22.

--- (2b)

It is far more critical that a cone meter operator keypad enters the precise cone meter inlet

diameter than it is for Venturi, nozzle & orifice meter dimensional keypad entries.

Surprisingly few operators of cone meters know this. However, Prognosis is capable of

automatically monitoring this issue for the operator (see Figure 41).

5d.2. Incorrect Geometry Inputs – Incorrect Cone Diameter

Cone (and all DP) meters are dependent on the throat area (At) being correctly entered

into the flow calculation software. In the case of a cone meter this means the correct

keypad entry of both the inlet diameter (D) and cone diameter (dc). For Venturi, nozzle

and orifice meters this means the correct keypad entry of only the throat diameter (d).

Let us now consider the effect of an incorrect cone diameter input.


26

The actual cone diameter of the 4”, sch 80, 0.63β cone meter is 2.998”. If. say, the cone

diameter was erroneously entered as 2.898” (i.e. a -3.3% cone diameter error) the induced

flow rate prediction bias is +11.9%. This scenario is another example of the metering

systems hardware as ‘found’ operating correctly, whereas the ‘expected’ performance of

the meter is erroneous, as the flow computer calculation expects the performance of a

different geometry meter.

Figure 43: 4”, 0.63 beta ratio cone meter with a high cone diameter of 2.631”.

Figure 43 show the diagnostic result for the Reynolds number 3.15e6 flow point

discussed in Section 5d.1. The diagnostic parameter uncertainties used were stated in

page 14, plus the standard DP summation uncertainty of 1%. The diagnostics indicate a

problem. The DP check shows the DP readings trustworthy indicating that the problem

lies with the performance of the meter body (which is correct as the expected and found

meter geometries are different).

Figure 44. Relative Sensitivity of 4”, 0.63β DP Meter Designs

to Throat Diameter Biases.

For completeness, Figure 44 shows the percentage flow rate prediction biases induced on

Venturi, nozzle & orifice meter flow rate predictions, and the cone meter flow rate


27

predictions, for their respective throat or cone diameter input percentage biases. As with

the inlet diameter case this relationship is beta ratio dependent and hence Figure 44 is

only applicable to the 0.63 beta ratio example. In this case both the cone and the other

DP meter designs are particularly sensitive to this issue, with the cone meter being

marginally more sensitive.

5e Incorrect Cone Meter Performance Parameter Keypad Entry

It is not just geometry entries that can be erroneous. Performance parameters such as the

discharge coefficient can be keypad entered incorrectly. This is another example of the

metering systems hardware as ‘found’ operating correctly, whereas the ‘expected’

performance of the meter is erroneous, as the flow computer calculation expects to see a

different meter performance.

The calibration performance parameters of the 4”, sch 80, 0.63β cone meter are shown in

page 14. The discharge coefficient is stated to be 0.803 ±1%. In straight pipe run (i.e. no

disturbed flow) the uncertainty was 0.5%. However, when this parameter is applied as a

diagnostic parameter, in order to reduce the chance of nuisance alarms, the assigned

uncertainty is expanded to 1%. Let us consider the scenario of an incorrect discharge

coefficient keypad entry of 0.83. This induces a +3.4% bias in the meter flow rate

prediction. The choice of 0.83 is not entirely random. There are two reasons for

choosing this value in this example. The first is the obvious and realistic scenario where

the operator entering the value makes the error of missing the ‘0’. The second reason is

that a prominent cone meter manufacturer’s ‘sizing program’ estimates a 4”, sch 80,

0.63β cone meters discharge coefficient to be 0.83. That is, before such a meter is

manufactured and calibrated to find the true discharge coefficient (which in this case was

found by CEESI to be 0.803) an initial estimate of 0.83 was offered. This example

therefore shows the flow rate prediction bias induced if the operator was to accept this

discharge coefficient estimate without calibrating the meter. In this case the bias is

+3.4%. Other cases can have higher or lower biases. As described by Hodges et al [7], it

is important to individually calibrate cone meters across their applications Reynolds

numbers for optimum meter performance.

Figure 45. 4”, 0.63β cone meter with erroneous Discharge Coefficient value.

Figure 45 shows the diagnostic result of this discharge coefficient keypad entry bias

when using a randomly chosen 4”, sch 80, 0.63β cone meter calibration point (at a

Reynolds number of 1.44e6) from the straight run with undisturbed flow at the meter

inlet. In this example the discharge coefficient was selected as the parameter incorrectly


28

keypad entered into the software. It is just as likely that any of the six diagnostic

parameters could be entered incorrectly. However, in all six cases the diagnostics show a

meter system problem - that is, the diagnostic system can self-diagnose its own health.

5f. Miscellaneous Comments Regards the Diagnostic Examples

The cone meter malfunction examples chosen in this paper are only a small selection of

what the diagnostic system is capable of seeing. Nevertheless, even with the small

number of examples given, it is notable that the diagnostic pattern can vary depending on

the malfunction.

DP Diagnostics has become aware that some engineers have mistakenly assumed that

Prognosis is primarily nothing more than a comparison of the in-service to found PLR

alone (i.e. y1 only), with the diagnostic checks x1, x2, x3, y2 & y3 being nothing more than

redundant and superfluous repeats. This is not true. Each of the diagnostics are valuable

in their own right. The six diagnostics x1, x2, x3, y1, y2 & y3 have different sensitivities for

different DP meter geometries exposed to different metering problems. It is therefore

incorrect to consider that y1 is the prime diagnostic with the other diagnostics being

superfluous. For example, section 5d shows that diagnostic check y1 can sometimes be

ineffective while other diagnostic checks are very effective. This is particularly true when

the problem is with the ‘expected’ performance and the meter body is serviceable. All the

diagnostics should be treated as equally relevant. Individually they are each valuable, but

when used together the whole diagnostic system is greater than the sum of its parts. The

combined diagnostics form an interwoven ‘lattice’ of diagnostics with significant strength

compared to any individual diagnostic check used in isolation.

When all the diagnostic checks are used together, when a meter malfunctions, the

resulting diagnostic pattern contains information as to what the source of the problem

could and could not be. Such information is valuable to meter operators and maintenance

crews. However, detailed discussion of cone meter diagnostic warning patterns is out-

with the scope of this paper.

Six common cone meter malfunctions were chosen as diagnostic examples. These can be

split into three groups:

DP transmitters giving an erroneous DP reading (one example),

malfunctions due to physical issues with or at the meter body, i.e. problems with

the meters actual performance as ‘found’ (two examples),

malfunctions due to the expected performance being erroneous, i.e. problems with

the meters keypad entered ‘expected’ performance (three examples).

In all cases, including when the ‘expected’ / calibrated baseline data and geometry values

were the source of the problem, the diagnostic system showed that the meters flow rate

prediction was not trustworthy. That is, it is shown that the integrity of Prognosis is not

reliant on the correctness of the calibration data and meter geometries keypad entered

into the system. Prognosis is as capable of monitoring for calibration / baseline

‘expected’ performance errors as it is for physical meter malfunctions.


29

6. Conclusions

Baseline calibration and flow disturbance tests at CEESI on 4”, sch 80, 0.63β and 0.75β

cone meters demonstrated that the cone meter is resistant to most real world flow

disturbances, at least within this meter size and beta ratio range.

The 0.63β cone meters flow rate prediction did not deviate by more than 1%

across all flow disturbances tested.

Only with the most extreme tests, that were conducted to guarantee that the very

worst of real world flow disturbances had been exceeded, did the 0.75β cone

meters flow rate prediction deviate by more than 1%.

Hence, it is concluded that cone meters with beta ratios of 0.75 are resistant to most flow

disturbances.

Whilst the 0.75β cone meter was found to be resistant – it is not entirely resistant to flow

disturbances. Some manufacturer’s early and overly optimistic claims of complete

immunity has not helped a naturally cautious industry accept that the cone meter does

indeed have an excellent, if not perfect, resistance to flow disturbances. There were three

upstream pipe work induced flow disturbances that could cause a 0.75β cone meters flow

rate prediction bias. These were:

a single 900 bend

a gate valve (50% open / closed)

extreme swirl with expansion.

Only the 0.75β meter was tested with the single 900 bend. A slightly greater than 1% flow

rate prediction bias was induced with the meter 0D downstream. Placing the meter at 5D

downstream caused this bias to disappear.

Both the 0.63β & 0.75β cone meters were tested with the Half Moon Plate (HMP)

mimicking a gate valve 50% closed. These results were not conclusive. The 0.75β meter

was immune to the disturbance created by a HMP 5D upstream (i.e. a gate valve at

approximately 3D) upstream of the meter. However, the 0.63β cone meter showed a

slight flow rate prediction bias when a HMP was 6.7D (i.e. a gate valve at approximately

5D) upstream of the meter. By 8.7D (i.e. a gate valve at approximately 7D) upstream of

the meter the bias had diminished to the border of the correctly operating meters

uncertainty. Therefore, operators should allow for at least 7D upstream of a gate valve if

they are to be assured of correct flow metering.

The 0.75β cone meter was unaffected by the 3” to 4” expansion 2D upstream of the

meter. However, the same meter produced an extreme flow rate prediction bias when it

was exposed to the very severe flow condition of extreme swirl and expansion 9D

upstream of the meter. The 0.63β cone meter faired far better in this severe test, but it still

had a small bias induced on the flow rate prediction. It is concluded that it is not

advisable to attempt to measure flow with such extreme swirl.

The DP meter diagnostic tool ‘Prognosis’ was shown to be simple and effective. The

diagnostic methods were shown to be of practical use even when the cone DP meter was


30

experiencing significant flow disturbances. Disturbed flow that does not cause a flow rate

prediction error does not cause false diagnostic alarms. Meter malfunctions do cause

diagnostic system alarms. The simple addition of a downstream pressure tapping to the

cone DP meter has produced a simple, practical and powerful tool to produce cone meter

diagnostics.

References 1. Peters RJW., Steven R., “Tests on the V-Come Flow Meter at Southwest Research

Institute® and the Utah State University in Accordance with the New API Chapter 5.7

Test Protocol”, NSFMW 2004, St Andrews, UK.

2. “Testing Protocol for Differential Pressure Measurement Devices”, API MPMS

Chapter 22.2

3. Steven R., “Diagnostic Capabilities of DP Cone Meters” International Symposium of

Fluid Flow Measurement, 2009, Anchorage, Alaska

4. Fish G. et al, “The Effects of Upstream Piping Configurations on Cone Meter and

venture Meter Discharge Coefficients”, NSFMW 2010, St Andrews, UK.

5. ISO 5167 – Part 4. “Measurement if Fluid Flow by Means of Pressure Differential

Devices Inserted In Circular Cross Sectional Conduits Running Full – Part 4: Venturi

Tubes”.

6. Steven, R., “Diagnostic Methodologies for Generic Differential Pressure Flow

Meters”, NSFMW, St Andrews, Scotland, UK, October 2008.

7. Hodges C. et al “Cone DP Meter Calibration Issues”, North Sea Flow Measurement

Workshop Oct. 2009, Tonsberg, Norway.

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Performance Improvement of Large Installation base of Wellhead Venturi Wet Gas Measurement in Petroleum Development Oman (PDO)

Abdullah Al Obaidani, Khalil Al Hanashi, Hamed Al Hadhrami, Dawood Al Sulaimani - Petroleum Development Oman

Abstract

Wellhead wet gas metering using venturi meter with over-reading correction is widely used in many gas fields including gas fields operated by Petroleum Development Oman (PDO). A large installed base of more than 300 venturi based wet gas meters delivering real time data of flow rates of gas, condensate and water are used to determine produced volumes in these gas fields and carry out well and reservoir management activities. Managing the performance of these devices require thorough understanding of fluid flow rates, composition, correction algorithm parameters and final data transfer across different systems in the data loop. With continuous changes in flow rates and process conditions, focused efforts are necessary to maintain adequate performance of these devices. Within PDO, different personnel have different role in contributing to achieving the required performance from these devices, integrated efforts and processes in capturing and updating changes are required.

This paper focuses on the experience and improvement achieved in the gas and condensate metering of one of the gas fields (referred to later as Field-A) where noticeable imbalance in the volume of hydrocarbons between the wells and export were observed since the commissioning of the Gas Plant (referred to later as Plant-A). A structured review by a multi-discipline team was initiated to focus on the whole data loop including sensor performance, parameters input, data transfer, algorithm set-up, procedures and staff competencies. The work has overall resulted in improving the fluid volume balance between wells to export. This paper also describes sensitivity of different parameters contributing to measurement error and how these could be eliminated or reduced. The paper also describes the challenges in meter sizing for changing flow profiles and depletion scenarios. Also, the challenges faced in the field in determining liquid loading (both condensate and water) using tracer dilution technique and mobile well testing units.

An overall integration between technology, people and process is detailed in this paper as a mean to realize measurement performance that meets the requirement.

1. Introduction

Managing performance of large installation base of flow sensors scattered at wells level, fields, production facilities and export network is becoming more challenging with the increasing demand from end data users for continuous and accurate quality data. The right technology for the application remains the most important first step towards realizing accurate and quality data during the operation phase. The application and its fluid data and process data need to be fully understood so the right technology and measuring method is selected. However, the selection and implementation of the right technology alone will not necessary ensure continuous accurate data is being delivered from these sensors. Correct installation, configuration, commissioning, parameters update, calibration, maintenance and performance review are essential elements. Additionally, a comprehensive set of procedures, clearly defined processes and competent multidiscipline teams are vital to ensure reviews and changes are adequate to achieve the required data quality at all times. The performance of the flow meters often are within the data users expectation when deployed in single phase streams, stable flow, known fluid properties and stable process. The performance often is impacted when other phases or impurities flow with the main phase in the same stream, especially when deployed at the upstream of the separation, processing and treatment facilities. Such streams are usually multiphase flow streams in the upstream oil

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and gas industry where conventional flow measurement techniques do not often work or require good understanding of the flow behavior, fluid characteristics and process changes. Deploying separation based methods generally solve these issues when engineered, operated and maintained adequately. However, several technical and commercial constrains play major role in deciding whether separation methods are feasible or alternative techniques should be sought. Managing gas and condensate production effectively requires continuous accurate flow data together with pressure and temperature and other key parameters. When the gas produced from the wells is dry gas, several measuring methods could be implemented with the expectation of good accuracy and performance. However, for wet gas production from the wells, continuous accurate determination of flow rates of all phases at economical level throws a challenge to flow measurement techniques. Although currently in the market there are several wet gas flow meters with good uncertainty for gas volume flow rate, often liquid volume flow rate uncertainty has relatively higher uncertainty. Furthermore, generally the costs of these devices fall outside the economic value for a low producing wet gas well.

Venturi meters behavior in measuring gas volume flow rate in a wet gas application was studied in past few decades with increasingly better understanding of parameters affecting the over-reading caused by liquid flowing with the gas. Several over-reading correction algorithms were developed to correct for these effects. Some of these algorithms use more than one differential pressure input or a combination of several differential pressure measurement input to compute the resultant over-reading and hence the corrected volume flow rate for gas and determining the liquid volume flow rates.

In gas fields operated by PDO, one of these over-reading correction algorithms is being used where it utilizes online measurement of the venturi throat differential pressure, flow line pressure, flow line temperature and several constants and operator input parameters. Although the principle generally works and delivers accuracies within the requirement, it requires several accurate input obtained by external methods. These include Condensate Gas Ratio (CGR), Water Gas Ratio (WGR), PVT data, fluid properties, etc. As these input parameters involve multiple discipline teams from petroleum technologists, engineering teams, operation teams, maintenance team, well engineering team, production chemistry personnel, real time operation systems engineers and corporate hydrocarbon accounting team, a comprehensive set of procedures, processes and clear roles are essential of the complete “system” to integrate effectively with the technology to achieve the anticipated data quality. Also, as the reservoir depletes, the flow operating envelope moves away from the flow meter designed operating envelope. Timely capturing and modifying the well head venturi wet gas flow meters for these changes is another challenge that requires adequate change process.

This paper details an improvement campaign carried out in one of these fields (refer to in this paper as Field-A), where a comprehensive study of aspects of technology, engineering, procedures, processes and people was carried out and resulted in an improved and sustained measurement performance of Field-A. The paper also highlights how a fully integrated system of technology, processes and people can deliver substantial improvements with virtually no cost.

2. Basic Wet Gas Measurement Definitions and Wet Gas Measurement Over-reading From a measurement point of view, wet gas is defined as gas streams where liquid is present. The liquid is condensate and water. Generally the wetness of the gas is described either by the term GVF (Gas Volume Fraction) or (LVF) Liquid Volume Fraction in either a fraction or more commonly as a percentage. In the 2-phase flow map (figure 1), this is referring to the bottom right area with GVFs generally above 90%. The Lockhart Martinelli (LM) parameter is a better indication of wetness of the gas for LM range of 0 > LM > 0.35. The LM parameter X is defined as:

The wet gas metering uses a single-phase gas flow metering device (eg. Orifice, venturi or cone type) where a positive bias is introduced when small volume of liquids flows with the gas. This is referred to as “over-reading”. This can be corrected using industry established correlations, or sometimes models, to ultimately calculate the gas component flow rate. These corrections can only be applied when the fluids properties and the liquid contents are well known.

Figure 1: Two-Phase Flow Map

3. Main Measurements in Field-A Field-A is one of several gas fields which produce gas and condensate to an integrated network of gas processing and condensate stabilization plants. Several gas wells are producing to Facility-A where the gas is processed and exported to the main gas line. The condensate is exported to another facility for stabilization and export. The main measured streams are: 3.1 Wellhead Wet Gas Stream

Using ISO-5167 venturi meters with throat differential pressure measurement, recovery differential pressure measurement dP2 (not used in the calculation), pressure and temperature measurements. The calculation is implemented in the DCS. The overall uncertainty requirement for each phase varies but generally better than +/- 10 – 20%.

Figure 2 - Typical Drawing of a Venturi Meter for Wet Gas Measurement in PDO

3.2 Facility Inlet Separation

The combined flow from all wells is separated in the three-phase inlet separators. Gas is measured by ultrasonic flow meter, condensate is measurement by mass flow meter and water is measurement by mass flow meter. All with required uncertainties of better than +/- 5 – 10%

3.3 Gas Export Measurement The treated and conditioned gas is exported to the main gas line and measured by an ultrasonic flow meter with required uncertainty better than +/-3%. The heavy components of the hydrocarbon in liquid form re-join the liquid processing train and are not measured separately.

3.4 Condensate Export Measurement Unstabilized condensate is exported to the condensate processing and stabilization facility in different processing plant. This stream is measured by a mass flow meter with required uncertainty of better than +/- 3%. The water is also joining the same stream and measured as gross liquid using the same mass flow meter and a dedicated water cut meter.

3.5 Fuel Gas Part of the treated gas, prior to export, is used as fuel gas and is measured using an orifice flow meter with required uncertainty of better than +/- 5 - 10%.

3.6 Flare Gas All flare lines are measured, although very small quantities, by ultrasonic flow meters with required uncertainty of better than +/- 5 - 10%.

3.7 Gas Volume Reconciliation and Back-Allocation The gas is reconciled and back allocated from the fiscal metering systems at final customer end (uncertainty +/- 0.5 – 1 %) back to all gas export streams then back allocated to individual wells in production volumes proportional basis. The flashed gas in the condensate stabilization facility is back allocated using algorithms computed considering compositions and flow rates of condensate from different fields.

3.8 Condensate Volume Reconciliation and Back-Allocation The condensate is reconciled and back allocated from the final condensate allocation meter after the condensate stabilization facility measured by ultrasonic flow meter with required uncertainty of +/- 1%. These volumes are then reconciled with each facility condensate stream then allocated proportional to the volumes produced.

4. Wellhead Venturi Wet Gas Measurement (WGM) Setup The measurements at gas wellhead are connected to Remote Terminal Unit (RTU) which connected via fiber optic to DCS system at Facility-A. The intermediate parameters used for the over-reading correction are computed and displayed as a final output for wet gas volume flow rate, dry gas volume flow rate, condensate volume flow rate and water volume flow rate. 4.1 Key Wet Gas Measurement (WGM) fixed Input

Line dimensions, typically 4 inch, 6 inch and 8 inch flow line pressure rating of 800#.

Venturi dimensions, internal diameter D, throat diameter d and discharge coefficient Cd.

Venturi dimensions reference temperature.

4.2 Key Wet Gas Measurement (WGM) Variable Inputs - Fluids

Gas Specific Gravity at standard conditions.

Gas Molecular Weight at standard conditions.

Pseudo Critical Pressure and Temperature.

Ratio of Specific Heat.

Condensate Density at standard conditions.

Condensate Molecular Weight at standard conditions.

Water Density at standard conditions.

4.3 Key Wet Gas Measurement (WGM) Variable Inputs – Liquid Ratios

Condensate Gas Ratio (CGR) at standard conditions.

Water Gas Ratio (WGR) at standard conditions.

4.4 Key Wet Gas Measurement (WGM) – PVT Data

Gas density polynomial coefficients for range of operating Pressures and Temperatures.

Gas Molecular Weight linear coefficients for range of operating Pressures and Temperatures.

Gas Pseudo Critical Pressure and Temperature linear coefficients for ranges of pressures and temperatures.

Condensate density polynomial coefficients for range of operating Pressures and Temperatures.

Condensate Molecular Weight linear coefficients for range of operating Pressures and Temperatures.

4.5 Measured Variables

Venturi throat differential pressure.

Venturi recovery differential pressure (not used in the computation).

Upstream pressure.

Flow line temperature

Figure 3: Typical Wet Gas Measurement (WGM) System Overview and Data Flow

5. Filed-A Wellhead Measurement Historical Performance Since commissioning of Facility-A together with introduction of wet gas production from Field-A wells, it was observed that there is noticeable gap between the volumes of gas and condensate reported by wellhead venturi wet gas meters and the final export meter measured volumes. An imbalance of 40-50% was consistence but with a slightly declining trend.

Due to this imbalance, the monthly volumes reconciliation factors of Field-A has been low for quite some time distributing the imbalance proportional to the volumes produced from each well.

Figure 4: Field-A Gas and Condensate Reconciliation Factors Historical Trends

6. Improvement Methodology Since the final data delivered from a set of tens of measurement points within Field-A, Facility-A and other facilities in the production network system; it was necessary to get key parties involved from subsurface team, engineering team, operation and maintenance teams, hydrocarbon accounting team, metering specialists, production chemistry, real time operation, DCS engineers and process engineers to ensure the whole chain of measurement and data transfer if healthy and to capture where issues are and how they can be fixed. 6.1 Teams Involved

Below is a short list of some activities contributing to the measurement performance of the venturi based wet gas measurement managed by each team involved.

6.1.1 Well and Reservoir Management Team

The team manages the performance of the reservoirs contributing to the production of Field-A, obtaining updated CGR and WGR for each test period for each well and manages PVT data relevant to Field-A.

6.1.2 Process and Concept Engineering Team

The team support in PVT simulation, carry out flash calculations of condensate and supply initial fluid properties in different stage of the process. In addition, the team does conceptual studies for new metering requirement at wellheads, facilities and export systems and reviews metering upgrade requirements for the future depletion scenarios and production profiles.

6.1.3 Design Team

The team carries out all design activities including sizing the suitable size meters for forecasted production profile, procuring, inspecting and testing the new meters. Also supplying all configuration documents, drawings, data sheets, construction drawings, spares, etc. In addition, managing all related activities relevant to connecting the measurement across all links to the hydrocarbon accounting system and maintenance management system.

6.1.4 Construction Team

The team is responsible to ensure the meters and supporting instrumentation is constructed as per the approved for construction drawings, carry out loop checks, calibration of instruments, configuration, testing and pre-commissioning activities.

6.1.5 Commissioning Team

The team ensures the hydrocarbons are measured and data are available across all systems up to the hydrocarbon accounting system. Also, resolves any calibration, communication, configuration issues and prepare to handover to operations.

6.1.6 Operations Team

The team does checks on the measurement and accepts new meters installations. During operation they monitor and report the volumes measured should the automatic transfer has issues. Adjust choke size and update the CGR and WGR values in the correction algorithm. Also, reports any failures or issues in the measurement.

6.1.7 Site Maintenance and Calibration Team

The team is directly involved in executing the planned maintenance and calibration activities, keeps records of calibrations, update in the maintenance management system and carryout corrective and troubleshooting activities when required. In addition, team highlights any improvement required through a facility change proposal process. The maintenance team also gets involved during commissioning of new meter installations as part of the handover and acceptance stage.

6.1.8 Hydrocarbon Accounting Team

This team monitors wells production, plan well testing, validate well testing data, update CGR and WGR values in the hydrocarbon accounting system and optimize overall condensate and gas production to meet customers plans. Also, the team raises any potential mis-measurement issues to maintenance support team for corrective actions and troubleshooting. In addition, the team incorporates new fields and facilities in the allocation structure.

6.1.9 Metering Maintenance Support Team

The team involves in metering reviews, new metering projects as operation/maintenance input, participate in design reviews, testing and inspections of new meters and during integration activities. Also, the team maintains an overview of metering issues and interfaces with projects, operation and site maintenance teams and follow up actions implementation. The team provides second line maintenance support to the site metering maintenance and calibration team and provides coaching to new metering technicians.

6.1.10 Systems (DCS) Support Team The team configures the wet gas measurement calculation blocks in DCS, creates all relevant DCS tags, creates and modifies WGM DCS graphics and reports and adds new WGMs of new wells. The team also interfaces with real time operation team for data connectivity and reprogram WGMs from Metering Specialist team requests.

6.1.11 Real Time Operations Team (RTO) The RTO team manages all data connectivity from DCS and other systems in the field to the data historian, production portal and the automatic transfer to the hydrocarbon accounting system. The team also configures automatic reports of all production data from WGMs and facilities.

6.1.12 Metering Specialist and Support Team

The team sets the metering strategies, evaluates metering technologies, the owner of all metering related procedures and processes and is the technical authority for metering across all phases and

stages of projects and operations. The team also conducts metering audits, leads metering improvement initiatives and develops metering staff capabilities and competencies.

6.1.13 Production Chemistry Team

The team supports metering systems by providing fluid properties information, takes fluid verification samples, carries out fluid sample analysis and maintains database of all fluid sample results.

6.1.14 Information Technology Team The IT team configures the hydrocarbon accounting system for any new stream, upgrades or modifications and interfaces with real time operations team in few aspects of the automatic data transfer and RTO applications.

6.1.15 Technology Implementation Team The team updates the current and future challenges in metering and interface with stakeholders and the metering Specialist Support team to develop and update metering technology roadmap. The team provides corporate support for evaluation and introduction of new metering technologies.

6.2 The WGM Improvement Team

A team was formed consists of members from WRM, process engineering, hydrocarbon accounting, site maintenance, metering maintenance support, RTO, DCS systems and lead by a Metering Specialist to “dive-in” the whole metering network of Field-A and Facility-A together with a comprehensive focus to the complete gas and condensate network in all other facilities and fields. A Steering Committee was formed to provide support and facilitate the mission of the improvement team. The Steering Committee team consists of the Petroleum Manager, Engineering Manager, Operations Manager and the Automation Manager. The role of the Steering Committee was very evident in facilitating the implementation of the identified issues and proposed solutions. A monthly meeting with the Steering Committee was organized to go through findings, progress and to clear any obstacles that may hinder the progress.

6.3 The Methodology The improvement team developed a methodology to be used during the improvement campaign that engages stakeholders, evaluates current key measurements, substantiates causes of mis-measurement, quantity measurement errors and communicate closely with the Steering Committee. 6.3.1 Stakeholders Engagement Workshop

A workshop prior to start any physical work was conducted. Representatives from different teams and disciplines participated to pin exactly the current symptoms and problem areas, focus on different measurement segments and propose practical time and cost effective solutions.

6.3.2 Existing Measurement Issues The team then listed and prioritized the measurement and potential impact on the wet gas measurement and hence the reconciliation factors. The issues and solutions were categorized as quick-fix, medium term or sustainability solution.

6.3.3 Measurement Issues Substantiation The team then zoomed in all contributors of potential mis-measurements in the WGM, facility and export metering systems. Derived a focused plan towards addressing the high potential ones and picked sample WGMs for error quantification.

6.3.4 Measurement Errors Quantification

The team started first with the simple checks that may heavily impact the measurement. Examples of these are configuration. Data flow across systems. Totalizes and compensations health checks, PVT and fluid data and calibration of selected WGM dP transmitters. Then these were used in off-line WGM simulation tool to quantify measurement errors associated with main aspects of the WGM measurement.

6.3.5 Communication and Steer All findings, errors and proposals were communicated to the Steering Committee for implementation. Informing all stakeholders that there will be a change happening at certain defined result and has an impact on the measurement within approximately the quantified errors. This step was necessary to get stakeholders onboard and prepared for the change. Also, it served as obtaining agreement for higher management on the change and providing the required support to execute the recommendations.

6.3.6 Implementation All activities are implemented by the authorized teams and documentation is updated. DCS graphics and measurement tags are modified and the whole chain of data transfer is checked up to the hydrocarbon accounting system.

6.3.7 Review Following the implementation, the new measurement trend is monitored against the expected change from the off-line simulation then accepted by WRM and operations team. The same steps then repeated for all WGM, facility and export metering systems. Proposals for upgrades, engineering changes, procedure improvement, competency and development of staff are presented and planned as sustainability activities.

7. Stakeholders Engagement Workshop Outcome The workshop was an essential stage of the whole process, the improvement team considers it as one of the most important stages to capture issues, understand the priorities, manage the expectation of different teams and individuals involved, and to ensure consensus and commitment among stakeholders to contribute and support the improvement team. Attendees reflected representation from all involved team. The workshop was opened by the Steering Committee which has injected the required importance and urgency to the campaign. The workshop was facilitated by an independent staff who is not involved in measurement or a data user but is skilled in facilitation; this added more effective structure to the workshop and maximized efficiency during discussions and suggestions. The issues were categorized in four main focus themes: Wells & Well Testing, wellhead wet gas measurement (WGM), Facility Measurement and Reconciliation Process.

7.1 Theme 1: Wells and Well Testing

This theme focused on the adequacy of wells flow rate monitoring, forecast data, initiation process for wellhead meters change or parameters update, well testing methods used to obtain CGR and WGR values, PVT data and other key relevant aspects.

7.1.1 Well Monitoring

The main aspect is to realize timely based on meter design data, fluid properties and forecasted flow rates that a meter change will be required and to plan for it. As often the change process takes sometimes a year or more, it was evident that to manage this aspect an efficient process or tool was necessary to notify data user that a change may be required for a particular meter well in advance. Although Field-A wells flow rates were within the operating window of the wellhead venturi meters,

it was necessary to capture this process or tool for future and for other fields which may require meter change sooner than Field-A.

7.1.2 Well Testing Facility-A is equipped with a three-phase test separator where wells can be tested at certain pre-defined frequencies. Although the separator and the associated measurements were healthy, the separator operating conditions were at high pressures at temperatures relative to stock tank conditions. The correction to stock tank conditions was not readily available and needed to be established and programmed in the DCS. The separator often was not available due to other operational consideration and was used less frequent for well testing. Mobile well testing units based on two-phase separation, or mobile wet gas meters based on multiphase flow measurement principle often were utilised. The performance of these units were generally satisfactory but often liquids uncertainty at lower liquid contents was impacting the accuracy of CGR and WGR values which are part of the wellhead wet gas venturi meters over-reading correction algorithm. Tracer dilution technique was extensively used in other gas fields but with limited success due to low flow rates, issues of mixing, tracer injection flow rate stability, sample extraction method, sample handling, accounting and re-calculated concentrations due to condensate shrinkage and other issues related sample point frequent blockage. It was suggested to evaluate the tracer dilution technique for Filed-A and assess applicability.

7.2 Theme 2: Wellhead Wet Gas Measurement (WGM) This theme focused more on the reliability and accuracy of measurement obtained for the wellhead wet gas venturi meters and all supporting parameters and processes. These included meters design and configuration, calibration, fluid updates, PVT coefficients, algorithm settings, process of updating CGR and WGR, maintenance procedures and awareness of WGMs among data users. The meters selection, design, sizing and commissioning stages were also highlighted. Issues and solutions were discussed and prioritized, ranked and agreed to focus on the high value gain ones.

7.3 Theme 3: Facility Measurement Although the facility measurement has relatively less impact on Field-A wells allocated volumes, they were factors associated with accuracy of BS&W measurements and export measurement. Although they were later ranked lower in priority due to minor impact, they were checked and verified during the improvement campaign. This theme also focused on bulk line measurement which in some streams the volumes are determined by difference. Recommendations were made to install few additional measurements at bulk lines.

7.4 Theme 4: The Reconciliation Process Field-A and Facility-A are part of a larger network of fields and facilities where volumes are reconciled in the reconciliation process. The complex reconciliation process was discussed and an agreement was obtained to evaluate the adequacy of the implemented process given that more fields were introduced since the process was initially introduced. This has resulted in a standalone focused activity to review the reconciliation process for current and future operations and also the associated measurements. Condensate flashed gas distribution to different contributors was also highlighted to be assessed.

Table 1: Stakeholders Engagement Workshop Key Focused Areas

8. Main Findings in Wet Gas Measurement (WGM) The improvement team, with close collaboration with different teams and disciplines, managed to gather key data and information related to WGM meters, facilities and export metering systems. It was found that most causes of errors were in the venturi wet gas meters data and data transfer. Additionally few facilities meters and condensate flash algorithm required minor improvements. 8.1 WGM Measurement Improvement

Reference to Figure 3, all aspects of WGM measurement, up to the hydrocarbon accounting system, were checked. Figure 5 shows a summary overview of main errors contributors.

SN Issues / Concern Priority Proposed Solution /Suggestion

Proposed

Action Party

1

Rapid well decline (outside meter

envelope) 3

Predict time of change of meter (display live

operating envelope) Team A

2 Commingling wells 2 Adhere to WRM strategy Team A

3 Knowing reservoir split 2 Increase PLT acquisition frequency Team A

4 Forecasting correct decline 3

Provide and communicate forecast to

engineers and project teams Team B

5 Frequency of well testing 2

Provide and utilise permanent well testing

facilities Team B

6 Timely fixing malfuncted sensors 1 Review adequacy of manpower levels Team E

7 Testing wells at line conditions/separator 1 5 wells trials at flash condition Team C

8 Communication (well to DCS) 1 Continue improving the remaining 10% Team D

9 Data freezing 1 Continue improving Team F

10 Accuracy of CGR and WGR 2

Assess current and alternative testing

methods Team B

11

Meters operated outside operating

envelope 1 Replace venturi meters Team D

12

WGM algorithm transparency and

parameters configuration 1

Conduct full review and health check (define

single point responsibility) Team D

13 Maintenance of venturi meters 2

Review maintenance strategy and how issues

are reported to RTO support team Team D

14 Update of CGR and WGR in EC and WGM 2 Define focal point for each cluster Team C

15

Methodology/technology of determining

CGR and WGR 3

Review tracer technique

Re-design sample/injection points Team B

16

Who's the owner and what's the process

of update 1 Come up with procedure/process Team D

17 BS&W Measurement 3

Review all BS&W measurement and come up

with plan Team D

18 No bulk meters for key streams 2 Consider meter installation Team G

19 Snap shot measurement to EC 1 Configure average/totalised measurement Team D

20

Volume based allocation (impact of multi-

processing on individual fields

condensate) 3

Conduct pilot study of mass reconciliation and

Hysis modelling to assess the impact for

individual field Team B

21

Manual update of the reading (not

relaying on meters) 2 Improve WGM and communication Team C

22 Flash gas calculation 3 To carry out simulation Team C

23

Awareness of allocation structure and wet

gas metering 2 Conduct road show/engagement/training

Team C

Team D

24

Methodology of allocation in absence of

meters readings 3 Develop methodology/procedure

Team C

Team D

25 Condensate export meter not fiscalised 3 Fiscalise export meter Team G

Figure 5: Field-A WGM Measurement Error Contributors

8.1.1 Parameters Configuration

This refers to the configured line and venturi data, transmitter’s ranges, scaling in the system, square root extraction and other related configuration. Mismatch was found between the latest data sheets, venture site information and configuration. This was mainly due to the requirement of multiple size venturi meters with different beta ratios to cater for the different project flow rate from each well. An example of this is a venturi-A details was configured as venturi-B details in the assigned well where physically venturi-A is installed. Another example is a double square root implementation is the transmitter and in the algorithm in DCS. Below table show sensitive of these combinations for two of the checked WGMs.

Actual

Bore Configured

Bore Tx Range

(Field) Tx Range

(Data Sheet) SQRT in

Tx SQRT in

DCS Error

%

Meter-A 79mm 136mm 25 KPa 20 KPa Yes Yes +43.7%

Meter-B 88mm 136mm 25 KPa 25 KPa Yes Yes +28.2%

Meter-C 79mm 79mm 20 KPa 25 KPa No Yes +11.8%

Table 2: Example of some parameters configuration mismatch

8.1.2 CGR and WGR Input Parameters These parameters are basically representing the liquid content as input for over-reading correction. The initial values of CGR were predicted lower than actual and updated values were not implemented across all systems in the data transfer loop. The actual CGRs and WGRs were almost twice as much which have resulted in more over-reading and hence should have more over-reading correction. Since the algorithm was configured with lower values, the over-reading correction was less and resulted in a net positive measurement error.

Figure 6: CGR Configuration Effect on WGM Measurement

8.1.3 PVT Data and Coefficients Since the measurement at the venturi wet gas meter is at high pressure and high temperature, the conversion to stock tank conditions of 1.01325 bara and 15 degree Celsius is obtained through deriving PVT coefficients for a set of supplied PVT data and programmed in DCS as part of the WGM calculations. The improvement team obtained new PVT data set and run new PVT coefficients. Small differences were apparent and overall had a calculated impact of around +9%. The new updated PVT data set indicated slightly heavier gas and lighter condensate compared to the PVT data set coefficients configured in the algorithm.

Figure 7: Example of Configured vs Updated Data of Gas Properties

Figure 8: Example of Configured vs Updated Data of Condensate Properties

8.1.4 WGM Configuration The WGM algorithm main calculation is the over-reading correction and also correction due to PVT effects. Additionally, the algorithm has a built-in logic with limits of flow line temperature, flowline pressure, differential pressure, subsurface safety valve position which forces the output to zero if those limits are crossed. These caused the totalizers to totalize zero values and results in less volume reported although the well is flowing during that period. These settings required review and update since reservoir pressure has depleted and flowline temperatures has decreases over the years.

Figure 9: Example of Some Issues in WGM Configuration Logic limits were reviewed and updated reflecting new operating conditions while ensuring the validity and applicability of the algorithms for these conditions. A future review will be required should the operating conditions fall close to the updated limits which may require algorithm update of alternative hardware or technology.

8.1.5 Condensate Flashed Gas Measurement The flashed gas from condensate in the condensate stabilization plant is allocated back to all contributing field based on volumes-composition ratios. Although the measurement at flashed gas is adequate and the volumes compared to the total gas production is minor, the flashed gas was slightly over-allocated to Field-A. Updated fluid data and simulations concluded that less that -1% impact on Field-A reconciliation factor.

8.1.6 Venturi Discharge Coefficient (Cd) The discharge coefficients used in the WGM is as supplied by the venturi meters suppliers of a default of 0.995. As the uncertainty requirement for this application is +/- 10-20%, a venturi Cd calibration has not been considered as Reynolds numbers are above 500,000. An estimated maximum unconfirmed uncertainty of +/-3% was used to establish potential impact on the reconciliation factor.

8.2 Data Flow Across Systems The final data output from the WGM in DCS has three forms, instantaneous readings, accumulator reading and yesterday volume representing the 24 hour volume. These are tags created in DCS for each WGM representing wet gas, dry gas, condensate and water volume flow rates. The configuration to the data historian, production portal and to the hydrocarbon accounting system is vital for automatic data transfer. Alternatively, a lot of manual entry of these figures will be required to the hydrocarbon accounting system which requires a lot of efforts and may be prompt to human errors. In some WGMs, totalizers and the 24h volume tags were not available and only the instantaneous tag was configured across the systems. Other examples where different tag numbers are mapped (wet gas volume rate instead of dry gas volume rate). As the 24h volume is captured for readings from 10:00 the previous day to 10:00 the current day, the data is stored in the data historian and then read at a later time (typically 30 to 90 minutes later) by the production portal and transferred automatically to the hydrocarbon accounting system. Some data freezes due to communication issues where the automatic transfer then reads incorrect values and download them to the hydrocarbon accounting system.

Figure 10: Example of WGM Measurement Errors during the Automatic Transfer Process A thorough review was conducted in all WGMs and facility tags, missing tags were created and tags configurations across systems were re-mapped. Descriptions in the data historian were standardized and updated in the automatic transfer in the production portal was carried out.

9. Overall Performance Improvement Implementation of the identified key issues has immediately resulted in considerable improvement. Although no hardware were changed, no vendor was called in and virtually at no additional costs, significant improvement was realized in both gas and condensate reconciliation factors for Field-A. In addition, automatic transfer of data has improved where the load on operations staff in manually entering the data in the hydrocarbon accounting system was significantly reduced. 9.1 Reconciliation Factor Improvement

The gas reconciliation factor has improved and sustained at a factor of 1.03 – 1.07 indicating an imbalance of some 3% - 7% compared to an imbalance of 40% - 50%. In addition, wells flow rates were more accurately determined on a day to day basis rather than only monthly volumes. The same campaign was carried out using the same methodology in Field-B and subsequently in all other fields and resulted in various improvements in the reconciliation factors of gas and condensate.

9.2 Automatic Transfer Improvement Another important measure to the success of the improvement campaign was the percentage of automatic data transfer against manual data transfer. This gives an indication of both data quality and reliability of the whole data loop from sensor up to the hydrocarbon accounting system. Also it indicates end users acceptance to the data. An improvement from some 10% automatic data transfer to above 90% automatic data transfer.

Figure 10: Improvement Achieved in Gas Reconciliation Factors and Automatic Data Transfer

10. Sustainability Activities Having addressed the measurement issues and realized improvements in both key elements of reconciliation factors and automatic data transfer, the focus was given to sustainability activities which will ensure continuity of the realized improvement. These were focused on staff awareness, engagement, coaching, training and competency. Also, it gave focus to processes and procedures which are key to ensure a complete link between technology and people.

10.1 People Road shows, awareness sessions, training on wet gas measurement relevant to these fields were conducted both at head offices and sites covering all teams involved in delivering quality and reliable data from sensors to the hydrocarbon accounting system.

10.2 Processes and Procedures Available related processes and procedures were reviewed and updated. Roles and Responsibilities were updated and WGM was included in the annual well reviews.

Table 3: Example of Roles and Responsibilities Matrix for Different Disciplines Involved in the WGM.

Activity Name

Rese

rvo

ir En

gin

eer

Co

ncep

t/Pro

cess E

ng

ineer

Pro

ject E

ng

ineer

Co

nstru

ctio

n E

ng

ineer

Co

mm

ission

ing

En

gin

eer

Asse

t Mete

ring

Fo

cal P

oin

t

Pro

gra

mm

er/H

CA

Pro

du

ctio

n M

easu

rem

en

t Sp

ecia

list

Sy

stem

En

gin

eer

RT

O E

ng

ineer

Mete

ring

an

d Q

MI su

perv

isor

Pro

du

ctio

n C

ord

inato

r/sup

erv

isor

Pro

du

ctio

n O

pera

tor

1 Define requirement for new WGM R A C/I C/I C/I

2 Provide initial forecaste data (flow, P, T, PVT, CGR, WGR, etc.) R A C/I C/I C/I

3 Confirm data against WGM working limits A R C/I R

4 Select the appropriate WGM system C/I A R C/I R C/I C/I

5 Complete process data sheet R R A C/I C/I C/I

6 Perform initial sizing of the venturi meter and TX ranges A R R

7 Produce specification & Instrument data sheet A C/I C/I

8 Review & approve vendor submittion and final venturi drawing and sizing A R R

9 Complete WGM Algorithm parametrs list R R A C/I R C/I

10 Complete WGM flow (totalisers and 24-hours volumes) tags list for RTU/DCS A C/I C/I R C/I

11 Install venturi as per design drawing (attention to be given to straight length and tapping orientation) A R C/I C/I C/I

12 Configure WGM with the parameters as per list in Activity 9 A R C/I C/I R C/I

13 Configure WGM flow tags (totalisers and 24-hours volumes) as per tags list in Activity 10 A R C/I C/I R C/I C/I

14 Calibrate WGM Tx ranges as per instrument data sheets (field and systems) A R C/I

15 Produce list of tags required to be in PI, shurroq/Nibras and EC C/I A C/I R C/I C/I C/I

16 Configure tags list (Activity 15) in PI, shurooq/Nibras and EC A C/I R C/I C/I R C/I

17 Configure SAP maintenece plan A R C/I C/I

18 Confirm venturi installation as per design and transmitter ranges are calibrated as per the instument data

sheetsA R C/I

19

Confirm that the well head instrument parametrs in the WGM are matching with the physically installed

venturi & Txs and their data sheets AR C/I C/I

20 Confirm WGM parameters configuration and tags are in line with list in (Activity 9) A R C/I C/I

21 Confirm that all relevant tags listed (in Activity 10) are availavle and working A R R C/I

22 Confirm that correct WGM tags are available in PI, shurooq/Nibras and EC A C/I R C/I C/I R C/I

23 Confirm that overall functionality of the WGM is healthy A R C/I C/I R C/I

24 Obtain a copy of the WGM final configuration and all instrument and venturi data sheets A R C/I C/I R C/I

25 Initiate WGM parametrs update A R R R R R R R

26 Provide updated CGR, WGR and other fluid properties (eg. PVT) A C/I C/I C/I C/I C/I C/I C/I

27 Calculate New PVT coefficients based on new PVT data A R C/I R C/I C/I

28 Update new CGR and WGR values in DCS/RTU C/I C/I A R C/I R

29 Update new CGR and WGR values in EC C/I A C/I C/I

30 Update PVT coefficients and fluid properties in DCS/RTU C/I A C/I C/I C/I R C/I C/I

31 Calibrate WGM well head transmitters C/I C/I C/I A C/I C/I

32 Review well production profile and forecast against WGM operating envelope R C/I A C/I C/I C/I R

33 Raise FCP for a WGM change C/I R A C/I R C/I C/I

34 Review new WGM Technology and working limts C/I C/I R C/I A C/I

A Implies the Accountable - sign off end product / deliverables (Owner)

R Implies Discipline input (Responsible)

C/I Implies a contributor to the product / deliverables (Consulted /Informed)

WGM Review

WGM Selection

WGM Design, Sizing and Configuration

WGM comissioning

WGM operation, maintenance and update

WGM Construction

11. Conclusion New technologies in measurement play an important role to achieve accurate and reliable measurement for the required applications. An equally important element is how these technologies are implemented, kept in healthy condition, reviewed regularly, the required parameters are updated and measurement performance s reviewed. In Oil and Gas industry, especially in large fields operations where multiple disciplines and teams involved in delivering quality data, it is essential to establish comprehensive set of processes and procedures that governs measurement performance. A thorough system of the right technology for the application, competent personnel who are kept engaged and communicated with and adequate processes and procedures that brings technology and people together will ensure that the required measurement performance is achieved. This improvement campaign has demonstrated that measurement improvement can be achieved through people and process aligned with the implemented technology. A structured methodology form engagement of key teams, supported by Steering Committee of key management personnel, proper evaluation and quantification of measurement errors prior to implementation and managing data users expectation were key aspects of the campaign success. Eventually, a complete synchronization of the three elements of technology, people and process complemented with regular reviews will be the input to explore for new technologies which can deliver more and maximizes measurement value.

12. Acknowledgement The author thanks the co-authors, the improvement team members, the Steering Committee, the stakeholders’ engagement workshop participants and all personnel who have provided support to this campaign. With their commitment and support this improvement has been realized. Thank you all.

fltough

Stamp

Proposal for the Presentation of a paper at the 2013 North Sea Flow Measurement Workshop:

The Application of Clamp-On Ultrasonic Flowmeters in Production Monitoring

An oral presentation.

Authors:

Theo Warmenhoven, GDF SUEZ E&P, Nederland B.V.

Tel: +31 223 63 96 39; [email protected]

Bernhard Funck, Flexim GmbH, Berlin, Germany

Tel: +49 30 93 66 76 912; [email protected]

Peter Liptrot, Flexim GmbH, Berlin, Germany

Tel: +49 30 93 66 76 979; [email protected]

Abstract

Ultrasonic technologies find many applications in measurement. Transit time techniques find favour in

flow measurement and are used in a variety of flowmeters. The performance of these flowmeters is now

well understood, and tests of their performance in challenging applications such as wet gas have

produced useful results.

Gas flow measurement offshore is typically carried out at an export metering point with well allocation

according to calculation. Upstream conditions often combine the challenges of very thick-walled pipes

in exotic materials with wet gas at relatively low pressures. In the last few years non-invasive (clamp-

on) ultrasonic flowmeters have been installed on individual well flowlines in gas production systems on

2 offshore installations operated by GDF SUEZ E&P Nederland B.V. Reconciliation with facility sales gas

metering is used to validate the wellhead measurements.

This paper considers the performance of these systems with GDF SUEZ E&P in the Dutch Sector of the

North Sea. The paper presents data from the incoming flowlines to a separator and compares these

with the gas measurement downstream of the separator. It also considers the operation of clamp-on

ultrasonic measurement used for allocation between different gas fields supplying a single installation.

Best practice for installation and operation on thick-walled DUPLEX pipes is reviewed and the

reconciliation philosophy considered.

The conclusion of the paper suggests that it is feasible to deploy a measurement system giving

production rates of individual wells using non-intrusive ultrasonic metering and how this system will

endure deployment in offshore environments. Recommendations for measurement of wet gas and

depleted wells are given and considered in the light of field experience and flow laboratory tests.

fltough

Stamp

1 Ultrasonic Gas Measurement Measurement of gas flow rates using ultrasonic transit time meters is an established technique. In

recent years the use of non-invasive (clamp on) flow meters has gained ground. There are, of course,

limitations in such measurement, which we will explore a little more.

Transit time flow measurement relies on contra-propagating transmission of sound bursts between pairs

of transducers situated diagonally across the pipe. The upstream signal is delayed and the downstream

signal is speeded up by the moving fluid. The difference in transit time is proportional to the flow.

Figure 1 - Transit time measurement

Figure 1 gives an indication of some of the challenges in clamp-on systems: the multiple reflections and

refractions of the ultrasonic signal can be seen.

The actual flow rate is defined by the equation:

Where Q is the volumetric flow rate

KRe is the Reynolds number correction factor

A is the cross-sectional area of the pipe

Kᾳ is the transducer correction factor

Δt is the difference between upstream and downstream times of flight

Tfl is the (average) time of flight

Re2 fl

tQ K A K

t

Naturally successful measurement relies on both the successful transmission of the signals and a

sufficient time difference between upstream and downstream propagation times to allow an accurate

calculation.

Non-invasive systems, where the transducers are external to the pipe, have been increasingly used in

gas measurement applications. The advantages of such systems are clear; the installation can be carried

out on existing systems without interrupting operation, and the use of such systems offers significant

savings in installation and maintenance costs. The deployment of clamp on systems removes at least 2

flange (or weld) connections and removes the need for by-pass and valve systems normally required to

allow maintenance of spool piece meters.

The drawbacks of clamp-on systems are that the propagated system must pass through two pipe walls

between transducers. Signal attenuation is no longer limited to the fluid, now there are significant extra

potential signal losses.

2 Ultrasonic Transmission The principal challenge in clamp-on ultrasonic measurement is to align the transducers to maximize

signal transmission between them. The application of Snell’s law of refraction is the first consideration.

Figure 2 - Snell's law of diffraction

The design of clamp-on flow measurement systems allows variable configurations in signal propagation,

with the possibility of single or multi-path transmission. The design of most systems usually includes the

transducers as separate units. In this way they can be located in different relative positions. This is

useful where conditions provide strong signals and high amplitudes – increasing path numbers may be

used to improve performance. Implicit in the application of Snell’s law is the required spacing between

transducers. The decision as to the number of paths to use is based on a number of factors, the most

important of which is the likely signal losses in the transfer media, these being the pipe and the moving

fluid.

sin sin sin

c cc

The proper installation of a non-invasive system requires the detailed input of the measured fluid and

pipe data to enable the correct spacing and orientation of the component parts of the system to be

determined. Most manufacturers of such systems will include some functionality to estimate the signal

loss in the measurement. It is important to understand the potential signal losses at each surface the

ultrasonic signal will propagate across, and to understand the attenuation of the signal in the flowing

fluid at the actual process conditions. If these values are understood then it is possible to estimate the

number of paths which will offer the most reliable, and most accurate, measurement.

3 Signal Attenuation The principal challenge in ultrasonic systems of this nature is signal attenuation. Ultrasonic transmission

is a function of acoustic impedance, which is the product of the particular materials’ speed of sound and

density.

3.1 Acoustic impedance:

The acoustic impedance (Z) of any material is given as the product of the density (ρ) and the speed of

sound (V) in the material.

Z =ρV

Internal Reflection =( (Z2 – Z1) / (Z2+Z1))2

The actual transmission rate is 1-reflection.

As may be seen, the greater the difference in acoustic impedance, the greater the transmission losses.

Early work in clamp on systems was successful in liquid measurement. Taking water as the bench mark

we still see approximately 90% of the incident signal reflected as the ultrasonic pulse passes from a steel

pipe wall and water. If we then consider gas measurement, even at elevated pressures, we are dealing

with signal losses of above 99% across the system boundaries. (In gas measurement the difference in

acoustic impedance is large, often in the magnitude of 500:1 or above).

Insertion losses are such that current test experience indicates that 10-15bar -dependent on pipe

geometry (principally wall thickness), fluid composition and process conditions- is the low pressure

threshold for reliable measurement.

4 Wave Propagation Clamp on systems have benefited from the use of lamb waves. In the application of this means of wave

propagation the transducer is designed so that the pipe wall is caused to resonate. In this way more

energy may be transmitted into the fluid.

This method has many useful applications but is limited to a narrow band of applications dictated by

matching the pipe wall thickness with the transducer frequency.

Outside of the application envelope of Lamb wave systems, the traditional technique of shear wave

propagation is best deployed. This elastic wave is perhaps the best method of transmitting ultrasonic

pulses, and finds widespread use in many applications. It is the only technique which is applicable to

thick walled pipes.

Figure 3 - Lamb wave propagation (top) and shear wave propagation: both in single path configurations

With the transducers of non-invasive systems separated by pipe walls, the signal losses are such that the

systems run with amplifier gains rather higher than is sometimes expected. In gas systems gains of

between 80dB and 100dB are typical.

The use of advanced diagnostic provides the opportunity to review system performance, and can often

provide an indication of how an installation may be improved.

Most manufacturers will provide a diagnostic report which indicates meter performance.

Figure 4 - typical diagnostics panel

When considering Figure 4 the importance of recorded diagnostic values becomes clear. Perhaps the

most important diagnostic value is the measured speed of sound (SOS). This is an immediate indicator of

the fidelity of the installation, and is the first thing to consider before making further assessments. As

discussed, measurement is only possible with a clear received signal. The signal-to-noise ratio (SNR) and

derivatives thereof are measured and –if above certain thresholds- are the principal determination of a

successful, and potentially stable, measurement.

Usually internal performance measures, such as gain (or it’s reciprocal, amplitude) and functions relating

to measurement stability are also indicated. From these data the overall measurement quality and

reliability may be assessed.

Typically arrival signal waveforms (“snapshots”) are recorded.

5 Installation Considerations - Geometry When applying external transducers to a pipe it may be seen that they must always be installed at a

tangent to the pipe. Thus the measurement paths are always diametric. This is the case irrespective of

path numbers.

Figure 5 - diametric measurement of single-path installation

Often additional pairs of transducers are applied to the same measuring point to improve system

performance. In single phase flow the traditional approach with a second channel is mount the 2 pairs of

transducers such that the measurement paths are perpendicular.

Figure 6 - dual pair installations: x-formation and perpendicular paths

Nevertheless the measurement is inferential and can be relied upon only if the flow is fully developed

and free of swirl and cross flow components. Often the installation configuration is dictated by pipe

surface conditions or geometric and flow conditions. Meter paths should never be in the same plane as

upstream bends and elbows.

To achieve fully developed flow some form of flow conditioning is required. With the attraction of non-

invasive systems being this very lack of any invasion of the pipework, the consideration of flow

conditioning elements is undesirable. It is therefore, normal that straight runs are required.

Good practise in this respect is of course an absolute minimum of 20 diameters upstream and 5

diameters downstream. However such guidelines are unlikely to be sufficient in high velocity conditions

or with large amounts of swirl. With diametric measurements, distorted velocity profiles will produce

unknown uncertainties and should be avoided.

6 Installation Considerations – Structural Noise The insertion losses observed contribute to significant structural born (pipe) noise. Modern DSP

electronics are effective in dealing with this – measuring coherent noise- but it is usual to reduce this

noise at source with mechanical damping. The application of appropriate acoustic damping materials to

the pipe is desirable. Signal to noise ratios can be enhanced in the order of 5dB-10dB with appropriate

damping. The structural noise increases with increasing wall thicknesses, and tends to be more

significant in lower-frequency systems.

7 Flow Stability In wet gas flows, low liquid contents and low gas velocities will cause the liquid to flow on the bottom of

the pipe with a lower velocity than the gas. This pattern is called “stratified flow”. When the liquid

content is high enough the pipe can get temporally blocked (“semi-slug flow”). With increasing gas flow

velocity the pattern changes to “wavy flow”. With even higher velocity the liquid travels in a non

symmetrical ring with the gas core laden with liquid droplets travelling through the centre of the pipe,

which is called “annular mist flow”. In the stratified flow state it can be assumed that the meter over

reads approximately corresponding to the percentage of the pipe area that is covered by the liquid. In

the annular mist state the over reading should approximately correspond to the LVF. So in the mist state

the error is much smaller than in the stratified state.

In the stratified flow state the amplitude of the ultrasonic signals is not affected as long as the liquid

level does not reach the sound path. In the mist state the attenuation of the wave propagation increases

with the LVF.

7.1 Definition of terms

The Liquid Volume Fraction is the ratio of the liquid flow rate to the gas flow rate.

l

g l

QLVF

Q Q

It needs to be considered, that the velocity of the liquid flow can be much smaller than that of the gas

flow. So the ratio of liquid to gas volume in the pipe can be much bigger than the LVF.

The Gas Volume Fraction is the ratio of the gas flow to the total flow rate. The relation to the LVF is:

1GVF LVF

The Lockhart-Martinelli parameter is the most useful wet gas parameter and is defined as:

gl l l

LM

g l g g

m QInertiaof Liquid Flowing AloneX

Inertiaof Gas Flowing Alone m Q

7.2 Relations between different parameters

1

lLM

g

LVFX

LVF

(0.1)

With small LVFs the XLM number is approximately

lLM

g

X LVF

.

(0.2)

This shows that with constant LVF the XLM decreases with the square root of the pressure.

With consideration of the mechanical limitations of the installation, the process conditions and flow

profiles, the design of an installation may be finalised.

The deployment of ultrasonic flow meters which this paper addresses is on a thick-walled DUPLEX pipe

measuring wet gas from individual wells. Such an application presents a number of challenges from

both process and piping perspectives.

DUPLEX piping has often been considered a difficult material in the context of ultrasonic measurement.

The complex grain structure is purported to degrade ultrasonic signal transmission and is reported to

provide sufficient attenuation as to make challenging applications which would otherwise be

straightforward. Insertion losses will be greater with such material.

8 Field Experience GDF SUEZ E&P Nederland B.V. have installed clamp on transit time meters on 2 offshore platforms in

the Southern North Sea. Two meters were installed in early 2011 on platform K9-B and two meters on

platform K12-C in November 2011.

The clamp on ultrasonic meters are mounted upstream of a test separator, measurement at which is by

spool piece multipath ultrasonic meters. The clamps on units comprise dual channel measurement in

single path X-configuration (see Figure 6). The ultrasonic transmitters are mounted in the control room

and output the mean actual flow (between channels A&B) to flow computers via a pulse output.

Normalized flows are calculated in the flow computers and SOS in accordance with AGA10.

Both installations included a number of interesting features.

Location Pipe Diameter Wall Thickness Pressure XLM

K9-B 115.2 14.2 92 0.015

K12-C 114 13.3 89 0.014

Figure 7 - Summary of installation parameters; K9-B and K12-C

These applications may be considered thick-walled and relatively small diameter. All pipework is Duplex,

and the geometry dictates the use of shear-wave transducers. Note, however, that the pressures are

high. All wells produce wet gas with Lockhart-Martinelli numbers of approximately 0.015. This suggests

that, at expected velocities, the gas/liquid flows will remain homogeneous.

Previous tests in other, similar locations have suggested that the installations will be noisy and will not

present fully developed flow profiles.

There was insufficient straight length on the K9-B installation to effect any measure of flow conditioning.

There was, however, sufficient deck space to offer some piping modification. A large flag of piping was

installed to allow appropriate straight lengths.

Figure 8 - Flag piping on K9-B; note transducer installation and damping material

In Figure 8 the additional piping installed by GDF SUEZ E&P Nederland B.V. can be seen. Inflow

conditions of 20 diameters were made possible with this configuration.

Figure 9 - K12-C installation -looking downstream

With shear wave transducers single path configuration was chosen in all cases. The orientation of the

transducers was selected so that both pairs were in the horizontal plane. This orientation was selected

to minimize the effects of the upstream swirl in-plane with the upstream piping.

In Figure 8 and Figure 9 the pipe damping mats can clearly be seen.

9 Results The received signal waveforms (“snapshots”) indicated reasonably high fidelity measurement. The ratios

of signal to coherent noise were in the order of 25dB-30dB.

A typical “snapshot” shows the measurement condition.

Figure 10 - Snapshot (K9-B, well 5; typical)

The snapshot in Figure 10 indicates a clear arrival signal and also some low amplitude noise before the

signal arrival. This is typical of thick-walled pipes but can be seen to be a limited contributor to the noise

in the SNR evaluations.

10 Diagnostic Review and Spotchecks

Well 6

Ch Feature MEAN Std.Dev Unit

A: MEASURE 132.11 1.511 m3/h

A: SSPEED 401.81 0.058 m/s

A: GAIN 84 0.5 dB

A: SCNR 28 0.6 dB

A: SNR 21 2 dB

B: MEASURE 129.62 1.344 m3/h

B: SSPEED 404.16 0.067 m/s

B: GAIN 82 0 dB

B: SCNR 22 0.3 dB

B: SNR 22 1.2 dB

Figure 11 - Diagnostic Panel, K9-B Well 6

All measures in the diagnostic panels show satisfactory results.

Well 5


A: MEASURE 110.44 9.744 m3/h

A: SSPEED 414.57 0.757 m/s

A: GAIN 81 0.4 dB

A: SCNR 23 0.6 dB

A: SNR 24 1.2 dB

B: MEASURE 117.15 9.747 m3/h

B: SSPEED 408.41 0.695 m/s

B: GAIN 80 0.5 dB

B: SCNR 26 0.9 dB

B: SNR 28 0.9 dB

Figure 12 - Diagnostic Panel, K9-B Well 5

Well 3


A: MEASURE 181.62 0.8 m3/h

A: SSPEED 412.86 0.11 m/s

A: GAIN 82 0 dB

A: SCNR 20 0.6 dB

A: SNR 17 1.5 dB

B: MEASURE 165.8 0.779 m3/h

B: SSPEED 412.13 0.107 m/s

B: GAIN 82 0 dB

B: SCNR 21 0.5 dB

B: SNR 17 2.3 dB

Figure 13 - Diagnostic Panel, K12-C Well 3

Previous research suggests a measurement error of between 2% and 4% may be expected due to the

wetness of the gas. Spot checks suggested variations between the upstream clamp on systems and the

separator outlet in accordance with this expectation. SOS variations indicate lack of homogeneity in the

pipe.

Well Calculated

SOS Measured SOS

5, t1 416.91 A 415.38

5, t1 416.91 B 415.31

5, t2 416.95 A 415.81

5, t2 416.95 B 408.59

6, t1 401.97 A 401.74

6, t1 401.97 B 404.17

6,t2 401.95 A 401.817

6,t2 401.95 B 404.09

Figure 14 - SOS Comparisons, K9-B at times t1 and t2, 15 minutes apart

The vicissitudes of upstream measurement do not impact the diagnostics, which remain pleasingly

consistent. What may be seen is that the measured rate between channels A&B vary by up to 10%. This

indicates some levels of cross flow components in the pipe (swirl). In this case the meter output is the

mean of both channels, using an internal calculation channel, the value of which meets the

expectations.

11 Performance Review, K9-B

Figure 15 - Outputs of clampon ultrasonic flow meters and the test separator, K9-B

Figure 15 15 shows the 2 separate wells (5&6, in red and green respectively) against the outputs from

the separator (liquid in yellow and gas in blue). The graph traces these flows over a period of 1 month in

September 2011. The deviation between the 2 clamp on measurements combined and the

measurement downstream of the separator was within 2%.

12 Performance Review, K12-C

Figure 16 – Respective measurement of the clamp-on meters and downstream meters, K12-C

This shows the well’s main & back up gas measurement ( in red and green respectively) against the main

& Back up gas measurements downstream the separator (in yellow and blue).

On K12-C just one well was originally commissioned. Original spot checks comparing the 2 channels for

this measurement gave agreements of SOS to within 0.2% but a deviation in flow rate of 4%. The

combined flow rate (the average of A & B) was within 3.5% of the reference.

Longer term measurements show the deviation between channels to decrease to within 2%. Agreement

with the separate reference remains between 2% and 4%. The measurements and agreement with the

reference remain stable.

Figure 17 - Outputs of clamp on ultrasonic meter (well 4) and reference meter, K12-C

With the additional well on line (well 4) the performance was also studied over time. Deviation between

the clamp on device and the separator reference was less than 2%.

13 Conclusion

The field installation of clamp on ultrasonic flow measurement systems for well flow line measurement

has been shown to provide consistent measurement to within limits of 2% - 4% deviation between the

actual measured flow and the reference.

If the specific conditions of the installation, pipework, materials and so-on are considered along with the

process conditions, the expectations of measurement uncertainty in wet gas measurement can be met

or improved upon.

14 References

[1] M. Panicke, B. Funck. Diagnose und Bewertung von Ultraschall-Clamp-on- Messungen. 4. Kötter Workshop Gasmengenmessung, 2008

[2] D. W. Spitzer (editor) Flow Measurement Practical Guides for Measurement and Control, Instruemnt

Society of America 1991

[3] J. Lansing, The Benefits of a Fully Self Diagnosing Gas Ultrasonic Meter, 8th South East Asia

Hydrocarbon Flow Measurement Workshop 2009

fltough

Stamp

Accuracy and long-term stability of ultrasonic gas meters at varying operating pressures

and different liquid loadings – field experience.

Alexander Jakschik, SICK AG

Jörg Wenzel, SICK AG

Theo Warmenhoven, GDF SUEZ E&P Nederland B.V.

Dr. Volker Herrmann, SICK AG

1. Introduction

The gas industry again underwent growth of almost 2 % in 2012 (world gas production

see Figure 1). The environmental advantages of gas and rising energy demands worldwide

in recent years will keep this trend ongoing. This will also result in an increase in the

onshore and offshore exploration of gas fields around the world.

The offshore ambient environment and technological economic circumstances, in

particular, places high demands on metering technologies. Liquids occurring in the

pipeline and pressure reduction require a meter technology which offers high accuracy

and long-term stability in these harsh circumstances. Well proven in typical transmission

and storage custody applications, ultrasonic meters are emerging in the gas production

segment. Advantages like high turn-down ratio, no pressure drop, low-maintenance, self-

diagnostic capabilities and proven long-term stability cause this trend.

This paper will discuss design criteria for optimized meter operation and also two

practical challenges to measurement:

Liquids in the gas flow - even after separators

Pressure change due to depletion of gas sources

The paper follows a discussion on the use of USMs along with practical examples,

including experience GDF SUEZ E&P Nederland B.V., which is one of the largest

operators in the Dutch sector of the North Sea, with an annual gas production of around

6 billion Nm3 at more than thirty production platforms.

fltough

Stamp

Accuracy And Long-Term Stability Of Ultrasonic Gas Meters At Varying Operating Pressures And Different Liquid Loadings – Field Experience.

- 2 -

Figure 1: World dry natural gas production by region, 1980-2010 [5]

2. Offshore gas production

Unlike onshore drilling, offshore production is typically realized by a small number of large

rigs (offshore platforms).

In the offshore oil & gas production process a distinction is generally made between three

types of production platforms: the so-called “main production platform,” the “manned

platform” and the “unmanned satellite platform.”

At the time this paper was written, GDF SUEZ E&P Nederland B.V. had 34 production

platforms, of which four were main production platforms, nine were manned platforms and 21

were unmanned satellite platforms. Via a complex system of underwater pipelines, most of

these platforms are connected to the gas transportation systems operated by

Noordgastransport B.V. and NOGAT B.V., which have their own gas treatment plant

onshore. Figure 2 provides an overview of the GDF SUEZ E&P Nederland B.V. production

platform network, while Figure 3 shows the gas production platform L10-A as an example.

The main production platform is where the streams arrive from different satellite platforms.

The gas is produced by separating the gas, dehydrating it and compressing it to the respective

pipeline pressure. The different gas streams are combined to one stream before transport from

the platform to the treatment plant. A satellite platform is a small, in most cases unmanned,

platform where liquid is separated from the natural gas before the gas is transported. The gas

is then transported to the main production platform. These platforms are designed to be

operated remotely under normal conditions.


- 3 -

Figure 2: Map of GDF SUEZ E&P Nederland B.V. production platforms

Figure 3: Gas production platform L10-A run by GDF SUEZ E&P Nederland B.V.


- 4 -

Figure 4 depicts a typical gas process scheme at a main GDF SUEZ E&P Nederland B.V.

production platform. Gas from several wells arrives at the platform: This can either include

wells directly connected to the platform or gas coming from a satellite platform.

Figure 4: Schematic process - main production platform

The gas is metered after the respective separators for process control (allocation

measurement) and reservoir management. The different gas streams are then gathered into one

stream. Compression takes place if the flowing wellhead pressure is close to or lower than the

pipeline pressure. The gas is cooled down to ensure that the separator runs at maximum

efficiency before the gas enters the glycol contactor. The gas is then measured to an extremely

high level of accuracy for transportation onshore.

Figure 5 shows a typical gas processing schema at a satellite platform run by GDF SUEZ

E&P Nederland B.V. There is also a production separator and a test separator for allocation

measurement and to quantify the amount of gas coming from the individual well for reservoir

management. The gas is transported to the main platform for further processing.

Figure 5: Schematic process - satellite platform


- 5 -

3. Gas flow measurement during offshore gas production

3.1. General design considerations for ultrasonic meters

The meter is the “cash machine” of the oil & gas producing company. The accuracy,

reliability and long-term stability of the metering equipment are crucial: Low performance, or

in the worst case down-time, can lead to substantial losses. In addition, remote unmanned

production platforms require a measurement device with extremely high availability and very

low maintenance.

The offshore environment itself places high demands on the metering equipment. This already

has to be taken into account by the meter manufacturer at the design phase. The meter must be

able to tolerate gas compositions outside standard specifications or recover quickly when the

operating conditions returned to “normal”. In addition the meter has to indicate in its

diagnosis whether it has been operating outside the specifications.

Main challenges for metering equipment in offshore production:

a) Harsh environment

b) Liquid in the gas flow even after separator

c) Pressure change due to depletion of gas source

To achieve maximum reliability under such conditions it is necessary to analyze and eliminate

potential issues such as:

Outside corrosion, mechanical damage

Vibration

Inside corrosion and fouling resulting in

- diameter change

- profile change

- symmetry change

Transducer failure

Loss of signal due to

- high attenuation in the gas

- reduction of transducer power, e.g. due to coating

- body noise due to water bridges

Reduction of path length due to coating

Electronic failure

The above factors require several engineering measures. Further, three criteria are

highlighted which are related to transducer design, because they are of special importance

for offshore applications: transducer encapsulation, pressure resistance and pressure range.


- 6 -

3.1.1 Transducer encapsulation

The transducer's active elements need to be protected from any damage resulting from

chemicals entering its internal structure. This is realized by encapsulating the active

elements from the operation fluid, though this encapsulation is rather difficult to achieve

in detail.

The transducer housing is made from corrosion-resistant titanium alloy. This material is

sufficient for general corrosion protection. On the other hand, the assembly requires some

threads, gaps or crevices within the housing to ensure that the optimum acoustic

performance is produced. These connecting elements operate like springs to energize the

transducer and increase the acoustic power of the resonant vibration. The remaining gaps

have to be closed for protection. Glues cannot ensure sufficient resistivity to the gases and

liquids in the oil and gas industry. Therefore welding is the only available technology to

achieve real hermetic encapsulation.

Welding the gaps is a difficult topic, as the inner transducer parts should not be subjected

to extensive thermal stress. The titanium alloy used typically melts above 1600 °C while

the piezo-ceramic elements must be kept well below 300 °C. In addition, the welding

technology options are limited, due to titanium's affinity to atmospheric gases such as

oxygen or hydrogen. Technologies such as electron beam welding or laser welding can be

used, but have to be optimized with respect to the welding parameters, while operation

during welding needs to be very stable because of the very tight parameter range.

Figure 6 shows a cut-away view of an electron beam weld under a light microscope. The

strongly localized influence of the heat during welding is a result of a well-defined,

precisely adjusted welding procedure. Not even the thread connecting the two welded

parts is influenced, and the ceramic is fully protected from the heat necessary to weld the

two titanium parts. The localized welding seam also indicates that there is not too much

heat introduced into the neighboring material. Tensions following uncontrolled heat

introduction into the titanium would act as additional electromechanical actors. As a

consequence the transducer would have parasitic resonance frequencies. Figure 7 shows

the completed transducer part with the weld on the top.

Figure 6: Weld on titanium transducer

Figure 7: Transducer dummy with weld

Weld

Heat influenced zone

uninfluenced material

Weld


- 7 -

3.1.2 Pressure resistance

Another aspect is that: the transducer is a part of within the pressure boundary of the meter

and must withstand the pressure within the meter. This requires larger thicknesses of all the

pressure pressure-bearing parts to be thicker.

To ensure sufficient that safety margins are efficient, the elements are designed to withstand

much higher pressures than the design pressure, in this case up to 450 barg. It has have to pass

much higher test pressures depending on the actual dimensions and material characteristics.

An example for of such this kind of experimental design verification is shown below

(Figure 8). A transducer was tested with increasing pressure, measuring the reaction (or non-

reaction) of the structure to the pressure. The transducer was designed to withstand a 450 bar

design pressure and from based on the actual wall thickness, actual strength values and

tolerance consideration; a final test pressure of 450 barg was calculated.

Figure 8: Transducer strength test – 450 barg design pressure

Looking at the test results, it can be concluded, that up to 1050 bar the transducer is free of

any damage. The transducer failed no earlier than 1250 bar (damaged transducer shown in

Figure 9).

Indication of plastic deformation

Rupture


- 8 -

Figure 9: Burst and deformed transducer at 1250 bar

3.1.3 Increased pressure range

Transducers that can withstand the extremely high pressures need to be extremely robust. This

includes having a relatively thick membrane. Having a thick membrane makes the transducer

somehow “deaf”.

An example for 2 different frequencies is given in Figure 10. This graph shows the

normalized sound pressure level [dB] over the membrane thickness [µm]. As higher the

frequency as higher the damping effect of thick membranes.

Figure 10: Relationship between normalized sound pressure level and membrane

thickness [2]


- 9 -

At high pressures this is not a problem, since the sound transmission is very good. But as soon

as pressure drops each dB sound pressure remaining can decide whether a meter can measure

or not.

Figure 11 displays the relationship between the sound pressure level [dB] and gas pressure

[bar]. One can see, that a pressure decrease from 10 to 1 bar means the sound pressure level is

20 dB lower. A decrease from 100 to 10 bar also leads to a decrease of 20 dB.

Figure 11: Relationship between sound pressure level and gas pressure

The challenge here is at lower pressures: The “deaf” transducer still has to receive enough

signal strength to measure in custody transfer quality.

To realize this extreme pressure range with one meter it is necessary to transmit as much

sound energy as possible into the gas and to make the dampening as low as possible.

For the transmission of a maximum acoustic energy into the gas an acoustic impedance

matching is mandatory. Since a hermetic sealing, as discussed in chapter 3.1.1., is necessary,

there is only a transformation or matching inside the encapsulation possible. Between the

transmitted energy and the received energy there is the attenuation: A logarithmic measure

along the path. A direct path layout positively supports signal quality in low pressure

application. Therefor it seems to be given by the physical principle, that at a given transducer

power a direct path layout will have a much wider pressure application range.

The overall concept allows the use of a direct path ultrasonic meter in the pressure range from

0 to 250 barg.


- 10 -

3.2 Challenge: Liquid in the gas flow

3.2.1 Application

During production and in test separators, liquids are separated from the gas. After separation,

the respective components are measured and transported away or disposed of. Here,

measurement plays an important role, for process control and/or to quantify the components

of the respective wells (allocation metering). Typically the gas flow temperature at the

separators ranges between 10°C and 90°C. The gas pressure is in a range from 120 down to

4 barg. Figure 12 shows a typical meter run after the test separator.

E17-A is a gas production platform without satellite platforms in the north west of the Dutch

sector with a daily production rate around 3.200.000 Nm³/day (Figure 13). The 4-inch

class 900 meter is installed behind the test separator in a 20D/USM/10D setup without flow

conditioner. Each well is sequentially connected to the test separator. The flow range of the

single wells is currently between 630.000 - 1.430.000 Nm3/day.

Figure 12: Meter run downstream of test separator

Figure 13: Platform E17-A, red marked


- 11 -

3.2.2. Theory of Liquid formation in Natural Gas

While separators are meant to extract all liquids from the gas stream, that target cannot be

realized in every case. Changes in the separator operating regime can result in the device

overflowing and the liquid accidentally being transferred into the gas meter. Another

reason for liquids within the gas flow is based on the thermodynamics of the gas mixture

itself: When a given composition of the gas stream is considered, the hydrocarbon dew

point (HCDP) and the water dew point (WDP) depend on the pressure and temperature

and can be calculated. The hydrocarbon dew points can be visualized with the help of the

envelope graph shown in Figure 14 [3].

Figure 14: Example of hydrocarbon dew point envelope [3]

Reducing the temperature from right to left at constant pressure, for operating conditions

above the hydrocarbon dew point envelope, the entire transported medium is gaseous.

Between the water dew point borderline and the HC dew point envelope, hydrocarbons

may be precipitated as liquids. Further lowering of the temperature at constant pressure

leads to the condensation of water and more hydrocarbons.

But not only hydrocarbon liquids may appear in the gas phase. Sweet natural gas also

contains water which has the potential to drop out if process conditions change. Figure 15

shows the calculation chart of water content in sweet natural gas.

An exemplary calculation from platform E17-A should demonstrate these dependencies.

Temperature during production separation process is at around 86 °C at a pressure of

around 112 barg. Due to low ambient temperatures, for example in winter time the gas

pipeline temperature is lowered as well. The pipeline in between separator and gas meter

causes a temperature drop of 4 K to 82°C. Knowing the pressure and the respective

temperature drop, one can conclude, that ~800 kg water per 1.000.000 Nm3/d gas drop

out into the pipeline only due to temperature drop of 4 K. At 82°C and 112 bar this results

in a liquid volume fraction of 0,18%.


- 12 -

Figure 15: Calculation chart of water content in sweet natural gas [1]

Figure 16 displays the hydrocarbon envelope of the respective gas mixture produced. By

this one can conclude, that there is no hydrocarbon liquefied at these process conditions if

the temperature decreases from 86 to 82°C.


- 13 -

Figure 16: Hydrocarbon dew point envelope for gas composition at platform E17-A

Finally liquids also may occur if the test separator has been out of operation for some time

and cooled down. In that case water may condensate on the separator outlet which will be

blown away after restart of stream.

In conclusion the meter in operation will be affected by water but not condensates entrained to

the gas phase if the ambient temperature causes a significant temperature drop of the gas.

3.2.3. Meter behavior – tests conducted on the wet gas loop at CEESI

Tests at the CEESI test stand have proven that even if the transducer is totally flooded, the

path recovers after the liquid disappears. A 3-inch and 4-inch 2-path USM was tested up to a

liquid volume fraction (LVF) of 5% at 13 and 55 barg and from 5 to 13 m/s [4]. The

following table gives the test sequence, the gas volume fraction, the meter performance for

the lower acoustic path as well as diagnostic values such as AGC, SOS and turbulence ratios

For the meter under test at 55 bar and 8,2 m/s. Table 1 shows results from the CEESI test.


- 14 -

Time GVF Performance SOS ratio Turbulence

ratio

10:45 1,000 100% 1,00 1,11

11:01 0,9550 0% 1,05 20,01

11:11 0,9771 68% 1,01 6,06

11:32 0,9951 100% 1,00 1,28

Table 1: Meter performance at different gas volume factions [4]

The meter remained in operation throughout the test, as the upper path didn’t fail at the

maximum LVF of nearly 5 %. At this maximum LVF level the lower path is flooded

completely. Reduction of the amount of liquid to less 2,3 % brings the lower path back to

operation. For lower liquid loadings, the measurement recovers completely and the

performance index reaches 100 %. The results and diagnostic capabilities of the 2-path system

from this particular test can also be transferred to a 4-path meter considering the different path

locations.

3.2.4 Test Data for platform E17-A

Ideally a simulated test for entrained liquids under real operating conditions would require

minimum ambient temperature, best performed during winter times.

The following test on the platform E17-A has been performed in July 2013 where the ambient

temperature was not very low. Therefore it was not expected to see a lot of liquids in the gas

phase right after the test separator.

Nevertheless, to increase the amount of liquid the separator was shut off for some hours and

cooled down. In that manner liquids had the chance to drop out and form on the outlet of the

separator. It was expected that after re-connecting a well to the test separator the increasing

gas flow will blow the liquids downstream. Further the expectation has been that the installed

4-inch, 4path ultrasonic meter shall detect the small amount of liquid by giving some

diagnostic indications.

The following process data have been measured after shut-down the test separator run:

Pressure 115 barg

Temperature 26°C

Speed-of-Sound 408 m/s

Flow velocity 0 m/s

In the next step gas stream has been routed through test separator. Velocity of gas is increased

and diagnostic values are recorded. Under these conditions liquid carry over into the meter

section was expected.


- 15 -

After the setup had been run under these conditions for 5 minutes the process went back to the

following parameters.

Pressure 116 barg

Temperature 66°C

Speed-of-Sound 418 m/s

Flow velocity 7,94 m/s

These are considered normal operating conditions.

In figure 17 an installed 8-inch 4-path ultrasonic meter is shown, removed from the meter run,

showing exemplarily the expected water droplets at the inner pipe wall.

Figure 17: Condensation on inner meter body wall due to temperature decrease below dew

point

Normally, the limits for different parameters and values are set in the meter itself. Once one

of the limits exceeds an alarm is generated. Following this alarm, the user can analyze the

diagnostic data using the parameterization software to get a detailed look into the operation

conditions. As it will be shown in the following example - the amount of liquid was too little

to cause alarms. Nevertheless the meter diagnostic indicates the changes in process conditions

very precisely.

The change in application conditions and especially the presence of liquids are visualized by

five main indicators:

Speed of Sound ratio

Turbulence

Performance

Profile factor

Symmetry

Figure 18 shows an example diagnostics screenshot, while the separator is being started up.

The flow range raised from 0 m³/h to 3m³/h.


- 16 -

Figure 18: Diagnostic data during start-up of separator, @ 14:09:39

The screenshot is made from the user software of the meter. Green indicates everything is

OK. Yellow indicates alarm, but measurement is still valid. Red would be an alarm where

measurement is invalid and the meter counts into the error counter. To help evaluate the

diagnostics more easily, the speed of sound deviation is displayed relative to the mean value

calculated from all four measurement path.

The flow profile is asymmetrical as the lower path indicates a lower VOG. The turbulence of

path no. 4 is found to be higher compared to all other paths. In addition, the speed of sound

measured on path 4 is lower compared to all other path values and the performance dropped

to 90%.

Profile factor displayed versus symmetry give also a very good indication about the liquid

loading. The graph in Figure 19 shows the so called profile indication (symmetry over profile

factor), where the profile indication is out of its limits. A valid value would be within the

dotted red box.


- 17 -

Figure 19: Profile factor vs. Symmetry at start-up and dry conditions

All parameters together clearly indicate that liquid has entered the metering section.

Another sensitive indicator of the ultrasonic meter is the speed of sound ratio. All four

acoustic paths should indicate the same speed of sound in normal operation. A significant

reduction of the speed of sound on one path could indicate the presence of liquid. An

estimation using the composition of the gas and the liquid at the measured conditions and

using Wood’s formula [6] gives the liquid volume fraction present. In the particular

application the average speed of sound of all paths is 412,265 m/s in the moment of the

highest liquid loading. The speed of sound of the lowest path at that time is 411.73 m/s. This

results in a ratio factor of 0.998. Comparing this result with the estimation of Figure 20 one

can conclude that the amount of liquid is very low. This shows how precisely the ultrasonic

meter can detect liquids.

Figure 20: Normalized speed of sound calculated from Wood’s expression for gas-liquid-

mixtures

(In this figure all speed of sound data are normalized to the value of the pure gas phase at the

reference conditions before shut-down of the separator.)


- 18 -

After approximately 2 minutes the metering section is cleared out and meter performance

comes back to 100% on all paths. Figure 21 shows the same diagnostic values as before after

maximum flow has been reached.

Figure 21: Diagnostic data after start-up at dry conditions, @ 14:11:05

The diagnostics and meter readings show, that the meter goes back to normal operation.

Performance recovers to 100% and turbulence is in the normal range. It also indicates that

there is no liquid in the gas anymore. The ability to recover after the presence of liquid is vital

for the operation.

3.3. Challenge: Pressure change due to depletion of gas source

Typically flowing well head pressure takes place at pressure of 3 - 500 barg. For further gas

processing pressure is reduced to 90 - 120 barg (“free flow”). However, natural gas sources

deplete over time. Hence the pressure will drop.

Pipeline pressure in between offshore and onshore is defined to be at 85 – 110 barg. Therefore

stream is connected to compression (Figure 4) in case of depletion, with the following steps:

6 - 10 barg, 12 - 20 barg and 35 - 50 barg, depending on the respective flow rate. Today the

lowest operating pressure in offshore applications of GDF SUEZ E&P Nederland B.V. is

around 7 barg. Nevertheless well pressure is expected to drop in the next coming years in

some cases as low as 4 barg.


- 19 -

Therefore GDF SUEZ E&P Nederland B.V. does require an ultrasonic meter technology,

which is able to cover a broad application pressure from ambient to 120 barg.

There are two major challenges for this extreme wide application range – Management of

acoustic power by means of transducer design and path layout as discussed in chapter 3.1. and

calibration discussed below.

The challenge regarding calibration is to provide a valid calibration over the full pressure

range during lifetime. Normal calibration is valid between the half and doubled pressure

during calibration. For example if a meter is calibrated at 60 bar it is approved for

measurement between 30 and 120 bar.

So what can be done if the operating pressure in offshore applications drops below the

calibrated thresholds? Here three possibilities shall be discussed.

1. Standard re-calibration procedure

In that case the meter needs to be dismounted and recalibrated on a dedicated calibration

institute. This is done by GDF SUEZ E&P Nederland B.V today (re-calibration interval of 5

years). One re-calibration result from a 8-inch meter after five years in operation is shown in

Figure 22. The maximum deviation to the initial calibration remains below 0.3% peek to peek

and the FWME according OIML is 0.061% between Qmin and Qmax. Values are in the area of

the uncertainty of the test stand.

Figure 22: Re-calibration chart of an 8-inch meter


- 20 -

2. Extended initial calibration

Another possibility is to perform an extended initial calibration. If the pressure range of the

application over lifetime is known the meter can be calibrated at different pressure levels

initially. Data can be stored and in case pressure level drops the meter can easily be updated

with the appropriate parameter setting. Or the individual calibration curves can be used by the

flow computer to correct the meter values for pressure.

One example shall illustrate this procedure:

Pressure range over lifetime: 4 to 110 barg

Calibration 1: @8 bar covering the range from 4…16 bar

Parameter set 1

Calibration 2: @30 bar covering the range from 15 … 60 bar

Parameter set 2

Calibration 3: @60 bar covering the range from 30 …120 bar

Parameter set 3

Performing all required calibrations before the meter is initially commissioned in the field

saves time and money for the operators.

3. Use of one set of coefficient of the whole range

To allow the use of one set meter coefficients for the whole pressure range it is necessary to:

a) Develop and investage an internal Reynolds correction which is valid over the whole

application range.

b) Approve and validate the correction by an independent authority like PTB or NMI

c) Continuously read the line pressure into the meters electronics

This procedure will be especially appropriate for high pressure applications above 120 barg,

as calibration institutes have their limits.


- 21 -

4. Conclusion

This paper discussed the performance of ultrasonic meters in applications where liquid is

present and significant pressure drop will happen over time. Beside theoretical examinations

also practical experiences and advises have been gathered from GDF SUEZ E&P Nederland

B.V., especially from an application on the E17-A gas production platform.

After what has been discussed one can conclude that USM’s work reliably and stably over the

long term in offshore natural gas production measurement, behind separators as e.g. at

GDF SUEZ E&P Nederland B.V. Long-term stability in this application has been

demonstrated also by showing re-calibration results from a meter which has been 5 years in

operation.

If an increased liquid content is temporarily present the meter accuracy and performance is

only temporarily affected and does recover. Diagnostics show not only potential changes in

meter performance, but can raise the alarm when there is a change in a process operation.

The performed test has been done in summer 2013. Due to the high ambient temperature the

amount of liquid drop out behind the separator was not as high as expected. But the meter still

detected it. Therefore it is recommended to repeat the test during winter time, in worst case

conditions to verify the diagnostic capability. During winter a LVF of 0.18% or higher should

be present.

USM’s are also an economical solution for allocation metering in offshore gas production

(behind separators) under the conditions of varying pressures. The design of the transducers is

vital for high metering performance and at a given transducer power a direct path layout will

have a much wider pressure application range.

With a high turn-down of up to 1:130 a single meter can serve a large operation range. With a

broad operating pressure range a single meter can be in use for the complete lifetime of a

production well. Meters measure accurately under all circumstances.

With an extended calibration where two or three different pressures are calibrated initially the

meter can be in use for fiscal operation until the well is depleted. The fact that ultrasonic

meters require nearly no maintenance and do not cause additional pressure drop turn

ultrasonic meters to be an economic device for use in onshore and offshore application

already behind the first stage of separation.


- 22 -

Literature

[1] Campbell, John M.; Gas Conditioning and Processing Volume 2; Campbell Petroleum

1979

[2] Herrmann, V.; Dietz, T.; Low Pressure Gas Measurement Using Ultrasonic Technology,

ISHM 2008

[3] Herring, J.. Hydrocarbon Dew Point – Critical Considerations, Source 10.09.2013:

http://www.michell.com/us/documents/whtpapers/HCDPMeasurementTurbineOpers.pdf

[4] Lansing, J.; Dietz, T.; Steven, R.; Wet Gas Test Comparison Results of Orifice Metering

Relative to Gas Ultrasonic Metering; AGA Conference 2012

[5] Source 10.09.2013: http://www.eia.gov/todayinenergy/detail.cfm?id=4790

[6] Gudmundsson, J.S., Celius, H.K.; Gas-Liquid Metering Using Pressure-Pulse Technology;

SPE Annual Technical Conference and Exhibition in Houston 1999

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Dynamic Testing

Authors:

Raymond J. Kalivoda

Jim H. Smith

Nicole L. Gailey

ABSTRACT

Dynamic factory testing is an important step in the manufacturing of ultrasonic meters for custody transfer and

other high accuracy petroleum applications. By utilizing a multiple product, high accuracy test system and a

proper test program, a meter’s performance can be simulated over a wide flow and viscosity operating range.

The test results give the user a detailed graph of the meter’s performance over the actual site operating

parameters. The test verifies the meter’s performance prior to shipment but more importantly provides K-Factor

sensitivity to optimize measurement accuracy throughout the operating range. This paper outlines the theoretical

basis and fundamentals of dynamic testing. It illustrates the process with data from an ultrasonic meter factory

test recently conducted for a North Sea operating company. The meter was a 12 inch multi-path ultrasonic meter

operating over a flow range of 636 to 1,113 m3/h (~ 4,000 to 7,000 BPH) and a viscosity range of 5 to 350 cSt.

The details of dynamic testing and the relationship between the measurement accuracy of a meter and dynamic

testing will be the focus of this paper. It will include:

The fundamental operating principle of ultrasonic meters

Fluid dynamic properties such as boundary layer and flow profiles

The characteristics of the flow profiles in the different flow regimes that affect crude oil measurement

The dynamic operating range of crude oil meters

How dynamic testing is used in factory testing to verify the performance of a meter

Results of the 12 inch multi-path ultrasonic meter factory testing

This paper will provide the necessary information to fully understand the basis and proper methods for dynamic

testing to determine the operating performance of an ultrasonic meter.

1. INTRODUCTION

As the world oil supply of heavier crude oils increases, in conjunction with an increase of use of liquid

ultrasonic meters, testing by the manufacturer plays a critical role in the meter performance verification for the

end product customer. If the meter manufacturer has their own flow test facilities, this can save significant time

and cost in the delivery of the meter. Dynamic testing or Reynolds Number testing has been used for

manufacturing and testing of helical turbine meters since their acceptance into custody transfer applications in

the mid-1990’s. While the Reynolds Number performance between helical meters and ultrasonic meters are

different, the dynamic test programs are very similar. By utilizing an ISO 17025 accredited, multiple product

test system and a proper test program, a meter’s performance can be simulated over a wide flow and viscosity

operating range.

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2. LIQUID ULTRASONIC METER OPERATING PRINCIPLE

Liquid Ultrasonic meters were initially used in the petroleum industry for non-custody applications. But with

the advances in microprocessors, transducer technology, electronics, and the introduction of multi-path meters,

transit time ultrasonic meters can provide accurate measurement over a wide range of applications. This

includes custody transfer of high viscosity crude oils. Ultrasonic meters, like turbine meters are inference

meters. They infer the volumetric through-put by measuring the velocity over a precise known flow area. As

with all velocity inference meters, they are Reynolds Number dependent. That is, they are affected by the

relationship between velocity, flow area, and viscosity. The fundamental difference between ultrasonic and

turbine meters is that the former uses non-intrusive ultrasonic signals to determine velocity and the later an

inline helical rotor. Since Reynolds Number was developed for free flowing pipes, its principles can be best

illustrated with ultrasonic meters.

As a review of the operating principle, ultrasonic meters derive flow rate by calculating an average axial flow

velocity in the pipe. This is done by summing the individual path velocities in the meter and then multiplying it

by the flow area in the meter throat as shown by the following equation:

Qtotal = Volume flow rate; A = Inside diameter; v = Path velocity; w = Chordal path weighing factor

The flow area in the equation is the physical geometric area based on the meter’s inside diameter which is

measured and input as a programmed parameter. However, the effective flow area is one that is formed by the

meter inside diameter and the boundary layer which is influenced by the pipe wall roughness, fluid viscosity

and velocity at operating conditions. All which will affect the flow profile shape. This will be discussed later.

The individual path velocity of a non-refracting configuration is determined by measuring the difference in

transit time of high frequency acoustic pulses that are transmitted with the flow (A to B) and against the flow

stream (B to A) at a known angle and length (Figure 1). The ultrasonic signals are generated by piezoelectric

transducers that are positioned at an angle to the flow stream. It is, therefore, imperative that a high quantity and

quality of signals propagate through the fluid medium to achieve a good representative sample. Some

manufacturers can supply different sets of transducers that operate at higher or lower frequencies to extend the

application viscosity and improve signal quality.

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Figure 1: Single Path of a Non-refracting Transducer Pair

The principle of ultrasonic measurement is simple. However, accurately determining the average velocity and

the effective area under different operating conditions can be difficult. Especially when attempting to obtain

custody transfer measurement accuracy over a wide dynamic range. The difference in time between the two

transducers can range between tens or hundreds of picoseconds for typical liquid ultrasonic flow meters

(depending on meter size and fluid density). The minimum time difference is tied to the lower flow limit and the

maximum time difference to the upper flow limit of the meter. Detecting and precisely measuring these small

time differences is extremely important to measurement accuracy and each manufacturer has proprietary

techniques to achieve this measurement. Velocity profiles are highly complex and one set of transducers only

measures the velocity along a very thin path which represents only a sample of the total flow across the meter

area. To determine the velocity profile more accurately, custody transfer ultrasonic meters use multiple sets of

transducers on chordal paths. The multiple chordal paths help in detecting whether the flow is laminar,

transitional, or turbulent. The number of paths, their location, and the algorithms that integrate the path

velocities into an average velocity all contribute to the meter’s accuracy.

Besides the axial velocity there are transverse velocity components (swirl, cross flow) as well. These

components of flow may be caused by two out-of-plane bends or other piping configurations, as well as local

velocities at the transducer ports. Both the swirl and cross flow components are included in the path velocities.

The local velocities are normally symmetrical and can be statistically cancelled. The transverse velocity

components should be eliminated or minimized by flow conditioning and must be accounted for by the meter

through measurement. Some ultrasonic meter designs measure the transverse velocity and account for it in the

axial velocity algorithms.

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Figure 2: Multi-path Non-refractive Chordal Ultrasonic Meter

3. BOUNDARY LAYER AND FLOW PROFILES

The flow area is dependent on the meter inside diameter which is a physical measurement of the meter

housing. The effective area is dependent on boundary layer thickness and can be seen in Figure 3 as the

diameter of the flatter region of the profile. The boundary layer thickness at the pipe wall is influenced by the

pipe roughness, viscosity, and velocity of the metered fluid. Looking at Figure 3 from left to right, we can see

various representations of flow profiles and boundary layer thicknesses. As the velocity decreases or the

viscosity increases, the boundary layer increases which reduces the effective flow area. At high flow rates with

low viscosity fluids, such as refined products or light crude oils, the boundary layer thickness is very small

(shown in Figure 3 on the right). This produces a flat shape velocity profile across the pipe inside diameter.

Figure 3: Boundary Layer Influence on Flow Profile and Reynolds Number

* Per API MPMS, Ch. 5.8 [1] (Annex D)

Low Re No High Re No

Zero Velocity at Pipe Wall

Boundary Layer Thickness

FLOW

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The boundary layer also defines a specific velocity profile. Determining the profile and compensating for its

effect on the calculated axial velocity is the key factor in the manufacturing of highly accurate ultrasonic meters

that are used over a wide dynamic range. The relationship of the velocity flow profile and flow area is

quantitatively defined by Reynolds Number (Re No) and Dynamic or Reynolds testing is the method used to

determine ultrasonic meter performance.

4. REYNOLDS NUMBER AND FLOW PROFILE

The shape of the velocity flow profile is the result of the viscous forces (viscosity) that constrain the liquid’s

inertial forces (velocity • density). When the viscous forces are greater than the inertial forces, the flow profile

becomes parabolic in nature. As the inertial forces become greater than the viscous forces the flow stream

becomes highly turbulent which produces a flat plug type flow profile. The parabolic shape of the flow profile

is determined by the thickness of the fluid boundary layer at the pipe wall. Regardless of the flow rate and

product viscosity, the velocity at the pipe wall will be zero. The maximum axial velocity is at the center of the

pipe, unless there are hydraulic influences from elbows, reducers, or the other types of upstream disturbance

which produce asymmetric profiles (maximum velocity off center).

At a low Reynolds Number the viscous forces constrain the initial forces, forming a greater boundary layer and

parabolic flow profile. But as the Reynolds Number increases due to an increase in velocity or decrease in

viscosity the boundary layer at the wall is reduced and the flow profile becomes flattened as shown in Figure 3.

In fluid dynamic terminology the parabolic flow profile is defined as laminar flow and is mathematically

designated by the dimensionless Reynolds Number as less than 2,000. The flat or plug shaped flow profile is

defined as turbulent flow with a Reynolds Number of greater than 4,000 to 8,000. The exact Reynolds Number

which defines the turbulent flow regime is dependent upon the upstream piping and other dynamic factors.

Between laminar and turbulent flow, transition flow occurs and the velocity profile changes rapidly between

laminar and turbulent. Over a wide Reynolds Number, transition occurs in a very narrow range.

An interesting fact determined by Osborne Reynolds over 120 years ago and repeated in thousands of

experiments since, is that the boundary layer and flow profile will always be the same at the same Reynolds

Number. This is illustrated in Figure 4 where three conditions are shown with different flow rates and

viscosities but the same Reynolds Number. In this case, we can use flow rate divided by viscosity for the

Reynolds Number comparison. Therefore, the ultrasonic meter’s field performance can be accurately duplicated

by Dynamic or Reynolds Number testing in a flow lab that is capable of producing the same range of

application. This provides a sound means for verification and calibration where field conditions cannot be

replicated. This is especially true with very large meters where it is not feasible or economical to achieve the

high flow rates and high viscosities associated with large meter specifications. This is a common limitation in

test facilities around the world.

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Figure 4: Velocity Profile Dynamic Similitude with Reynolds Number

* Per API MPMS, Ch. 5.8 [1] (Annex D)

5. DYNAMIC FACTORY TESTING

An important step in the manufacturing of custody transfer and other high accuracy petroleum meters is the

factory flow test. Conventional meters such as positive displacement (PD) and turbine meters (inference meters)

are typically tested on a light petroleum fluid (2 cSt to 4 cSt) over a specified flow range to verify that the

meter’s performance meets specifications. Validation of the meter occurs in the field by proving the meter in-

situ under operating conditions. This is typical for meters up to 16 inch in size.

The performance of ultrasonic meters for light product applications can be determined from low viscosity

factory tests. If these meters are to be applied over a wide viscosity range they must be tested over both a flow

and viscosity range. This flow and viscosity range is what’s known as the “dynamic performance range”. This is

especially true for ultrasonic and helical turbine meters which will be subject to heavy crude oils. The ultrasonic

meter requires the development of a special algorithm to compensate for the effect of viscosity on flow profile,

where a helical turbine meter requires the “tuning” of its rotor to operate accurately within the operating

conditions. The accuracy of the factory dynamic test will determine how well these meters perform under actual

operating conditions.

Because of the unique operating characteristics of ultrasonic meters in crude oil applications it is necessary to

develop new dynamic factory test protocols. These methods are different than traditional factory testing. They

provide a greater level of confidence that the meters will fully meet the performance requirements over the

complete operating range.

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All dynamic tests are developed from Reynolds Number which can be defined as the following:

Re No = (K • Flow rate)

(Meter Size • Viscosity)

K = 2,214; a constant for flow in barrels per hour (bph)

K = 13,925; a constant for flow in cubic meters per hour (m3/h)

Meter size = bph or m3/h meter sizes in inches

Viscosity = Kinematic Viscosity [1 centistokes = 1 millimeter squared / second (mm2/s)]

Typical Reynolds Number ranges for hydrocarbon products are displayed in Figure 5. The low viscosity

products produce high Reynolds Numbers and have more predictable results. Therefore by obtaining water test

data at 0.6 cSt at 40°C (104°F) it is possible to accurately predict the performance on a Liquid Petroleum Gas

(LPG) at 0.3 cSt.

The same is not true for high viscosity products, such as medium or heavy crude oils. The Reynolds Number

plot is inherent to the meter size, type, flow range, and viscosity range where the deviation in meter factor from

a light crude oil to medium or heavy crude oil can be 2% to 5% or even greater prior to compensation. The only

way to develop the proper correction and validate the meter’s performance over this range is to dynamically test

the meter over the same Reynolds Number range.

Figure 5: Reynolds Number Ranges for Petroleum Products

6. DYNAMIC TEST EXAMPLE

The following example will best illustrate the methodology of Dynamic or Reynolds Number Testing. Table 1

shows the field operating conditions for three sizes of multi path ultrasonic meters – 6, 12, and 20 inch (150,

300, and 500 millimeter) with their flow ranges and products at 800 cSt and 1,000 cSt respectively. The table

also shows these operating conditions expressed in Reynolds Number.

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Table 1: Example of Field Operating Conditions

Meter

(Inches) Flow Range

Viscosity

(cSt)

Reynolds Number

Range

6 bph 1,500 4,500

800 690 2,080 m3/h 240 720

12 bph 6,330 19,000

1,000 1,170 3,510 m3/h 1,010 3,020

20 bph 14,000 42,000

1,000 1,550 4,650 m3/h 2,230 6,680

Utilizing the field operating conditions, factory testing is developed based on the available test fluids and test

system flow ranges. Table 2 shows the tests that satisfy the dynamic operating range of the meters in Table 1.

This can be confirmed by observing that the Reynolds Number ranges are the same. This method of dynamic

similitude allows the meters performance to be validated for service on higher or lower viscosities and flow

rates than the specified field operating conditions. In the example, all three product viscosities can be simulated

with a 300 cSt test fluid by reducing the meter’s maximum flow rate. As long as the ultrasonic meter is

operating above its minimum specified flow rate, the test results are valid. Obviously, if higher viscosity fluids

are available for testing, lower Reynolds Number testing can be achieved.

Table 2: Example of Flow Testing Conditions

Meter

(Inches) Flow Range

Viscosity

(cSt)

Reynolds Number

Range

6 bph 560 1,690

300 690 2,080 m3/h 90 270

12 bph 1,900 5,710

300 1,170 3,510 m3/h 300 910

20 bph 4,200 12,600

300 1,550 4,650 m3/h 670 2,000

For a wide dynamic operating range, multiple test systems as well as multiple test fluids may be required.

Using multiple test systems is an accurate method of dynamic testing, as long as they use the same base

standard. For large volume hydrocarbon test laboratories, this would be is a displacement or tank prover. The

test systems should be accredited to a specific standard, typically ISO / IEC 17025. This provides the accuracy

or expanded uncertainty on the certificate of accreditation which are factored into the test results (Addendum

A). An example of dynamic testing results are shown in Figure 6. These results were obtained using the

multiple systems and fluids approach in which a High Flow (HF) test system (Figure 7) and Multi-Viscosity

(MV) test system (Figure 8) were used.

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Figure 6: Multi-Viscosity Test System Dynamic Range

Dynamic similitude is achieved by replicating the Reynolds Number range. The testing can be accomplished by

controlling flow rates and viscosities. Therefore the facility must have precise flow control and temperature

stability in order to maintain the Reynolds Number throughout the testing. Temperature control is the largest

contributing factor that determines how viscous of a fluid a test facility can handle. Heating and cooling systems

which are necessary for temperature stability can be extremely large and costly. Therefore some manufacturers

will utilize 3rd

party test facilities to achieve the test range. Figures 7 and 8 are examples of two test systems.

The main components are listed. Note that each test system is tied to the same standard, which in this case, is a

Small Volume Master Prover (Item 6 in the Figures).

Figure 7: High Flow Test System

1. Test Run

2. Pumps

3. Tank

4. Chiller

5. Master PD Meter Provers

6. Master Prover

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Figure 8: Multi-Viscosity Flow Test System

7. 12 INCH MULT-PATH DYNAMIC TEST

In this example, data is presented from a recent evaluation testing program for a major crude oil production

company. The 12 inch multi-path ultrasonic meter was to measure a range of crude oils from 5 to 350 cSt and

achieve a linearity of +/- 0.10% over the customer’s flow range for a given crude oil. The operating conditions

and Reynolds Number operating ranges are shown in Table 3.

Table 3: 12 inch Ultrasonic Customer Application Data

Meter Size 12

Meter Type Multi-Path Ultrasonic

Flange Class ASME Class 600

Meter Schedule (ID) SCH XS (ID 11.750 inches)

Minimum Flow Rate 636 m3/h [4,000 bph]

Maximum Flow Rate 1,113 m3/h [7,000 bph]

Viscosity Range 5 –350 cSt

Reynolds Number Range 2,153 to 263,796

Table 4: 12 Inch Ultrasonic Dynamic Test Range

Meter Size 12

Meter Type Multi-Path Ultrasonic

Flange Class ASME Class 600

Meter Schedule (ID) SCH XS ID (11.750 inches)

Minimum Flow Rate 79 m3/h [500 bph]

Maximum Flow Rate 3,021 m3/h [19,000 bph]

Viscosity Range 12 – 150 cSt

Reynolds Number Range (±10%) 1,884 to 298,340

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Based on the on the field operating conditions and the flow test facility’s capability, an equivalent dynamic test

range is defined in Table 4. Utilizing the dynamic range, a detailed test plan was developed in which multiple

test systems and products were used (Tables 5 and 6). The factory test plan thus covers the complete field

measurement range.

Table 5: Dynamic Similitude Test 1

Test ❶

Test System High Flow (HF) Test Stand (Reference Addendum A)

PD Meter Master Prove 9.7 m3 [61 bbl] Prove Volume

Test Fluid Medium Fluid

Temperature ~32.2°C [90°F]

Viscosity 12 cSt

Nominal Flow Rates (BPH) 500 4,200 7,900 11,600 15,300 19,000

Nominal Flow Rates (M3/HR) 79 668 1,256 1,844 2,433 3,020

Reynolds Number Test Range 7,851 65,949 124,047 182,145 240,243 298,340

Uncertainty 0.027% @ 0.95(normal) per API 5.8

Table 6: Dynamic Similitude Test 2

Test ❷

Test System Multi-Viscosity (MV) Test Stand (Reference Addendum A)

PD Meter Master Prove 9.7 m3 [61 bbl] Prove Volume

Test Fluid Extra Heavy Fluid

Temperature ~35°C [95°F]

Viscosity 150 cSt

Nominal Flow Rates (BPH) 1,500 4,750 8,000

Nominal Flow Rates (M3/HR) 238 755 1,272

Reynolds Number Test Range 1,884 5,967 10,049

Uncertainty 0.027% @ 0.95(normal) per API 5.8

The meter was then tested to determine the raw or uncompensated performance that covered a Reynolds

Number range of 1,000 to 350,000 which is a much larger measurement range. The purpose for this was such

that the correction method developed for a particular meter size and model can then be used in future

applications that are covered within the Reynolds Number range. Due to the extensive testing on multiple

products and test points, this approach helps in the manufacturing optimization. The test results for the larger

range are shown in Figure 9. The uncorrected K-Factor variation was within +/- 1.113 %.

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Figure 9: 12 Inch Ultrasonic Uncorrected Test Data

A linearization algorithm which reduces the meter sensitivity to flow profile and hence Reynolds Number

changes is applied. From the empirical raw test data the algorithm was developed to compensate for the K-

Factor variation due to viscosity effects on the meter’s performance. The meter, when retested with the VPC

algorithm in place, had a K-Factor variation of +/- 0.139% over this much larger dynamic range (Figure 10).

Figure 10: 12 Inch Ultrasonic Test Data with Correction

Overall Linearity ± 1.113%

Linearity over a 619:1 Dynamic Turndown

190 – 3,000 m3/h [1,200 – 19,000 bph]

Overall Linearity ±0.139% Linearity over a 619:1 Dynamic Turndown

190 – 3,000 m3

/h [1,200 – 19,000 bph]

Medium

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With the algorithm in place, a customer application can be tested over their specific dynamic range. The results

of the two test plan (Tables 5 and 6) are shown in Figure 11.



Test ❷ Test ❶

Overall Linearity ± 0.139% Linearity over a 158:1 Dynamic Turndown

Reynolds No. Range 1,884 - 298,340

Overall Linearity ± 0.139% Linearity over a 38:1 Flow Turndown

79 – 3,020 m3

/h [500 – 8,000 bph]

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Figure 13: 12 Inch Ultrasonic Test Data with Correction within Customer Application Range

Representatives from the oil company witnessed the compensated meter performance over the complete

dynamic operating range as outlined in the factory test program. The combined test results of K-Factor vs. flow

rate are shown in Figure 13 which correlates to the Reynolds Number curve over a smaller range of the

customer application. While the two product tests would have covered the customer range, an additional test

was conducted up to 220 cSt to verify the reduced sensitivity in K-Factor variation compared to the other test

fluids.

8. CONCLUSION

Liquid Ultrasonic Flow Meters (LUFM’s) have gained acceptance in the petroleum industry for a wide range of

applications. Initially they were used for non-custody applications of light hydrocarbons. But with advances in

microprocessor, transducer, and electronic technology multi-path LUFM’s can provide highly accurate flow

measurement of crude oils from light condensates with a viscosity of less than 0.5 cSt to heavy crude oils over

2,000 cSt.

Developing and verifying the performance of these meters over field operating conditions is an essential part of

the manufacturing process. It is especially important for high viscosity fluids where velocity profile correction

is required to provide accurate and linear measurement. The key parameters that determine meter performance

are size, flow rate, and viscosity, which are related by a well-established dynamic parameter - Reynolds

Number.

By employing the principle of Dynamic Similitude, performance can be validated for service on a higher or

lower viscosity than the test fluid. Simply stated, performance at a given Reynolds Number is the same no

matter the combination of flow rate and viscosity. Therefore by utilizing multi-viscosity test systems, Dynamic

Tests can be run to determine measurement accuracy over a wide range of operating conditions.

Overall Linearity ± 0.048% Linearity over a 2:1 Flow Turndown

636 - 1113 m3

/h [4,000 – 7,000 bph]

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References

[1] API MPMS Ch. 5.8, “Manual of Petroleum Measurement Standards, Chapter 5.8 – Measurement of

Liquid Hydrocarbons by Ultrasonic Flow Meters”, 2nd

Edition, November 2011.

[2] ISO 12242:2012, “Measurement of fluid flow in closed conduits – Ultrasonic transit-time meters for

liquid, First edition 2012-07-01

[3] Lunde, P., Kalivoda, R.J. , “Liquid Ultrasonic Flow Meters For Crude Oil Measurement”, 23rd

International North Sea Flow Measurement Workshop, Tonsberg, Norway, 18-21 October 2005.

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Addendum A

Test Facility Expanded Uncertainty

Test Facility: FMC Technologies Flow Research and Test Center, Erie, PA

USA

Meter Types: Positive Displacement, Helical and Conventional Turbine, and Ultrasonic

Test

System(1)

Viscosity Range

(cSt) Flow Rate (m3/h) Flow Rate (bph)

Prove Method(2)

Expanded

Uncertainty(3)

min max min max min max

HF 10 25

30 135 190 850

DPM

0.075%

13

5 2,782 850 17,500 0.045%

60 158 380 995

MMPM

0.120%

15

8 6,680 995 42,000 0.084%

MV

2 150

30 135 190 850

DPM

0.065%

13

5 1,270 850 8,000 0.047%

60 103 380 650

MMPM

0.091%

10

3 1,270 650 8,000 0.084%

150 250

30 135 190 850

DPM

0.055%

13

5 1,270 850 8,000 0.042%

LF

2 6 0.6

270

4

1,700

DPM 0.037%

4.0 25 MMPM 0.077%

7 25 0.6 4 DPM 0.036%

3.2 20 MMPM 0.076%

20 100 0.6 4 DPM 0.035%

2.4 15 MMPM 0.075%

80 225 0.6 4 DPM 0.035%

1.6 10 MMPM 0.075%

Notes:

1.) HF (High Flow); MV (Multi-Viscosity); LF (Low Flow)

2.) DPM (Direct Proving Method with Small Volume Prover); MMPM (Master Meter Proving Method with PD Meters)

3.) Expanded uncertainty based on a coverage factor of, k = 2, with a level of confidence of approximately 95%.

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Sampling: What the standards don’t tell you

NSFMW 2013

Mark A. Jiskoot P a g e | 1 October 2013

Introduction

International standards are developed to form a basis for trust in the uncertainty of measurement,

they can be normative and informative, and they demand conformance to performance criteria.

Unfortunately there remain areas of design easy to misinterpret that can significantly influence the

overall uncertainty of the measurement. This has gained the attention of measurement engineers,

the standards committees and some research investment to further improve knowledge.

The commercial value of the uncertainties caused by poor sampling has never been more evident;

originally developed in the 1980’s the API, IP/EI and ISO standards are under review. The philosophy

and techniques for sampling hydrocarbons needs to change to correlate poorly understood “best

practices” developed between users and vendors with practical reality.

With over 35 years field experience in designing and proving sampling systems and with developing

the standards used to define them both then and now, we are uniquely placed to share.

This paper will summarise some of the main issues that repeatedly arise and provide suggestions as

to their relevance, effect on system uncertainty and how best to address them.

Newsflash - The Standards are changing! The original need for better sampling standards in the early 1980’s was driven by the oil shocks of

the 1970’s and specifically for the receipt and pumping of crude oils (API Chapter 8.2). It comprised

essentially the experience of “pipeliners” and the then relatively new role of “loss controllers” with

not much recognition of the needs of offshore production or product quality sampling.

The API standard based much of its “Table 11” on a series of tests based at the old Mobil Paulsboro’

facility and some other testing performed in a meter calibration loop of the, then, Smith Meter in

Eyre, Pennsylvania.

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NSFMW 2013


This table was based upon the testing of a single oil viscosity/density in a limited range of pipelines –

it is therefore really only applicable as a very rough guide.

As this was gestated under API, the then IP 6.2 (now EI) and the ISO 3171 were concurrently

developing their own standards taking a slightly more guidance based approach and seeking to

insert more practical examples of designs (either in use or on the drawing board at the time) and

science by way of calculation associated with pipeline mixture quality and recognising ongoing

testing then being performed. Real data was used to correlate the calculations based upon profile

measurements in a large pipeline.

Most of the committee work concluded in about 1984 and the ISO standard was published in 1988.

The design of the optimal sampling systems used today for crude oils appear in the IP document


NSFMW 2013


which in contrast to the ISO and API document also considered to some degree the sampling of

higher RVP products, but not “clean” products.

The API was revised in 1995 (and re-confirmed since); key changes included adding the statistical

approach of the ISO/IP documents, but also significantly to allow for the use of manifold sampling,

component testing and the requirement for two sequential tests to prevent the “ethically

challenged” from repeating the overall testing until they got the result they wanted. It also broke

out the question of sample handling and mixing (including the containers used for retention into API

8.3 and API 8.4). The largest change of all was the abandonment of a grading system based upon the

proving tests in a favour of a single (but still perhaps flawed) pass/fail criteria. (below)

The focus of the standard remained stabilised crude oils.

Also in 1995 the Norsok I-SR-100 “Automatic Oil Sampler” was published; essentially a reference to

the API and ISO standards.

The API, IP, ISO standards were never designed to address the needs for multi-product pipeline

sampling or recognise the many special demands of offshore measurement which included

convoluted piping configurations, smaller diameter pipes, high RVP products etc. and indeed the

requirements for the measurement of density and on-line water determination (OWD).

Total Water (Wbl + W inj) Allowable Deviations

Using Tank Gages Using Metres

0.5 0.13 0.09 1.0 0.15 0.11 1.5 0.16 0.12 2.0 0.17 0.13

2.5 0.18 0.14 3.0 0.19 0.15 3.5 0.20 0.16

4.0 0.21 0.17 4.5 0.22 0.18 5.0 0.23 0.19

This was the original “graded”

acceptance criteria used by the API

1983 version and STILL current for

ISO 3171, IP 6.2

This is the current API

Pass/fail criteria


NSFMW 2013


This has perplexed those seeking to use the standards, specifically in the area of pipeline mixing

when they think they have faithfully applied them only to discover their results do not make sense.

Over the same timescales, many of the onshore and pipeline systems designed under these

standards have been tested by water injection with the significant majority passing, but some that

failed have done so without explanation. Unfortunately people are reluctant to admit failure and

much of that data is immediately lost.

Work continues to revise the API document to recognise the wider application of sampling

technology in both process i.e. extending them from stabilized crude oils to un-stabilised and to

recognise the sampling of products including those with higher RVP’s.

An early objective was that the standards generated may be balloted for adoption by ISO,

unfortunately this now looks unlikely.

In the meanwhile the offshore North Sea has long been seeking guidance to improve measurement

of un-stabilised oils and to deploy on-Line water determination (OWD) systems.

Sampling Technology Comprised typically in-line sample probes and low pressure receivers (often stationary), pipeline

mixing was often overlooked because Table 11 was the datum and people did not read the small

print:

High RVP was a nuisance that typically resulted in flaring until late in the 1970’s when legislation in

the North Sea meant it had to be piped ashore so technology was developed to try to properly

sample it.

Most systems so designed catered for stabilised crude oils of middle range viscosity and density, not

to the extremes now seen in the form of condensates at one end and heavy oils (or even tar sands)

at the other.

Density compensation for metering was generally made by using the analysis of the sample so taken

and much debate arose about the meaning of “Wet” or Dry” density. Later slipstream loops were

created to pass a stream of oil through a duty standby (parallel) arrangement of density meters and

this grandfathered practice often continues.


NSFMW 2013


The holy grail of quality measurement, WaterCut meters (or as some still continue to erroneously

title them – “ B S & W probes”) certainly existed as simple capacitance meters and were used as

go/no go measurements.

Some local standards also required the sampling system to be upstream of the metering system

because the metered volume should NOT include the sample volume removed! (as a result the

benefit of mixing induced by strainers, meters, and control valves was lost).

The standards paid little attention to the issues of sample handling and mixing until the release of

API 8.3, indeed some specifications still mistakenly call out IP 386 as a reference for sample mixing.

This is in fact a Karl Fischer Titration standard!.

Pipelines Designers must treat sampling systems installed offshore or at the end of a long pipe run that may

include elevation changes differently to the sampling of stabilised crude offloaded from a ship (and

sampled close to the tanks in which oil has been resident for days).

In the best (ship) case the process comprises oil with a small concentration of water that must be

adequately mixed into the stream at the point the sample is taken, but in itself this provides

challenges. We like to consider that the concentration does not change markedly in the short term

and indeed unloading profiles would suggest this. However, this stability will not arise in piping

configurations as typically found in a production environment (platform etc.) which are subject to a

large number of orientation and elevation changes which are actually ”slug” generators!


NSFMW 2013


However we all accept that crude and water do not mix well; separation is inevitable at low

velocities in a pipeline and this gets worse with higher difference in density and viscosity (for

examples condensates) and higher water concentration.

What we often fail to consider is that a steady input of water concentration even in a horizontal

environment creates the potential for significant fluctuation over a length because the water under

certain flows will collect into “rolling” slugs. Placing a steady input to a horizontal line followed by

an elbow to a riser makes the situation worse, many of you will be aware of the annular, churn or

barber polling that render sampling in vertical flow an optimistic proposition unless the flow

entering the riser is already well dispersed. The poor droplet sizing and distribution can also be seen

in the photograph of a vertical riser on page 10.

0

5

10

15

20

25

30

35

0 50000 100000 150000 200000 250000 300000 350000 400000 450000

Batch Volume (BBls)

AP

I/%

W/c

0

10

20

30

40

50

60

70

Th

ou

sa

nd

s

Flo

wra

te (

BB

ls/H

r)

Instant Water Content

Ref API

Average Water Content

Flowrate


NSFMW 2013


Technology Development Much of the technology that has been developed has been initiated by industry requirements and

best endeavours and the investment in time and effort in testing by individual oil companies in

conjunction with their preferred suppliers a process that continues to this day.

JIP’s

JIP’s in the UK sector (for example NEL), in Norway and elsewhere have run a number of excellent

projects to validate the best available technologies. Examples of this would be the NEL “Hi-water”

projects dealing with the ability of samplers to meet representivity, the need for “Isokinetic”

(actually not – this is more related to droplet size vs. opening) and the current crop of activity

focussed to validation of the Annex A.

Mixing

Pierre Hayward and Ari Segev did much theoretical work on pipeline mixing that drove the standards

development focussed largely to steady state pipelines.

Vendors proposed a number of static mixer designs, including the use of variable static mixers,

turbine mixers and variety of valve types (Swing Checks and the Neles “Q-Ball”)

A number of profile tests have been executed to seek to validate steady state flow regimes (….and

some accidental tests on unsteady states)

Testing of profiles for low-spot corrosion hotspots in pipelines (Yuri Fairzurov)


NSFMW 2013


What is clear is that the calculations within the current standard are limited in application and

because this is not clearly understood they are easily misused.

High RVP

When high RVP crude was first piped ashore from the North Sea two alternate approaches were

taken:

High pressure collection receivers (requiring a constant pressure receiver to be handled

offshore, transported mixed and analysed onshore)

“Split-Phase” Samplers (requiring the sample pressure to be reduced offshore to collect a

stabilised oil sample and a low pressure gas sample)

The procedures for mixing high pressure sample collection receivers are not defined in any standard.

The migration to sampling at close to vapour breakout renders its own challenges to the sampling

beyond the sample collection. Mixing such a stream is a challenge as mixing implies the dissipation

of energy either from the process in the form of pressure loss or by external addition. High RVP

oil/water mixtures simply cannot afford the creation of pressure loss and the resultant vapour

breakout in the form of gas as this will significantly affect the metering system.

It is postulated by some, I believe erroneously, that placing the quality system off-take in a vertical

line provides a suitable location for mixing. Certainly in some aspects it is an improvement but such

an approach completely forgets the transient nature of the process!

Proving

API 8.2, 1983 proposed a methodology for water injection proving a sampling system using a single

test and assigning a grading to the outcome based upon the deviation from the expected result. (the

testing method expected the use of Centrifuge or Dean and Stark distillation as Karl Fisher was not

widely available.)

The first tests of installations under this draft were made on systems installed at BORCO in the

Bahamas and over time more and more systems have been tested with mixed success.

Unfortunately it is easier to access successful results than those that failed!

When the API standard was revisited in 1995, the committee concluded that it was statistically

impossible to render a meaningful grading system (where “A” – suitable for custody transfer (+/-

0.05% /%) and despite EI provision of a number of tests indicative that it was attainable and

desirable, the input was rendered as technically non-persuasive and the standard was changed to a

simple pass/fail criteria based upon a negotiated set of wider tolerances based upon whether the oil

measurement was by tank gauging or metering. This test had one key highlight of great significance

in that it required two sequential tests to be within the acceptable boundaries which precluded

those that would test repeatedly until the combination of errors worked in their favour to pass a

system! The EI and the ISO standard however retained the tighter tolerances and the graded

criteria.


NSFMW 2013


Water injection proving has been carried out now thousands of times, mainly on loading, unloading,

pipeline and allocation based samplers and generally on stabilised oils. This is because it is

significantly easier to control the stability of the baselines and to perform sample handling and

analysis. These results can be used to validate that the mixing, handling and analysis meet the

acceptance criteria for the COMPLETE system. It does not seek to differentiate error sources as the

result is a cumulative one.

One other issue the API did seek to address was the idea of “component testing”. Both the API and

the ISO/EI standard always allowed for the testing of pipeline profile using a profile probe and this

remains a useful tool with some limitations to use. The API also allows for the design of a system to

be ported without further testing between applications, provided that the installation and process is

identical (a rare idea!).

OWD

Simple capacitance probes have been used for years to control whether “oil” quality was adequately

processed to be used, but they have at best been a trending device. Several attempts to improve

the technology were made by use of additional calibration inputs (typically density, temperature)

and the current generation of these devices have more sophisticated electronics but suffer many of

the same problems. The design of sensors using microwave frequencies provide an overall

improvement to the result but there remain unknowns in the process that continue to preclude

them being accepted for fiscal use. The API spent considerable time and effort in the collection of

field data and then in drafting a standard which ultimately failed to gain acceptance because there

was insufficient validating information; it was ultimately published as a technical report. The scatter

below of a calibrated system shows the OWD results are well outside the expected +/- 0.05%


NSFMW 2013


Density

On-line Density meters were initially installed in bypass loops either pumped or driven by differential

pressure from an orifice plate or sometimes a simple forward facing takeoff probe. Often no

recognition was made that the standard requires that the off-take be “representative”. A good

designer now recognises that the density metering off-take MUST match that used for sampling and

it makes logical sense to integrate the density, sampling and OWD in a single system subject to the

same design constraints.

Although the current density standard proposes that the two density meters be mounted in parallel,

there is limited logic to this approach. Suppliers typically recommend that the density meter be

mounted vertically; with the flow upwards and in excess of 3 m3/hr (to mitigate effects of entrained

gas/bubbles). Mounting them in parallel requires that the overall loop flow rate be higher and that

provision is made to ensure that the flow stream is fully homogenous before division.

Ideally if two density meters are to be used for “run and check” they should best be mounted in

series.

Scatter of results based upon CoJetix as Datum

-0.8000% -0.6000% -0.4000% -0.2000% 0.0000% 0.2000% 0.4000% 0.6000%

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35

37

39

41

43

45

OWD

In Line with Static Mixer


NSFMW 2013


Isokinetic The API has little guidance on the design and implementation of loops in which the sample extractor

is mounted externally to the pipeline, it currently defines “Isokinetic” as of “matching velocity

between the pipeline and the inlet to the sampler”, this was relaxed to suggest that provided that

velocities match between 50-200%. Neither of these statements was based on testing. The IP did

some field testing and in that standard suggested that as the opening of the sampler gets larger in

comparison to the droplet size, 10-300% velocity matching appeared to be of little influence.

In practice we have found that the larger the entry the better, though certainly any opening over

11/4” (33mm) does not seem to be influenced from a variance between the process pipeline velocity

and the sample inlet velocity in a properly mixed pipeline.

Parallel run density meters are

typical, but they should not be.

There is a relationship between droplet size and the capability of a sample off-take to handle it. The photo to the

right shows a vertical riser oil/water mix and clearly the droplet sizes are large and poorly distributed – the probe

seen is unable to sample representatively.


NSFMW 2013


From our direct experience we would contend that “Isokinetic” sampling is undesirable in properly

designed sampling loops for a number of reasons,

1. Proven accuracy of well designed sampling systems using fixed velocity “loop”

technology at larger inlet sizes.

2. Water can fall out within the sampling loop if a true Isokinetic flow regime is created at

the inlet as the flow range changes perhaps by factors of up to 30:1. This will certainly

make the imbalance between parallel mounted density meters or samplers more

extreme that is found even today.

3. Low flow velocities create water fallout and where there are directional and orientation

changes, will create a more “sluggy” flow regime, hence potentially reducing sample

representativity.

Uncertainty

The ISO provides a statistical method to calculate uncertainty based upon a number of influential

parameters, frequently misunderstood.

It is evident that dispersion/distribution quality is the single largest influence on the result but grab

size repeatability, the number of samples taken per batch, the number of repeat analyses are also

important. In getting to an overall result (on paper) the issue of mixing and subdivision of the sample

and indeed the number of analysis results and how they should be handled is more often than not

ignored.

We have seen companies taking a sample and repeatedly analysing it until they got the “best”

number they could! The uncertainty calculation shows that averaging two lab analyses will improve

uncertainty by 0.01% (20% of the allowable error under ISO at a nominal 1% water.)

0

0,5

1

1,5

2

2,5

3

0 5000 10000 15000

% R

ela

tive

Un

cert

ain

ty

Number of Sample Grabs

Measurement Uncertainty Influence(number of Grabs)


NSFMW 2013


The standards propose that the number of grabs should relate to the batch volume, it is clear that if

the process is subject to transient flow, then a higher grab count should improve representivity.

However it is also clear that the graph above would suggest that over a certain number of samples,

there is not a significant improvement in uncertainty with grab count. In this example the threshold

appears to be about 2,000 and even when a large sample is easy to collect (i.e. low RVP samples)

anything over a maximum of 10,000 samples unlikely to yield a significant improvement. The

practice in small leases of collecting and processing 30 gallons (over 100,000 ml) would seem

counter-intuitive because the mixing, handling and sub-sampling (not to mention disposal, cross

contamination, cleaning etc.) would surely increase uncertainty.

One interesting observation and one that causes some debate is the use of the phrase “Systematic

uncertainty” or bias. In the process of proving many sampling systems, which requires two

sequential tests under API (and which we also do even when proving under ISO) it has become

evident that for some system designs both of the results will be within the negative tolerance bands

and therefore the averaged result is almost always negative.

A study of this has led us to conclude that many sampling systems exhibit a negative bias, which if

compared to a metering system it could be ‘tuned out’, unfortunately with a sampling system this is

a physical manifestation that we cannot simply magic away with a “K-factor”. It is also clear that

some sampling system designs show no such bias.

Pipeline Mixing

The standard includes a calculation methodology for mixing which has significant provisos on its

range of application. It requires the assumption that the water concentration is at all times below

5%, that the flow is steady state, that the pipeline is essentially a long horizontal pipe with no

transients and that viscosity and density are within a tight range.

Since these calculations were based upon large pipelines, there is some debate as to how they work

on smaller pipelines. It is unsurprising then that many people are looking to CFD for answers.

Our company has been seeking to use CFD for assessing pipeline mixing for many years and certainly

with the improvement in computing power and the sophistication of cell based meshing it has

progressed significantly. Since we joined Cameron we have been lucky to have access to these tools

and qualified scientists to run them. We have spent considerable efforts in comparison of CFD

models to the calculations within the standards and we have discovered that it is all too easy to

convince yourself that the answers you see are right, simply because they look convincing - a minor

change to an input parameter can make a significant change to a result that perhaps only yesterday

you believed to be true! We like to think that CFD can account accurately for the complete process

but decisions not only on the configuration, mesh sizes, time cycles, inlet properties, length, droplet

size and distributions, coalescence etc. etc. etc all add complexity to the model and time. A simple

10 seconds of flow simulation (which is rarely enough to create stability) for can take many hours of

processing time!


NSFMW 2013


As an example of the challenge we are sharing three practical comparisons:

The profile data within the API standard,

A system where the JetMix in a conventional installation was upgraded after many years’

service.

A JetMix installed in large pipeline around elbows and vertical risers.

Case Study 1

API made a number of profile tests with a multipoint probe. We would define these profiles as

“REAL” and they are repeated within the ISO 3171. A CFD estimates “local” values in the profile free

of the influence of the offtake profile probe, sample collection errors and analysis errors, that said

the trend should be easily comparable.

But looking at the results, the CFD data does not match within the expected tolerance to the field

data! This could be influenced by a multitude of choices made by the modeller – what it is

imperative to understand is that it is therefore critical that methods are established to calibrate CFD

method with other data sets to enable us to better understand how we can use CCFDtm (Calibrated

CFD) with confidence.

Case Study 2 (Jet Update1)

A major crude oil Terminal that imports 1.7 MBPD (300,000 m3/day) had a Mk1 JetMix system with

and in-line probe installed in the late 80’s in the main incoming offshore feed pipeline.

ISO 3171 -

Page 52

CFD

Simulation-1

ISO 3171 -

Page 52

CFD

Simulation-2

ISO 3171 -

Page 52

CFD

Simulation-3

ISO 3171 -

Page 53

CFD

Simulation-4

ISO 3171 -

Page 53

CFD

Simulation-5

Velocity ( m/s)

Water Injection %

Point A 3.6 2.9 4.6 4.654 5.3 6.2 3.4 3 5 4.73

Point-B 3.4 3.09 5 5.166 5.7 7.22 2.9 3.19 5.3 5.107

Point-C 3.8 3.1 5.4 5.17 6.1 7.24 3.4 3.2 5.4 5.11

Point-D 3.2 3.1 5.3 5.17 6.6 7.24 3 3.2 5.4 5.11

Point-E 3.8 3.09 5.9 5.169 6.7 7.24 3.4 3.19 5.5 5.109

Point-F 3.4 3.09 5.7 5.169 7 7.23 3.2 3.19 5.8 5.109

Point-G 3.8 3.09 5.9 5.169 6.8 7.23 3.6 3.19 5.6 5.109

Point-H 4.4 3.37 6.4 6.275 7.2 7.9 3.6 3.48 6.2 5.8

5.11

2.38

3.2

2.381.84

3.1

1.84

7.24

1.84

5.17


NSFMW 2013


This sampling system was successfully (meeting the acceptance criteria) and repeatedly certified to

the API 8.2 standard yielding an average uncertainty of -0.13% (note the negative systematic bias)

The customer wished to see if the uncertainty could be improved. The original JetMix system had 4

large perpendicular jets; it was redesigned with a new Jet head design similar to the photo using our

latest calculations and design methodology. Because no changes were made to either the pump

used for the mixer or to the rest of the sample collection, handling or analysis equipment (or

methods) this enabled us to understand the influence of the design changes to the Jet Head. It also

allowed us to witness any erosive or scaling effects on the Jet Head.

Original Jet as removed and new Jet type.

The system was retested using the new Jet head but under the same, worst case, operating

conditions. The results were impressive; following the exact prediction of the CCFD, the API


NSFMW 2013


certification test came in at -0.03% uncertainty. This resulted in improvement to the customer’s

bottom line measurement uncertainty of $136,000 per day. As an aside after over 25 years of daily

service there was no evidence of significant scaling or erosion to the original part.

CFD Modelling shows the difference between the two designs

Case Study 3 (Static Mixer update)

Another site had an installed 48” static mixer which due to the rangeability was generating good

mixing at high flowrates but damaging equipment due to high vibration. At low flowrates it was clear

that the mixing was inadequate. They pondered if a JetMix design could be used, but consideration

was compounded by the installation configuration which was in a short length of pipe around a

number of “out of plane” elbows – a situation frequently seen in North Sea applications (but of

course significantly smaller pipes than 48”!). A conventional jet had been installed in a vertical rising

section but after initial testing it was felt the results, though better than the Static Mixer, could be

improved. Observations from a sidewall mounted density offtake loop led us to conclude that the

water was “poling” around the elbows and missing the highest turbulence zone of the Jet head. As a

result we redesigned the head to change the energy intense zone and successfully proved the

system.

Many of you will have heard us speak of other similar experiences where the physical performance

did not match either a conventional model or one generated using un-calibrated CFD technologies,

indeed at a transient complex flow consortia meeting a delegate complained as to when the “C” in

CFD would mean CREDIBLE.

CFD can provide useful insights into the potential “influencers” to the result, but they cannot provide

an absolute estimate of the result unless they are calibrated. Some proponents with no supporting

field data confidently state they can halve the amount of energy required to mix a pipeline! Again

we suggest that inexplicable failures are more expensive than conservative and field proven success.

The capability to calibrate or normalise CFD can only be attained with the collection and comparison

of significant volumes of practical field data across a wide range of pipeline sizes, oil types and flow

ranges . We call this CCFDtm or Calibrated CFD. We are blessed to own such a data set, admittedly

largely forged in success, but tempered by the occasional failure.


NSFMW 2013


Proving The water injection proving of a sampling system is the catchall method, similar to the ongoing

proving of a metering system, except a sampling system cannot currently be proven during each

batch process whereas a metering system can.

Proving a sampling system should exercise all the contributors to the uncertainty of the

measurement system, unfortunately some testers use the overall tolerance allowance in comparison

to a limited testing of the system, not recognising that one or more sources of uncertainty have

been eliminated with no recognition of their influence to the result. A sampling system is intended

to render a result on a piece of paper and therefore all the steps to the written results can be

influential to the error.

Pipeline mixing dispersion/distribution.

Sample extractor.

Sample collection receiver.

Sample receiver mixing and sub-sample withdrawal.

Number of sample results used, sample analysis method.

Two questions are debated with regard to proving, the first being the process conditions under

which a proving takes place. Received wisdom suggests that we should select what we believe to be

the minimum flow rate, minimum viscosity and minimum density but in truth the worst conditions

for one design of sampling system may not be the worst for another!

The second is proving frequency and this must clearly be a function of risk and opportunity. Proving

a sampling system is expensive; it requires a stable baseline and this may simply be impractical in the

transient regimes often typically found in a production environment. Even when the system is

capable of being subject to a stable regime to allow proving, how frequently should it be performed?

Some have suggested every 5 years is mandated, my view would be when the process has changed

significantly (for the worse) or the mechanical performance of the system is known to have changed.

While certainly in the past the highest risk and hardest application for a sampling system was

unloading ships through large diameter pipelines, other processes are in their own way equally

demanding.

Sampling at the outlet of a separator provides a flow stream potentially rich in free water where any

change in pressure used to promote mixing will cause gas breakout.

Many production sources now have higher water concentrations, higher sand and even more

corrosive properties. In light service a sampler can easily surpass a million grabs, but the bituminous

crude oils derived from tar sands have caused the creation of expensive severe duty samplers using

enhanced metallurgy, coatings and seals which can multiply the expected life by several orders of

magnitude.

The first universally used sampler was made by Clif Mock, ironically when Jiskoot was acquired by

Cameron in 2008 this sampler was returned to our portfolio and still sells well, but meanwhile


NSFMW 2013


Jiskoot has progressed from the loop samplers we manufactured in the 1970’s through in-line

samplers and Jet Mixers in the 1980’s to the sophistication of the CoJetix designs that we now use.

There is a place for all of these technologies including CFD, but it takes considerable skill and field

experience to apply them correctly.

A typical quality system for use on high RVP oils including cell samplers, CPC receivers, density

meters, OWD. (Part of a CoJetix system)

fltough

Stamp

1

The Emperor’s New Clothes? - Oil with Water Flow Metering

T. Cousins, CMSI (CEESI), C. Hodges, CMSI (CEESI),

R. Steven, CEESI, Damon Myer, CEESI

CEESI, 54043 County Road, 37, Nunn, Colorado 80648, USA

1. Introduction

Oil with water production flows is a significant problem to the hydrocarbon

production industry. It may sound like poor measurement practice to try and measure

oil production flows with greater than a few percent ‘water cut’. However, production

realities are making it essential to do so.

With oil prices tending to be in excess of US$100 / barrel, production of high ‘water

cut’ oil flows is commercially viable. As oil reservoirs age they tend to naturally

produce slower production flow rates and higher ‘water cut’ flows. Water injection is

a common method of increasing the pressure in a well to stimulate higher production

rates. However, this technique not only increases the oil production flow rate, but also

further increases the ‘water cut’. Furthermore, due to rising oil prices, low quality oil

wells previously considered commercially unviable are now being produced with their

high ‘water-cuts’. As a result of these developments some wells are now producing oil

at water cuts over 50%, with a few cases even reaching 90%. However, while the

number of high water cut production flow is increasing, the practice of flow

measurement without separation is becoming more prevalent as operators strive to

reduce production costs.

Oil with water metering techniques are becoming increasingly important. However,

from a recent investigation by CEESI, it appears that the independent data available

on the performance of oil flow custody transfer metering systems operating with high

water content of 5-90% is both scattered and sparse.

2. Present Industry Oil with Water Measurement Philosophy

It would appear that oil with water flow metering is a classic ‘multi-phase meter’

problem, and that typical multiphase metering uncertainties in the region of 5-15%

would be acceptable. Although the fluid dynamic problem is similar, the oil with

water flow measurement philosophy is very different. The assumption is that flow

meters should to read the combined volume of the two fluids to “custody transfer”

levels of accuracy.

The typical approach of oil with water flow measurement is to utilise a volume flow

meter to measure the total volume flow. A mixer element is used to create a

‘homogenous’ flow that can be sampled to produce a water to total volume flow rate

ratio (or equivalent parameter). Combining these two separate measurements

produces the individual oil and water flow rate predictions. Therefore, in order for the

typical approach to give custody transfer flow rate prediction uncertainties, both the

meters total volume flow rate prediction and sampling techniques must have low

uncertainties. This is all very different to the currently recognised multi-phase

measurement uncertainties.

3. What is Considered a “High Water Cut”?

‘Water cut’ (ω) is defined as the ratio of water produced compared to the volume of

total liquids produced at standard conditions. Equation 1 shows the water cut. It is a

fltough

Stamp

2

description of the water content contained in produced oil. Note that Qwater is the

volume flow rate of the water, Qoil is the volume flow rate of the oil, and Qtotal is the

volume flow rate of the combined oil and water flow.

total

water

oilwater

water

Q

Q

QQ

Q

(1)

The WLR is defined as the ratio of water to the volume of total liquids produced at

line conditions. The conversion of the volume at line conditions to standard conditions

is complicated in a two liquid system by determining the coefficient of expansion of

the combined liquids. Even when fully mixed this is subject to an increased

uncertainty. In this paper water cut is assumed to be representative of the flow at line

conditions.

Most standards concentrate on the measurement of water content, whereas, what is of

real interest is the measurement of oil content. It could be argued it would be more

appropriate to describe the relative amount of oil and water in terms of the ratio of oil

to total liquids produced, i.e. equation 1a. The aim of the typical oil with water

metering approach is to apply the oil flow rate prediction equation 2.

total

oil

oilwater

oil

Q

Q

QQ

Q

1 (1a)

1*totaloil QQ (2)

The standards discuss sample uncertainty in terms of water cut. That is, the standards

focus on water measurement uncertainty instead of the oil measurement uncertainty.

Setting a required sampling ‘quality’ by fixing a required water cut uncertainty

produces an increasing allowable oil flow rate measurement uncertainty as the water

cut increases.

Figure 1. Water Cut vs. Oil Flow Rate Prediction Uncertainty

for Various Water Cut Uncertainties

Figure 1 shows a graph of the relationship between the flows water cut, the allowable

water cut uncertainty and the oil flow rate metering uncertainty. With traditional low

water cut production flows the associated oil flow rate prediction uncertainty is low,

3

even for relatively high water cut uncertainties. However, as the water cut increases

(as it is doing across many modern production flows) the effect of water cut

uncertainty on oil flow rate prediction increases significantly. For example, let us say

there are 100 units of total volume flow. Let us say a water cut measurement (by

sample) is allowed an uncertainty of 1%.

Case 1: Consider a water cut of 0.1, i.e. 10 units of water and 90 units of oil flow.

Water cut measurement uncertainty is set here at +/-1%, i.e. the water cut

measurement is in the range 0.1 +/- 0.001. That is, the water cut is found to be within

the 0.099 to 0.101 range. Therefore, when a volume meter shows that the total volume

flow rate is 100 units, the oil flow rate prediction is somewhere in the 89.9 to 90.1

units of flow range, i.e. 90 units of flow +/- 0.11% uncertainty.

Case 2: Consider a water cut of 0.9, i.e. 90 units of water and 10 units of oil flow.

Water cut measurement uncertainty is set here at +/-1%, i.e. the water cut

measurement is in the range 0.9 +/- 0.009. That is the water cut is found to be within

the 0.891 to 0.909 range. Therefore, when a volume meter shows that the total volume

flow rate is 100 units, the oil flow rate prediction is somewhere in the 9.1 and 10.9

units of flow range, i.e. 10 units of flow +/- 9% uncertainty.

It used to be practically acceptable to discuss sampling quality in terms of water-cut

uncertainty. However, as industry copes with much higher water cuts it may become

more questionable if this is the most appropriate way to describe sampling

uncertainty. It is noteworthy, that if the industry chose to discuss sampling quality in

terms of ‘oil-cut’ uncertainty, i.e. the uncertainty of a samples oil to total flow rate

ratio, the uncertainty of the oil flow rate prediction would remain constant (for a

known correct total volume flow rate) across the water cut range.

There is no industry wide agreement to what constitutes a “high” water cut. Ideally,

for custody transfer oil flow metering there should be no water present. In reality,

even after refining, oil flows can still contain some water (and sediment).

Traditionally, in the US at least, industry has tended to consider the uncorrected oil

flow rate meter output uncertainty associated with water cuts ≤ 0.5% acceptable. A

general definition of ‘high’ water cut, therefore, could be a water cut higher than the

accepted industry standards for water content, i.e. > 0.5% water cut.

If sampling is required for higher water cuts, the generally accepted upper limit

inferred from API 8.2 [1] for an acceptable sampling and mixing operation is 5%

water cut. API 8.2 does not explicitly state an upper water cut limit, but the worked

examples are all for < 5%, and it was written at a time where < 5% was normal across

most of production. Therefore, for sampling procedures, traditionally water cuts > 5%

could be considered high. ISO 3171, does discuss the mixing and conditions relating

to higher water cuts, up to 30%, but does not go into any great detail on the issues

likely to be encountered at these higher values.

With the oil industry set to encounter oil production from fields that can produce

water cuts anywhere between 0% to in excess of 90%, the very question “… what

constitutes a ‘high’ water cut?” is perhaps becoming irrelevant, and even meaningless.

The reality of modern oil production is that industry will increasingly have to cope

with production flows that fall across the full range of water cut, i.e. 0% ≤ ω < 100%.

However, presently there is not a clear guidance as to how to approach such metering

challenges, and what uncertainties can practically be achieved.

4

4. The Known Issues with Current Practice

It is generally accepted that the following issues will arise due to waters presence with

oil:

an increase in the flow meters volume flow rate prediction uncertainty

a more complex volume conversion between actual and standard conditions

increase in samplings oil component uncertainty with increasing water cut

increasing difficulty to determine water content of a sample as water cut increases.

4.1 Flow Metering

The most commonly applied flow meter types to oil production custody transfer are

the turbine meter, the positive displacement meter, the Coriolis meter and the

ultrasonic meter. The Differential Pressure (DP) meter is not commonly applied to this

particular flow metering application.

For such an important subject there is surprisingly little independent data regarding

the performance of flow meters with high water cut oil flows. Individual meter

performance will depend on the meter type, and individual design. For Reynolds

number and viscosity dependent meters the combined and relative viscosities may

affect the performance. A flow meters performance may depend on how the oil and

water are dispersed in the flow, i.e. the meter performance depends on how well

mixed the oil and water flows are.

4.1.1 Coriolis Meters

Coriolis meters are joint mass flow meters and densitometers that utilise the principle

of the Coriolis force. This has led some engineers to assume that it doesn’t matter

what the flow consists of, the total mass flow and average density will be metered. If

this was the case the Coriolis meter would be a ‘silver bullet’ to many long standing

flow metering problems, such as wet gas flow, multiphase flow, flow with particulate,

and water cut oil flows. Unfortunately this is not the case.

Coriolis meters are good single phase flow meters. If the fluid is homogenous then a

Coriolis meter is an excellent mass flow meter and a good densitometer. (It is for this

reason that CEESI has utilised Coriolis meters as the water and oil flow reference

meters in the new oil with water test facility described in Section 5.) However, if a

flow is not homogenous, then the Coriolis meter can be adversely affected, e.g. see

GRI report No. 04/0172 [2] for wet gas flow metering. Different Coriolis meter

designs with excellent single phase homogenous gas flow and liquid flow

performance were shown to be very significantly affected by the presence of wet gas

flow. Hence, it does not stand to reason that a Coriolis meter should be automatically

assumed to be able to read an accurate total mass flow rate and mean density with a

water and oil flow mix. Operators should demand third party independent data as

proof of any performance claims.

Coriolis meter manufacturers have released research into multiphase flow

performance of Coriolis meters (e.g. Wienstein [3]) where sophisticated correction

factors and meter diagnostics have come into play. It is therefore possible that

individual manufacturers have confidential methods of coping with water cut flows.

Again however, operators should demand third party independent data as proof of any

performance claims.

5

Some Coriolis meter manufacturers claim (e.g. Wienstein [3]) that the mixture oil and

water flow density and total mass flow rate will be measured by the Coriolis meter.

Knowledge of the individual oil and water densities then allow the water cut and

individual oil and water flow rates to be determined. However, the manufacturers

have not shown any data and have not stated any uncertainties to these mixture, total

low rate and individual oil and water flow rates predictions.

One independent research project (Andersen [4]) showed that Coriolis meters with oil

and water flows have a performance that is dependent on the level of fluid mixing.

The Coriolis meters performance with oil and water flow deteriorated as the flow rate

reduced. Low flow facilitates separation of the oil and water flow. Water ‘hold-up’ in

the Coriolis meters U-bend tubing can cause significant meter output biases. It was

shown by Andersen et al that a mixing element reducing separation upstream of the

Coriolis meter significantly improved the meters performance at low flow rate oil and

water flows. A further issue that may require investigation is the effect of low

Reynolds number in combination with high water content. As there are linearity

challenges at low Reynolds numbers with a single phase flow, the extra dimension of

water content may be expected to increase the problem. Many of the higher water

content applications will be with heavy, high viscosity oils, producing low Reynolds

number flows. 4.1.2 Turbine Meters

Turbine meters are often a meter of choice for oil with water flow measurement, but

yet there is surprisingly little published research into turbine meters performance with

water cut oil flows. One of the perceived benefits of using a turbine meter is that even

with these adverse flow conditions they do tend to keep producing a flow rate

prediction output. However, a largely unasked question appears to be what does this

output actually represent?

There are many questions regarding turbine meter performance with high water cut oil

flows. For example, in horizontal flow (which is the most common turbine meter

installation) how do turbine meters cope with unmixed phases (i.e. separated oil and

water flows)? Does the level of mixing at the meter inlet change the meters

performance? Does the presence of the turbine meters alter the level of mixing?

4.1.3 Ultrasonic Meters

There is some data on ultrasonic meter reaction to oil with water flow, but there is

little independent data, and little data on different meter designs. Some manufacturers

have publicly reported on the issue with in-house research and data, e.g. Brown [5].

Here it was shown that the oil with water flow causes a degradation on the single

phase meters performance (as would be expected). However, it was shown that but

that averaging results over time, the meter could predict the flow rate, all be it at an

increased uncertainty compared to that achieved when the flow is a homogenous fluid.

Oil with water flow generally causes the standard deviation of each chords signal to

increase. Various distributions of oil and water can cause various deformations of the

wave signal. Lost signals are common (hence the benefits of averaging results over

time requirement). With stratified oil and water flows ultrasonic paths in the vicinity

of the interface are particularly vulnerable to such problems.

6

The presence of two fluids also has a significant adverse effect on the ultrasonic meter

diagnostic suite. The ultrasonic meter diagnostics signal that the meter performance is

very much different and poorer to the standard homogenous flow operation. The

diagnostics can indicate the presence of stratified water oil mixture. However, in

general, extracting meaningful quantitative information from these diagnostics for

various meter sizes, flow rate, water cuts and oil fluid properties is an extremely

complex issue.

One ultrasonic meter manufacturers has informed the authors that when quoting

meters for an oil with water application they prefer to modify the meter design, e.g.

lower frequency transducers are deemed beneficial as this reduces the number of lost

signals.

4.1.4 Positive Displacement Meters

Positive Displacement (PD) have the advantage of reading the actual total volume

flow rate regardless of how the oil and water are dispersed in the flow. However, it

has also long been known that PD meters, with their moving parts and gears, are

susceptible to damage from adverse flow conditions such as being over sped and wear

from contaminates in the flow. Reliability is often an important requirement in

custody transfer oil metering, and as such PD meters would be chosen for niche

applications. There is very little information in the public domain regards the

performance of PD meters with oil and water flows.

4.1.5 Provers and Oil with Water Flows

If a flow meter is calibrated in-situ by a prover, is the calibration only valid for oil

only flow, or is it valid for all subsequent water cuts?

With Coriolis, turbine, PD and ultrasonic meters widely assumed suitable for use with

oil with water flows there is a lack of independent data showing the various meter

designs performances across different oil with water flow conditions.

4.2 Corrections for Meter Compensation

Whatever flow meter is utilised, its actual volume flow rate output needs to be

converted to standard flow conditions. Single phase homogenous oil flow conversions

between actual and standard conditions require that the oil’s thermal expansion factor

and compressibility are known. In the case of an oil and water mix it is not clear what

thermal expansion factor and compressibility are appropriate or the combined fluid.

There is also a lack of data on the performance of densitometers when applied to oil

with water flow applications. There is some data, mainly theoretical produced in

Norway by CMI [6].

4.3 Sampling

Sampling techniques used across the full range of water cut and production flow

conditions found in the oil production industry today, including the more traditionally

common low water cut range, are not fully independently tested and verified. For

higher water cut production flows there is little independent verification of the

integrity of the sampling techniques used.

Sample systems often use a ‘mixer’ component upstream of the sample probe array.

This mixers purpose is to mix the oil and water flow such that the flow is effectively

7

homogenous at the sample point, thus making the sample representative of the water

to total flow rate ratio, i.e. the water cut. However, it is well known that oil and water

are ‘immiscible’. The definition of ‘immiscible’ fluids according to the Webster

Dictionary is: “incapable of mixing or attaining homogeneity”. So a mixer that

homogenizes immiscible fluids is an oxymoron, these mixers are attempting to mix

the unmixable. In reality, the best that can be reasonably hoped for is that a mixer can

produce ‘near’ homogenous flow at the sample position directly downstream of the

mixer, where the sample approximates the actual water cut to an acceptable

uncertainty.

Static (‘passive’) mixers are obstructions in the pipe on which the flow does work, i.e.

the flow supplies the work to mix the flow. Dynamic (‘active’) mixers are powered

mixing elements that do work on the flow, i.e. an external source supplies the work to

mix the flow. Presently, there is little research describing the effectiveness of static or

dynamic mixers.

It is generally assumed that dynamic mixers are very effective in any sampling

application. It is an open question to what static mixer designs have what

performance, especially at low flow rates and at the newer area of increasing interest,

high water cut flows. Industry does not yet know the limitations of different static

mixer designs. Questions needing answered include:

What is the optimum static mixer design?

Do static mixer designs have different optimum performance water cut ranges?

Does pipe / mixer diameter affect a mixers performance?

Do fluid properties significantly affect a mixers performance, and if so, how?

What is a realistic sample / water cut uncertainty expectation across the water cut

range?

How much more effective is a static mixer in vertical flow to horizontal flow?

Is vertical down flow a better static mixer / sample location than vertical up flow?

Can any dynamic (or ‘active’) mixer design be relied upon to give a near

homogenous mix and associated low uncertainty sample in any applications flow

conditions?

The standards API 8.2 [1] and ISO 3171[7] describe the mixing properties of different

fittings in theoretical terms. API 8.2 sets a limit to be 5% of water. ISO 3171 discusses

higher volumes of water. However, neither document states a required type of mixer

design, with a precise set of procedures and expected uncertainties. Neither of these

documents give any method for calculating the ability of a proprietary mixer to mix

the fluid. The only information on these issues is therefore supplied by the mixer

manufacturer. API 8.2 shows a rough guideline table to indicate the performance of

different mixing configurations, but it is short in technical detail, and very general in

nature.

4.4 Determination of Water Cut

The standard methods of water cut determination are not feasible for high water cuts.

Combined methods have to be utilised. First, the majority of the entrained water in a

sample (which has separated out) is removed and measured. The oil with the

remaining water residue (i.e. dissolved water and water in an emulsion) can then be

analysed with standard water cut analysis methods, such as distillation. Measuring

8

cylinders appear to also give good results. A good description is given in the API test

document on high water cuts [8].

During commissioning of the CEESI oil with water flow facility CEESI requested a

test sample water cut determination service from several companies throughout the

US. No quotations were received. The samples were deemed to have too high water

cuts for the service companies analysis techniques to operate correctly.

5. CEESI Oil with Water Test Facility

A CEESI review of industrial practice has indicted that:

sampling tends to be neglected by most operators of metering systems – there is a

dearth of independent higher water cut test sampling data,

there is a dearth of independent, higher water cut oil flow metering data, and,

high water cut flow sampling and metering is becoming increasingly important.

Figure 2. Schematic of CEESI Oil with Water Test Facility.

CEESI has built an oil with water flow test facility, which has Coriolis meter

individually measured oil and water inputs, a 2” to 8” pipe test section capability, long

clear viewing sections in horizontal and vertical piping, a flexible working section for

testing flow meters in different orientations, and a sampling section to determine the

distribution of mixing across the pipe cross sections. The water content can be ranged

from 1% to greater than 90%. The test facility operates at ambient conditions. This

paper discusses the design of this flow facility and some of the initial lessons learned

from commissioning this facility with different flow meters.

The system, shown in Figure 2, comprises two centrifugal pumps, that draw from

individual water and oil tanks. These pumps have controllers, but for lower flows,

control valves are required. The pumps are set to their respective required flow rates

according to the downstream oil and water Coriolis meter outputs. These set flow

rates set the water, oil and total flow rates as well as the water cut. Both pump outlets

send the flow to a phase stabilizer to reduce any instability caused by the pumps, flow

meters and control valves. Figure 3 shows the design of the phase stabilizer. It is a

header split by a dividing sealed plate such that the oil and water can flow separately

inside the header. Each cross sectional area is relatively large to induce low velocity

flow. This allows a relatively long retention time for the flow in the header where the

flow has time to settle. The oil and water flows are combined at the exit of a long

(seven degree) reducer, designed to minimize turbulence during mixing. This is to

reduce the distance required after comingling for the flow to mimic a flow of oil and

water in a long straight production pipe.

9

Figure 3. The Design of the Phase Stabilizer.

The test pipe section is in excess of 40 ft long, so for 2” ≤ D ≤ 8” there is at least 60D

available. The majority of the pipe length is clear plastic, allowing a clear view of the

mixing levels along the pipe. The test section configuration is versatile and can be

modified, changed and developed depending on the equipment being tested and the

aims of the test. In Figure 2, the objective was to model an actual sampler installation

mounted in a vertical down orientation. The sampling took place in the vertical down

leg, after a static mixer. However, this section can be replaced by any configuration

for testing, including a complete system if required. Flow meters and mixer / sample

systems can be tested in both the horizontal and vertical orientation. Figures 4 & 5

show photographs of previous configurations. After the test section the flow enters an

oil / water separator, from where the separated constituents return to the oil and water

supply tanks. Table 1 shows the present CEESI water in oil flow facility.

Figure 4. 4” & 6” Meter Sections Figure 5. Vertical Down Sample Section

Parameter Value

Pipe Diameter 2” ≤ Diameter ≤ 8” (up to 12” feasible)

Oil Grade Various (currently Shellsol D80 dyed red)

Maximum Average Velocity 14 ft/s

Water Cut (with salt if required) 0.2% to 90%

Oil Reference Uncertainty 0.2%

Water Reference Uncertainty 0.2%

Water Cut Reference Uncertainty 0.28% at 50% water cut

Table 1. CEESI Water in Oil Flow Facility Specifications.

The clear piping allows good flow visualisation along the test section. Figures 6

through 9 show sample 6” horizontal oil with water flow patterns (or ‘flow regimes’)

created with long upstream pipe runs at different water cuts and average flow

velocities. Figures 6 & 7 shows the oil and water are separated at the low average

10

Fig 6. Horizontal flow, 50%WC, 0.6 m/s Fig 7. Horizontal flow, 30%WC, 0.6 m/s

Fig 8. Horizontal flow, 5%WC, 0.6 m/s Fig 9. Horizontal flow, 50%WC, 0.9 m/s

Figure 10. 4”, 0.6 m/s, Water and Oil Flow

(80% WC) with Horizontal to Vertical Up Flow.

flow of 0.6 m/s (i.e. 2 ft/s) for 50% & 30% water cut respectively. Figure 8 shows that

the oil and water flow of 5% water cut was more mixed at the low flow of 0.6 m/s.

Figure 9 shows 50% water cut at the increased average flow velocity of 0.9 m/s (i.e.

2.25 times the dynamic pressure). Although still separated, there is visual evidence

here of the water phase having more entrained oil compared to the 0.6 m/s case in

Figure 6. Figure 10 shows the effect on the flow pattern of turning a 4”, 80% water

cut flow of average flow velocity of 0.6 m/s from horizontal flow to vertical up flow.

Significant mixing by the elbow and change of orientation is clearly visible. Unlike

mixing caused by horizontal bends this mixed flow continued downstream without

any visual evidence of separation occurring.

The specific results of the first tests on the effect of large water cuts on static mixers

for sampling systems are confidential, but one general point can be made from that

test program. Mixer design is critical. If the mixing mechanism induces a swirl

component to the flow, although this is a very large upheaval of the flow, centrifugal

forces move the heavier fluid (i.e. the water) to the outside of the pipe. That is, such a

‘mixer’ design acts as a vortex separator. This phenomenon was found for a particular

mixer design that produced an unchecked swirl component. A multi-point sampler

11

downstream of this ‘mixer’ design showed more oil at the centre of the pipe than the

periphery of the pipe. Further confirmation of this phenomenon came from CFD

simulations carried out by CPA [8]. Figure 11 show CFD results for 6” pipe with a

50% water cut and an average flow velocity of 0.6 m/s. Flow is from right to left.

Blue and red represents water and oil flow respectively. The horizontal inlet flow has

separated flow (as had the actual test case – see Figure 6). Mixing occurs at the 90o

vertical up bend, and the flow remains mixed as the flow turns another 90o bend back

to horizontal flow. This mixed flow begins to separate out after only a few horizontal

diameters. These CFD results were similar to what was observed at CEESI. A mixer

design that induced a swirl component on the flow was installed in the vertical down

section. Although a significant disturbance is evident from this CFD elevation view, a

cross sectional view of the flow at the exit of the mixer is inlaid onto Figure 11. The

swirl induced centrifugal force has caused a higher proportion of water at the pipe

periphery, and a higher proportion of oil at the pipe centre line. Figure 12 shows a

typical result for a multiport sample system downstream of such a mixer. In this

particular case the water cut was 50%. The mixer design inducing the swirl is not

mixing the flow effectively.

Figure 11. CFD result for 6”, 0.5%WC, 0.6 m/s.

Figure 12. Typical Multi-Port Sample Result Downstream of a Mixer that Induces an

Unchecked Swirl Component.

12

6. Oil with Water Flow Metering

The water cut prediction attained from the sampling system will be combined with the

total volume flow rate prediction from a flow meter to give the desired measurements,

i.e. the oil and water flow rates. Hence, the uncertainty of the oil and water flow rate

predictions will be dictated by both the sampling and flow meter uncertainties. As

discussed in Section 4.1 industry has yet to fully research the methodology of

metering oil with water flows.

During commissioning of the CEESI oil with water flow facility there was the

opportunity to test flow meters. Two of the meter designs tested are discussed in this

paper. The chosen meters are the turbine meter (chosen due to its popularity for this

application) and the cone DP meter.

6a. Turbine Meters

CEESI had available for testing a 4” Potter turbine meter (see Figure 13) and a 4”

Daniel turbine meter (see Figure 14). These meters were tested in the horizontal

orientation at various oil with water flow conditions. CEESI also built a 4” clear body

turbine meter for visual testing (see Figure 15 & 28). This meter was tested

specifically for visual confirmation of a turbine meters inter-action with various oil

with water flows.

Turbine meters link rotor revolution to volume flow. The number of revolutions per

unit time, i.e. the rotor rotational frequency (f), represents a volume flow rate (Q). The

rotor frequency and volume flow rate are related through the meters “K-factor” (K).

Equation 3 shows the generic turbine meter volume flow rate equation.

K

fQ (3)

Some turbine meter designs take a reading, or “pulse”, as each rotor blade passes the

counter, usually a magnetic pick off (see Figure 16). Some turbine meter rotors are

“rimmed” (see Figures 16 & 17), i.e. they have a band around the rotor blade tip

circumference with several magnetic ‘pips’ equally spaced between blade tips. This

increases the measurement resolution by significantly increasing the pulse count. The

Potter turbine meter tested is not rimmed, while the Daniel turbine meter tested is

rimmed. Adding a rim to the rotor design alters the flow around the blade tip region.

Therefore, rimmed and un-rimmed rotors can have different performances.

CEESI chose a Potter turbine meter to test as it is a basic traditional ‘un-rimmed’

turbine meter design that is widely accepted as trustworthy and reliable. CEESI had a

4” Potter meter available with a significant quantity of water flow test data. The 4”

Daniel turbine meter was tested as it was more common in industry, and was rimmed,

therefore allowing the two different turbine rotor designs to be tested. All turbine

meters tested were given > 50D straight run of pipe upstream of the meter inlet. This

initial series of ambient condition oil with water tests on these turbine meters

produced unexpected results which are as yet not fully unexplained.

Figure 18 shows all the oil with water flow data recorded on the horizontally installed

4” Potter turbine meter. With several operators reporting turbine meter operation in

very high water cut ranges, CEESI selected a desired water cut flow range of

0% ≤ ω < 100%.

13

Figure 13. 4”, Potter Turbine Meter

Figure 14. 4” Daniel Turbine Meter

Figure 15. 4” Clear Body Turbine Meter

Turbine meter K-factors tend to be Reynolds number sensitive. The Reynolds number

is defined by equation 4, where ‘m’ is the mass flow rate, ‘μ’ is the viscosity, ‘ρ’ is the

fluid density, and ‘D’ is the meter diameter.

D

mUD

4Re (4)

For any typical single component liquid flow application where the fluid pressure and

temperature do not significantly vary, the meter size (i.e. the diameter ‘D’) and the

fluid properties are fixed. Hence, it is possible to relate a turbine meter K-factor

directly to the average flow velocity ‘U’ instead of Reynolds number.

With oil with water flow tests, examining the data in this way has an added advantage.

The level of mixing between a horizontal oil and water flow is dictated by the energy

in the flow, i.e. the fluid dynamic pressure. For oil and water at given densities, and a

given water cut, the dynamic pressure, and hence the level of mixing of a horizontal

flow is dictated by the velocity. That is, the velocity is an indicator to the level of oil

and water mixing. A low velocity means separated flow (e.g. Figure 6), while a high

velocity means a well-mixed flow (e.g. Figure 8).

14

Figure 16. Assembly of a Liquid Turbine Meter with Rimmed or Un-Rimmed Rotor.

Figure 17. A Rimmed Turbine Meter Rotor with Magnetic ‘Pips’.

Figure 18 shows that the Potter meter has similar performance with oil only flow (i.e.

0% water cut) and water only flow (i.e. 100% water cut). That is, for a single

component flow the Potter turbine meter was found to have a repeatable performance,

regardless of the different fluid properties of oil and water. It is generally accepted

that such a result cannot be automatically assumed before calibration. Turbine meter

K-factors typically need to be re-established by proving, with any significant change

in flow conditions, e.g. change in viscosity and Reynolds number.

Figure 18. 4”, Un-Rimmed Potter Turbine Meter.

15

The Potter turbine meter water cut data was so surprising that initially it was assume

that there was an experimental problem. It is noted that at < 1 m/s this 4” turbine

meter was at the bottom end of its range. Low flow repeatability is a potential

problem, as it is with all flow meters. However, the tests were repeated to confirm the

results. The results were repeatable.

There is a general industry rule of thumb that oil with water horizontal flows are

separated at ≤ 1.5 m/s. It was observed at CEESI that this was approximately correct

for the Shellsol D80 oil used, with the water and oil horizontal flows looking well

mixed at > 2 m/s, in transition between 2 > U (m/s) > 1.5, and separated at < 1.5 m/s.

At higher velocities of approximately ≥ 2 m/s, where the oil and water were well

mixed, regardless of the water cut the Potter meter behaved similarly to water or oil

only flows. However, as the average flow velocity reduced below 2 m/s, a flow of any

water cut within the range tested of 5% ≤ ω ≤ 75% showed an increasing K-factor.

The fact that the K-factor shift coincides with physical observations of oil and water

dispersion through the meter gave CEESI some confidence in the first data sets in this

relatively new research area.

It had been expected that either the Potter meter would be immune to the fact that the

flow is a mix of oil and water (as current industry practice assumes) or, as the water

cut increased, an increasing discrepancy between performance and the oil only flow

calibration result would be seen. Although the Potter turbine meter did appear to be

immune to the > 2 m/s well mixed water cut flows, the < 2 m/s data for separated

flows (which is common in industry) showed that the meter performance was

adversely affected in the same way across the water cut range tested, i.e.

5% ≤ ω ≤ 75%.

It is counter intuitive that 5% water cut can cause as great an adverse effect as 75%

water cut. The experiment method was scrutinized. The tests were repeated. The result

was consistent. CEESI does not claim to understand these results and reports them as

found.

Figure 19. 4”, Rimmed Daniel Turbine Meter

It was noted that the Potter turbine meter is an un-rimmed meter. Rimmed and un-

rimmed turbine meters can have different performances, and as rimed meters are more

16

common in industry, it was decided it was necessary to test a rimmed meter. Figure 19

shows all the data gathered from the subsequent 4” Daniel rimmed turbine meter.

Due to the number of repeat tests on the Potter turbine meter to confirm the validity of

the unexpected results there was a reduced time frame available for testing the Daniel

meter. Hence, the 4” Daniel turbine meter was tested with water and oil flows only,

and then with 50% and 75% water cuts. Again, at < 1 m/s this 4” turbine meter was at

the bottom end of its range. Low flow repeatability is a potential problem, as it is with

all flow meters. In this case the test data was not repeated due to time constraints.

Repeatability is only assumed with this particular meters data set.

Figure 19 shows an apparent discrepancy between the 4” Daniel turbine meter’s

performance (K-factor) with water only and oil only. Across the velocity range tested

the water flow produced a near constant K-factor. Across the same velocity range the

oil flow produced the same K-factor as water at the high flow rates, but then the

K-factor increased as the flow reduced. Although repeat tests would be beneficial to

confirm this result it is noteworthy that this apparent discrepancy can be reasonably

explained by considering the water and oil data in terms of K-factor vs. Reynolds

numbers.

Figure 20 shows the Potter turbine meter data expressed as K-factor vs. Reynolds

number. It shows good overlap of the water and oil K-factors for the same Reynolds

number range. As the oil flows Reynolds number was reduced below that of the water

data the K-factor tended to rise. The Potter turbine meters K factor was fixed to a

constant value fit. A tighter fit was easily obtainable if the K-factor was to be fitted to

Reynolds number.

Figure 21 shows the Daniel turbine meter data. It shows good overlap of the water and

oil K-factors for the same Reynolds number range. As the oil flows Reynolds number

was reduced below that of the water data the K-factor began to significantly rise. The

Daniel turbine meters K-factor was fixed to a constant value fit from the water flow

data. Within the water flow rate range the meter performance with oil flow was the

same as the water flow. Only at lower Reynolds numbers did the K-factor diverge.

The oil flow data’s tendency to have a rising K-factor as the Reynolds number

reduces below the water flows test range is likely to be the normal turbine meter

performance when the Reynolds number has reduced below the point where bearing

friction becomes significant. The lowest three Reynolds number points tested in

Figure 21 were taken below the meters stated range, but are included for

completeness. As Potter and Daniel turbine meter are different designs there is no

concern about their performances being different at low Reynolds number tests.

The 4” Daniel turbine meters performance with water only and oil only appears to be

understandable and trustworthy. Turning attention to the water cut data, the 50% &

75% Water Cut data suggests that the Daniel turbine meters response to water-cut

flows is similar to the Potter turbine meters response. At higher flow rates the Daniel

turbine meters K-factor is similar to the calibration data of oil or water only flow, but

as the velocity drops the K-factor rises. Again, CEESI does not claim to understand

these results and reports them as found.

Finally, it was found from visual tests on the clear body turbine meter that the

presence of the turbine rotor had no effect on the dispersion of the oil and water, e.g.

17

Fig 20. 4” Potter Turbine Meter Oil & Water Only Data Sets.

Fig 21. 4” Daniel Turbine Meter Oil & Water Only Data Sets.

see Figure 15. The stratified flow recorded here (at 1 m/s at 20% water cut) was

completely unaffected by the presence of the fully serviceable freely rotating un-

rimmed turbine rotor. It had been expected that the turbine would facilitate a

reasonable amount of mixing. This was found not to be the case. Whatever flow

regime (i.e. oil and water dispersion) existed at the inlet to the turbine rotor remained

the flow regime through the rotor and downstream of the rotor.

A common industry assumption when metering oil with water flows is that a turbine

meter can give a total volume flow rate prediction. These turbines are usually

calibrated in single component liquid flow. These initial turbine meter results suggest

that both rimmed and un-rimmed turbine meters may produce volume flow rate

prediction biases if the effect of water cut is not accounted for, especially at moderate

to low total volume flow rates. The performance of turbine meters in oil with water

flow application appears to be difficult to predict, as may be expected when a single

phase, single component flow meter is applied to the adverse flow condition of

multiphase or multi-component immiscible liquid flows. The fluid mechanics

involved could be complex, e.g. oil properties could have an s impact on the turbine

meters performance in oil with water flow applications. CEESI does not profess to

18

understand the results of this small preliminary test program. CEESI is continuing to

conduct turbine meter oil with water flow tests with the aim of further developing an

understanding of the phenomena affecting the turbine meters performance.

6b. Cone Meters

Figure 22: Sectioned view of a Cone Meter (flow is left to right)

Figure 22 shows a sketch of a cone DP meter. The cone meter homogenous flow mass

flow rate equations are shown as equation 5. Cone meters are not traditionally utilised

for oil with water flow metering.

PPLpplrrttdt PAKPKEAPCEAm 222 (5)

Note:

m is the mass flow rate

E is the “velocity of approach” (a geometric constant)

At is the minimum cross sectional (or “throat”) area

Cd, Kr & KPPL are the discharge, expansion & PPL coefficients respectively

ρ is the fluid density

∆Pt , ∆Pr & ∆PPPL are the traditional, recovered & PPL DPs respectively.

Figures 23 through 27 show photographs of horizontal cone meter flow tests carried

out at CEESI. Water and oil (Shellsol D80) at ambient conditions were flowed

through a clear body 6”, 0.438 beta ratio (β) cone meter. Note the cone meter beta

ratio is defined as:

2

11

D

d

A

A

A

AA

A

A ccct (6)

where A & D are the inlet cross sectional area and diameter respectively, Ac & dc are

the cone element cross sectional area and diameter respectively, and At is the

minimum cross sectional (or “throat”) area.

The beta ratio of a cone meter, i.e. the relative size of the cone to the pipe diameter,

significantly affects the meters mixing capability. The smaller the beta ratio, the

higher the local cone flow velocity and the better the oil and water mixing. However,

the smaller the beta ratio, the greater the permanent pressure drop. Lower beta ratios

improve mixing but this advantage comes with increased operational cost.

The 0.438 β tested here has a relatively small beta ratio. Figure 23 shows a low speed

of 0.6 m/s (left to right) and a high water to total mass flow ratio (ωm) of 50%. At this

low speed the upstream flow is entirely separated. A significant amount of mixing is

seen to occur downstream the large cone even for this low speed. Figure 24 shows a

moderate speed of 1.6 m/s and a lower (but still substantial) water to total mass flow

ratio of 20%. Whereas the flow visually looked well mixed in the upstream pipe (with

the cone meter being installed >70D downstream of 90 degree bend), there is a

19

Fig 23. Cone meter, 0.6 m/s, ωm 0.5. Fig 24. Cone meter 1.6 m/s, ωm 0.2.

Fig 25. Cone 1.2 m/s, ωm 0.5. Fig 26. Cone meter 1.6 m/s, ωm 0.5.

Fig 27. Cone meter 1.6 m/s, ωm 0.75. Fig 28. Turbine meter 0.6 m/s,ωm 0.2

distinct change in colour downstream of the cone indicting a significant increase in

mixing. This pattern was consistent across all tests. Figures 25 & 26 both show a high

water to total mass flow ratio of 50%. Figure 25 shows 1.2 m/s produced a moderately

separated upstream flow and Figure 26 shows 1.6 m/s produced a more mixed

upstream flow. Both flows were significantly more mixed downstream of the cone.

Even at a water to total mass flow ratio of 75% at 1.6 m/s (see Figure 27) where the

inlet flow is stratified, the flow downstream of the cone element is mixed. In all tests

the mixing effect extended dozens of diameters downstream before separation began

to be evident.

The cone element looks to be a good oil with water mixer. Furthermore, it is simpler

than the traditional mixer designs, and can also be used as the flow meter. That is, a

cone meter could potentially be used as a joint mixer and flow meter instead of the

current practice of having a mixer component and a separate meter component. Like

all mixer designs, a cone would benefit from vertical flow to increase mixing before

the cone.

6b.1. Oil with Water Flow and the Analogy with Wet Gas Flow

There is no methodology in the public literature for analysing oil with water flow

through a cone (or any generic DP) meter. However, there is a direct analogy with wet

gas flow through a DP meter. Whereas wet gas flow is a two-phase flow of gas and

liquid, water cut flow metering is a single phase two component flow of oil and water.

20

Therefore, by replacing the gas phase with the oil, and the liquid phase with the water,

parameters developed for analysing DP meter wet gas flow can be converted to an

equivalent for analysing water cut flows. In this way, the substantial wet gas flow DP

meter research can be utilised to analyse a cone meters reaction to oil with water

flows. The following is a description of these modified parameters.

A modified Lockhart-Martinelli parameter ( *

LMX ) can be defined as:

water

oil

oil

water

LMm

mX

* (7)

where oilm an waterm are the oil and water mass flow rates and oil and water are the

oil and water densities respectively. The density ratio (DR*) is defined as equation 8.

water

oilDR

*

(8)

An oil densimetric Froude number (Froil*) can be defined as equation 9. Here, g is the

gravitational constant (9.81m/s2).

oilwateroil

oil

oilgDA

mFr

1* (9)

The uncorrected oil mass flow rate prediction can be called the ‘apparent’ oil mass

flow, apparentoilm ,

. This is calculated by equation 10.

toildtapparentoil PCEAm 2,

(10)

The oil flow rate prediction positive bias induced by the presence of the water can be

called an ‘over-reading’ (ORoil). This can be expressed as a ratio (equation 11) or as a

percentage.

oil

apparentoil

oilm

mOR

, (11)

The water-cut ( ) is defined as equation 12. Note that waterQ and oilQ are the water

and oil actual volume flow rates.

total

water

oilwater

water

Q

Q

QQ

Q

(12)

A water to total mass flow ratio (ωm) can be utilised, as shown in equation 13.

total

water

oilwater

water

mm

m

mm

m

(13).

If fully mixed the homogenous density (ρh) can be calculated by equation 14.

moilmwater

wateroilh

1

. (14)

21

6b.2. DP Meter Diagnostics

Figure 29. Cone meter with instrumentation sketch and pressure fluctuation graph.

Figure 29 shows a cone meter with a third pressure tap allowing the traditional DP

(ΔPt), a recovered DP (ΔPr), and a permanent pressure loss DP (ΔPppl) to be read. This

allows a full patented generic DP meter diagnostic suite to be available. Stobie et al

[9] gives a detailed review of these diagnostics. A summary is given below.

The sum of ΔPr and ΔPppl must equal ΔPt (equation 15). This fact allows a DP reading

check. Each DP offers an independent flow rate prediction, i.e. the traditional DP

meter flow rate prediction (equation 16), the expansion DP meter flow rate prediction

(equation 17), and the PPL DP meter flow rate prediction (equation 18).

PPLrt PPP uncertainty ± θ% (15)

Traditional Flow Equation: tdt PCEAm 2 uncertainty ± x% (16)

Expansion Flow Equation: rrt PKEAm 2 uncertainty ± y% (17)

PPL Flow Equation: PPLppl PAKm 2 uncertainty ±z% (18)

Every cone meter body is in effect three flow meters. These three flow rate

predictions can be compared. The percentage difference between any two flow rate

predictions should not be greater than the root mean square of the two flow rate

prediction uncertainties. Table 2 shows the flow rate prediction pair diagnostics.

Flow Prediction Pair % Actual

Difference

% Allowed

Difference

Diagnostic Check

Traditional & PPL % % 1%%1

Traditional & Expansion % % 1%%1

PPL & Expansion % % 1%%1

Table 2: Flow rate prediction pair diagnostics

With three DPs read, there are three DP ratios:

PPL to Traditional DP ratio (PLR): ( PPLP / tP )reference , uncertainty ± a%

Recovered to Traditional DP ratio (PRR): ( rP / tP )reference , uncertainty ± b%

Recovered to PPL DP ratio (RPR): ( rP / PPLP )reference , uncertainty ± c%

A cone meter’s DP ratios are characteristics of that meter. DP ratios found in service

can be compared to their expected values. The difference between a found and

expected value should not be greater than the reference DP ratio uncertainty. Table 3

shows the flow rate prediction pair diagnostics.

22

DP Ratio % Actual to Ref

Difference

% Reference

Uncertainty

Diagnostic Check

PLR % %a 1%%1 a

PRR % %b 1%%1 b

RPR % %c 1%%1 c

Table 3: DP Ratio diagnostics

Any inference that Equation 15 does not hold is a statement that there is a malfunction

in one or more of the DP transmitters. The sum of ΔPr and ΔPppl gives an ‘inferred’

ΔPt,inf. The inferred and directly read traditional DP should not be greater than the root

mean square of the combined DP transmitter uncertainties. Table 4 shows the DP

reading integrity diagnostics.

% Actual to Inferred

Traditional DP Difference

% RMS Combined DP

Reading Uncertainty

Diagnostic

Check

% % 1%%1

Table 4: DP Reading Integrity Diagnostic

Table 5 shows the seven possible situations where these diagnostic would signal a

warning. For convenience we use the following naming convention:

Normalized flow rate inter-comparisons: x1 = %% , x2 = %% , x3 = %%

Normalized DP ratio comparisons: y1 = %% a , y2 = %% b , y3 = %% c

Normalized DP sum comparison: x4 = %%


tP & pplP -1 ≤ x1

1 -1< x1 or x1 1 1 ≤ y1

1 -1< y1 or y1 1

tP & rP -1 ≤ x2

1 -1< x2 or x2 1 1 ≤ y2

1 -1< y2 or y2 1

rP & pplP -1 ≤ x3

1 -1< x3 or x3 1 1 ≤ y3

1 -1< y3 or y3 1

readtP , & inf,tP -1 ≤ x4

1 -1< x4 or x4 1 N/A N/A

Table 5: The DP meter possible diagnostic results

Figure 30: Normalized diagnostic box (NDB) & results, DP check included.

A box can be drawn, centred on a graph’s origin, and four points plotted representing

seven diagnostic checks (as shown in Figure 30). If the meter is fully serviceable all

points must be inside the box. One or more points outside the box indicate a

malfunction. The diagnostic pattern of an alarm offers information on the source of

the malfunction. Different malfunctions can cause different diagnostic patterns.

23

6b.3. Cone Meter Baseline Flow Data

The cone meter with a downstream pressure tap has three flow rate calculations, i.e.

equations 16, 17 & 18, with the discharge coefficient (Cd), the expansion coefficient

(Kr), and the PPL coefficient (KPPL) respectively. Initial testing was conducted on oil

flow only and then water flow only. These ‘baseline’ results for the flow coefficients

and DP ratios are show in Figures 31 & 32.

Figure 31. 6”, 0.483β Cone Meter Flow Coefficients in Homogenous Liquid Flow.

Figure 32. 6”, 0.483β Cone Meter DP Ratios in Homogenous Liquid Flow.

Figure 31 shows the cone meter had a fitted discharge coefficient with a 1%

uncertainty for either water or oil flow. The expansion and PPL coefficients were

fitted to 3% and 2.5% respectively. These check meters are not as accurate as the

traditional meter, but give important secondary flow rate information for diagnostics.

The DP ratios shown in Figure 32 are unaffected by whether the flow is oil or water,

and have been fitted to liner lines. The PLR fit has 4% uncertainty, the PRR fit has

6% uncertainty, and the RPR fit has 7% uncertainty. These uncertainties may look

large but diagnostic examples show these results are still very useful in practice.

6b.4. Cone Meter and Water in Oil Flow Data

Figure 33 shows the three flow rate prediction responses to oil with water flow in

terms of the percentage over-reading (ORoil%) vs. the modified Lockhart-Martinelli

parameter (XLM*). All three flow rate predictions give approximately the same over-

reading. For wet gas flow, research has shown that a combination of the traditional,

expansion & PPL flow rate predictions being approximately equal is a signature of

fully homogenized flow. Furthermore, it was noted that for this constant density ratio

24

Figure 33. 6”, 0.483β Cone Meter Oil with Water Data.

(DR*) of 0.82, varying the oil densiometric Froude number (Froil*) had no appreciable

effect on the over-reading. For wet gas flow, research has shown that this too is a

signature of fully homogenized flow. Hence, the three flow rate predictions matching

each other is a diagnostic check that the cone meter is mixing the oil with water flow

to a near homogenous flow.

The theoretical correction factor for a DP meter with a fully homoginized oil with

water flow can be shown to be equation set 18 & 19.

2**

,,

1 LMLM

apparentoil

oil

apparentoil

oil

XCX

m

OR

mm

(18)

oil

water

water

oilC

(19)

Figure 33 shows the homogenous oil with water correction results. It is assumed from

the outset that the oil and water densities are known. To apply equation set 18 & 19,

equations 7 & 10 must be used. Equation 7 requires the water to oil flow rate ratio be

supplied from an external source. In Figure 33 this external source is the test facility

reference meters. In the field, the external source is the sampling results. The

homogenous correction method offers a dramatic improvement of the oil flow

prediction.

Figure 33a. 6”, 0.483β Cone Meter Oil with Water Homogenous Model Correction.

25

Figure 33a only shows the homogenous models correction results. All three oil flow

predictions have approximately 3% uncertainty. It is noteable that the correction is

not a data fit, but rather a fully theoretical correction factor. This is the reason there is

a slight negative bias in the results in Figure 33a, especially at higher modified

Lockhart Martinelli parameter (XLM*) values. It is expected that if the cone meter had

been installed vertically up, the enhanced mixing would mean the meter performance

would be closer still to homogenous flow.

This data can be fitted to a tighter fit. Linear data fits i.e. the form shown as equation

20, have been fitted here as a simple example. (Other more complicated data fits can

be chosen.)

*

,,

1 LM

apparentoil

oil

apparentoil

oilMX

m

OR

mm

(20)

The three gradients for the three meters were found to be:

M traditional = 0.9857, M expansion = 0.9825 & M PPL = 0.9650.

Figure 34. 6”, 0.483β Cone Meter Water in Oil Linear Fit Corrected Results.

Figure 34 shows the 6”, 0.483β cone meter water with oil results when the oil flow

rate over-reading is corrected for a known water to oil flow rate ratio with these linear

fits. The traditional meter has the same corrected oil flow rate prediction uncertainty

as the theoretical homogenous model. The expansion meter has a slightly higher

corrected oil flow rate prediction uncertainty. With the exception of a single outlier,

the PPL meter has a slightly reduced corrected oil flow rate prediction uncertainty. It

is therefore possible that cone expansion meters, or cone PPL meters, may give as

good or better an oil over-reading correction, as the traditional meter correction.

6b.5. The DP / Cone Meter Diagnostic System Applied to Water with Oil Flows

The diagnostics summarized in Section 6b.2 work with cone meters. These

diagnostics are designed as homogenous flow DP meter diagnostics. The literature

shows these patented diagnostics correctly indicate that a problem exists when a cone

meter suffers various common problems, such as:

incorrect keypad entered inlet or cone diameter

DP transmitters problems (e.g. drift, over-ranging or incorrect calibration)

partial blockage at cone

deformation / shift in alignment of the cone element

26

disturbed flow at cone meter

incorrect keypad entry of flow parameters (e.g. discharge coefficient)

wet gas flow (in the common event the wet gas is not homogenously mixed)

Of the diagnostics described in Section 6b.2 for single phase homogenous flow, the

only known problem that the diagnostic system does not monitor for is density errors.

Whereas this is a minor limitation to the diagnostic system when applied in its normal

homogenous flow applications, it is a benefit in an oil with water application.

The integrity of the traditional mixer / sampler and volume meter oil with water

metering system is wholly dependent on the integrity of the sample system and the

integrity of the volume meter output. However, traditionally the volume meters

(primarily ultrasonic and turbine meters) diagnostics are compromised when applied

to oil with water flows. Furthermore, traditionally the mixer / sample system has no

internal method of monitoring its own effectiveness.

A cone meter could be developed to be both mixer and flow meter, thereby

eliminating the requirement for two separate pipe components of a mixer and a meter.

Such a system can be installed in any pipe orientation. It could be installed in vertical

flow, as is common practice for stand-alone mixer designs with sample systems

downstream. However, flow velocity and beta ratio dependent, it is potentially

possible that such a system could be successfully installed in horizontal flow, thereby

alleviating the requirement for vertical flow sections to aid mixing. If the cone

effectively mixes the flow, the homogenous fluid diagnostic system will be unaffected

by the fluid being a mix of oil and water. Hence, cone meters have the potential to

have workable diagnostics in oil with water applications.

Finally, note that traditional mixer designs have no diagnostic system, i.e. no way of

indicating the quality of the mixing. If a cone meter with a downstream pressure tap is

used as a mixer, the cone meters comparison of the three separate flow rate

predictions offers a monitoring system to the quality of the mixing. The closer the

three flow rate predictions match, the better mixed the oil and water are.

6b.5.1. Cone Meter Diagnostics in Operation using Oil with Water Flow Data

The 6”, 0.483β clear body cone meter (shown in Fig 23 through 27) was calibrated

with water only flow and oil only flow. The results are shown in Figures 31 & 32.

Random samples of this correct baseline data diagnostic results plotted on a NDB (see

Section 6b.2 , Figure 30) are shown in Figure 35.

Figure 35. Random Examples of Baseline Diagnostic Results.

27

First, consider random examples of the DP meter in use with water or oil flows when

there is a problem. Say there was an inlet diameter keypad entry bias where the inlet

diameter 6.065” (i.e. 6”, schedule 40) was used instead of the correct value of 6.00”.

The traditional flow prediction has an error induced of +10%. Figure 36 shows the

diagnostic result for correct and incorrect inlet geometries on a randomly chosen oil

only flow. The diagnostics can identify a problem exists.

Figure 36. Results from Oil Flow Example for Incorrect Inlet Diameter

Say there was a cone diameter keypad entry bias where the cone diameter of 5.252”

was incorrectly entered as 5.3”. The traditional flow prediction has an error induced of

-5.5%. Figure 37 shows the diagnostic result for the correct and incorrect cone

geometry being used. The diagnostics clearly identified when the problem exists. The

diagnostic system is shown to operate correctly with water or oil flows, as required.

Figure 37. Results from Water Flow Example for Incorrect Cone Diameter

Figure 38. Results from Water in Oil Flows when the Cone Meter is Serviceable.

Figure 38 shows sample data only (so as not to over-crowd the NDB), of various oil

with water flow examples, when the cone meter is fully operation. The diagnostic

system (’Prognosis’) does not look for density errors. Therefore, the diagnostic system

28

is immune, or ‘tolerant’, of the oil with water density ‘issue’. The presence of two

immiscible fluids does not adversely affect the operation of the diagnostic system.

The diagnostics monitor the meters serviceability in oil with water flow applications.

Figure 39 shows diagnostics results from a serviceable cone meter with oil with water

flow, and when that meter has an error induced by an incorrectly entered discharge

coefficient. The correct discharge coefficient is: Cd=0.791+(-2e-8*Re). In this

example the erroneous discharge coefficient used is: Cd=0.791+(-2e-7*Re). The

induced error was -4.6%. When the meter was serviceable no alarm was given. When

the discharge coefficient was incorrect an alarm was raised.

Figure 39. Results for Correct and Incorrect Cd in Oil with Water Flow Application.

Figure 40 shows diagnostics results for when a DP transmitter read the traditional DP

correctly and for when it was saturated (or ‘over-ranged). The saturated transmitter

associated flow rate prediction error was -2.8%. When the meter was serviceable no

alarm was given. When the DP transmitter failed an alarm was raised.

Figure 40. Results from DPt Read Correctly & When Saturated / Artificially Low.

Figure 41. Results from DPt Read Correctly & When Drifted / Artificially Low

Figure 41 shows diagnostics results for when a DP transmitter read the traditional DP

correctly and for when it had drifted low. The drifted transmitter associated flow rate

prediction error was +1.5%. When the meter was serviceable no alarm was given.

When the DP transmitter failed an alarm was raised.

29

Conclusions

There is an increasing commercial requirement for low uncertainty oil with water

flow measurement across the entire water cut range, i.e. 0% ≤ ω < 100%. The

literature, and the standards documents, do not give a comprehensive description of

the mixing, sampling and flow measurement procedures required to obtain some

metering output uncertainties. A great deal of flow testing is required for industry to

improve its knowledge of the operation and performance of meters and sampling

systems at high water cuts.

CEESI have developed an oil with water facility designed specifically to carry out

testing at high water cuts with a good reference uncertainty. This flow line has been

commissioned and used successfully for testing of static mixers and flow meters.

The turbine flow meters were found to be adversely affected by oil with water flows,

especially at lower flow velocities, in a way that has not been adequately explained.

The cone meter was found to be adversely affected by oil with water flows. Utilising a

wet gas flow meter analogy, it was found that a 6”, 483β cone meter’s performance in

oil with water flow applications could be characterised by the homogenous model. As

such, a sampling systems water cut result could be used with the homogenous model

to allow the cone meter to predict the oil and water flow rates. It was also found that

the generic DP meter diagnostic system ‘Prognosis’ was unaffected by the ‘adverse’

flow conditions when applied on a cone meter in an oil with water flow application.

References:

1. API MPSM Chapter 8.2 “Sampling”

2. “Coriolis Mass Flow Meter Performance with Water, Air, Dry – Gas & Wet – Gas” GRI-04/0172, Sept 2004, report No. 04/0172

3. Weinstein J., “Multiphase Flow in Coriolis Mass Flow Meters – Error Sources and

Bes Practice”, North Sea Flow Measurement Workshop, October 2010, St Andrews,

UK.

4. Andersen O. et al “Two Component Coriolis Measurement of Oil and Water at

Low Velocities”, North Sea Flow Measurement Workshop, October 2004, St

Andrews, UK.

5. Brown G. et al “Oil / Water Tests on a 4-Path Ultrasonic Meter at Low Flow

Velocities”, North Sea Flow Measurement Workshop, October 2006, St Andrews,

UK.

6. “Fiscal measurement of oil with high water fraction: Phase 1: Sensitivity study for a

turbine meter based fiscal metering station” CMI 2007

7. ISO 3171 (1988) “Petroleum liquids - Automatic Pipeline Sampling”

8. “API High Water Content Phase II – Analytical Test Methods” Final Report

5/05/05

8. D. Sawchuck, Raphael Selirio CPA Computational Solutions Ltd. Private

Communication.

9. Stobie G. et al “Flow Disturbance Cone Meter Testing”, North Sea Flow

Measurement Workshop, October 2013, Tonsberg, Norway.

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Stamp

1

31st International

North Sea Flow Measurement Workshop 22-25 October 2013

Field test for the comparison of LNG static and dynamic

mass measurement methods

Tore Mortensen, Justervesenet

Henning Kolbjørnsen, Justervesenet

Content 1. Introduction ......................................................................................................... 2

2. Test specification ................................................................................................. 2

2.1 Test procedure (measurement related)…………………………………………………..3

2.2 Road Tanker…………………………………………………………………………………....3

2.3 Weighbridge…………………………………………………………………………………...4

2.4 LNG terminal (LCNG refuelling station)………………………………………………5

3. Measuring instrument specifications and traceability ........................................... 6

4. Results ................................................................................................................. 7

5. Conclusion .......................................................................................................... 8

6. Acknowledgements ............................................................................................. 8

7. References ........................................................................................................... 8

A.1. Detailed observations from testing 10/9 ............................................................... 9

A.2. Road debris and road tanker semi-trailer weight (Dummy test) ........................... 13

A.3. Weighbridge calibration ..................................................................................... 13

fltough

Stamp

2

1. Introduction This paper summarizes the results of a series of measuring comparisons for LNG static and

dynamic mass measurements performed onsite. The comparison consists of observing the

difference in indicated and measured LNG mass from several measurement systems.

Measurement systems belong in one of the two categories:

1) Static mass measuring system by the use of a scale (truck weighbridge) and

2) Dynamic mass measuring system (coriolis mass flow (CMF) meter).

A LNG road tanker is utilized to transfer the mass of LNG between the two categories of

measurement systems. The dynamic measurement from simultaneously unloading of a road

tanker semi-trailer filled with LNG through a coriolis flow meter is compared to the

measuring results of a static non-automatic weighbridge used before and after the unloading

of the road tanker. The differences in measured mass are noted. A specification of the test

procedure and a description of the different measuring instruments involved are given in

chapter 2, test specification.

A total of 5 reproduced tests were conducted, one on each of the days 5/9, 10/9, 18/9, 8/10

and 15/10 in the year 2012 and the results are given in chapter 4. In one of the tests (10/9)

some additional observations related to repeatability of the weighbridge and the stability of

the flow during unloading was obtained. A summary of these observations are given in

appendix A.1.

There are different and to some degree incomplete traceability chains for the measuring

instruments that have been tested. The weighbridge utilized for testing was calibrated by

Justervesenet and is traceable to national weight standards. The result from the weighbridge

calibration is given in appendix A.3.

The results of the reproduced testing will indicate the degree of agreement between the CMF

meter under test and the weighbridge. More important, the testing that was performed also

demonstrates and provides experience with the measurement capability for meter comparison

and a method for validation in field.

2. Test specification Access to facilities for testing was obtained in cooperation with the Norwegian gas company

Gasnor. Gasnor manages in addition to LNG production also distribution of LNG by use of

ship tankers as well as road tankers.

The Gasnor LNG production facility where the road tanker semi-trailer is filled with LNG is

located at Kollsnes, close to Bergen on the west coast of Norway. The receiving terminal

where the LNG is transferred from the road tanker to a stationary storage tank is located at

Haukås approximately 70 km by road east of the production facility location. Access to a

weighbridge was obtained at the premises of the company Stena Recycling located on the

route between the LNG production facility and the LNG receiving terminal.

For the testing procedure the road tanker is first filled at the production location for LNG at

Kollsnes and then the complete mass of road tanker semi-trailer and LNG is measured using

3

the weighbridge at Stena Recycling. After the weighing the driver takes the road tanker to

Haukås and the LNG bulk cargo is transferred to the storage tank at the LNG terminal. In the

transfer line between the road tanker and the terminal storage tank there is a coriolis mass

flow meter. The rate of flow of LNG is approximately constant throughout the transfer. When

the LNG transfer is complete the driver takes the road tanker to Laksevågneset and the mass

of the now empty road tanker semi-trailer is measured using the same weighbridge at Stena

Recycling. The mass difference from the weighing which equals the LNG transferred at the

terminal is compared to the mass measurement result from the coriolis mass flow meter. A

step by step description of the test procedure is given in the next section. The next few

sections also gives more details and characteristics on the different elements employed in the

testing.

2.1 Test procedure (measurement related)

1. Mass of road tanker semi-trailer with LNG is measured using weighbridge at Stena

Recycling [Mfull,WB]

2. LNG transferred from road tanker to storage tank is measured using the coriolis meter

installed at the Haukås terminal [MCMF]

3. Mass of empty road tanker semi-trailer is measured using weighbridge at Stena

Recycling [Mempty,WB]

2.2 Road Tanker A road tanker tractor of make Scania was utilized to transport the LNG between the

production facility, the weighbridge and the receiving terminal. The particular road tanker

semi-trailer has a capacity of approximately 45 m3 (or 22 ton) of LNG. The tractor itself has a

weight of 10 ton while the net weight of the semi-trailer is 17 ton.

All measurements were performed using the same road tanker operated by the same driver.

The driver was made familiar with the test procedure before the testing commenced and he

was also trained for the measurements and the use of the elements involved with the

measurements.

The mass of the tractor will change during transport due to consumption of fuel, oil, etc. To

eliminate this type of error in the measurements the tractor was disconnected from the semi-

trailer during weighing on the weighbridge. An alternative method would be to estimate the

amount of mass consumed during transport or to top off fluid at every weighing but the tractor

being the front of the vehicle is also more exposed to road debris so this method was

preferred. To analyse the effect of road debris on the road tanker a “dummy” test was

performed where the weighing took place as for a normal test and the road tanker was driven

the regular route as for the testing but no transfer of LNG cargo took place at the LNG

terminal. A description of this “dummy” test and its result can be found in appendix A.2.

Also for the measurement of the road tanker LNG mass it is important to note that the semi-

trailer cargo tank represents a closed system. Between the full and empty weighing at the

Stena Recycling weighbridge there is no flaring or escape of gas from cargo weight. This is

possible due to the prominent isolated tanks on this type of vehicle. The pressure buildup

4

under normal circumstances is less than 0.3 bar per 24-hour period and the excess pressure

buildup is released during transfer at the receiving terminal.

Figure 1: LNG road tanker (tractor plus semi-trailer) on weighbridge

at Stena Recycling, Laksevågneset.

2.3 Weighbridge

The weighbridge at Stena Recycling is located approximately 21 kilometers away from the

Haukås LNG terminal. The location and the weighbridge load plate has good shield against

wind so that unstable weighing conditions are avoided as far as possible. A scale indicator

with an optional resolution of 2 kg of type Flintab 47-10 was connected to the weighing cells

of the weighbridge. The load plate consists of two separate 2 x 9 meter cast sections resting

on a total of 6 weighing cells. To establish traceability for the weighbridge it was calibrated

both before and after the 5 series of tests with weights traceable to national weight standards.

Result from the calibration is given in appendix A.3.

For the weighing procedure the road tanker initially drives onto the weighbridge. Then the

road tanker semi-trailer is disconnected from the tractor and the tractor is driven off the

weighbridge leaving only the disconnected semi-trailer to be weighed. The hysteresis effects

of the weighbridge have to be considered in the calculation of the weighting result.

5

Figure 2: Weighbridge at Stena Recycling. The

weighbridge and its load plate has good shield

against moderate wind.

2.4 LNG terminal (LCNG refuelling station)

The LNG terminal located at Haukås is a LCNG (liquefied-compressed natural gas) refuelling

station for city buses in the Bergen area. Approximate capacity of stationary vertical LNG

storage tank located at the terminal is 80 m3. At the terminal, on the LNG inlet side of the

storage tank, there is a stationary mounted coriolis meter of make Emerson Micro Motion that

may be used for billing purposes.

6

Figure 3: LCNG terminal at Haukås. Transfer of LNG from road

tanker to storage tank.

3. Measuring instrument specifications and traceability Weighbridge:

Indicator Flintab 47-10

Weighing cells 6x Landgraff & Flintab N.A.

Capacity 60 ton

Resolution 20 kg (2 kg option)

Load plate 2x9 meter cast sections

Traceability:

Weighbridge was calibrated by Justervesenet before testing commenced on the 30th

of May

2012 and again after the testing was ended on the 27th

of November. (See also appendix A.3).

All indications of weight are either in the range 39.5 to 40.1 ton (full semi-trailer, Mfull,WB) or

in the range 17.3 to 20.0 ton (empty semi-trailer, Mempty,WB). The actual weighbridge

corrections (cΔWB) for the 5 measurements of mass difference weighing (MΔWB = Mfull,WB –

Mempty,WB) are listed below:

Weighbridge corrections (actual):

Indicator mass difference reading

(Mfull,WB - Mempty,WB = MΔWB) Correction (cΔWB)

20 050 kg (5/9) +2 kg

22 561 kg (10/9) +3 kg

22 202 kg (18/9) +3 kg

22 384 kg (8/10) +3 kg

22 168 kg (15/10) +3 kg

7

Coriolis Mass Flow Meter:

Sensor 2 inch Emerson Micro Motion Elite

Transmitter Micro Motion model 1700

Traceability:

No information available for cryogenic application.

4. Results

Using the weighbridge as the reference, the result of the comparison of the mass metering

systems can be summarized as shown in table 1 and figure 5 below.”MCMF” is the mass

measurement results from dynamic measuring of LNG by coriolis meter and the “Mc,ΔWB” is

the corrected static mass measurement difference (full semi-trailer minus empty semi-trailer)

from weighbridge readings.

The percent error of mass, is calculated according to the formula:

Table 1: Relative mass measurement differences from comparison results

Date: 20120905 20120910 20120918 20121008 20121015 Average MAD

-0,12 % -0,08 % -0,19 % -0,03 % -0,13 % -0,11 % 0,05 %

Figure 5: CMF relative error compared to weighbridge

-0,12

-0,08

-0,19

-0,03

-0,13

-0,25

-0,20

-0,15

-0,10

-0,05

0,00

20120905 20120910 20120918 20121008 20121015

[% E

rro

r o

f m

ass]

[Test date]

CMF relative error to weighbridge

8

As can be seen from table 1 and figure 5 above the results shows good agreement between the

weighbridge and the Emerson Micro Motion coriolis mass flow meter as the error is relatively

small. The mean error of measured relative mass difference is -0.11% with a mean absolute

deviation (MAD) of 0.05%.

Measurement uncertainty

This report has no calculation of measurement uncertainty. The possibility to make

validations have been limited but still the list below should indicate possible significant

influences for the readings of transferred mass.

Comparison CMF meter vs. weighing result

Uncertainty in calibration value of weighbridge (significant)

Stability of weighbridge (significant)

Loading effect and hysteresis of weighbridge (significant)

Change in mass of the road tanker related to water (rain), dust and road debris (may be

significant)

Startup conditions (may be significant)

Different operating conditions regarding temperature and pressure (minor)

Flow profile (minor)

Mechanical installation effects (minor)

5. Conclusion

The results of the comparisons indicate good consistency of data between the dynamic

metering method of the coriolis mass flow meter (-0.11% error, 0.05% MAD) with the static

metering method of weighing.

The low spread and relatively small value of MAD (0.05%) indicates that the influences from

random errors are low. This shows that the method of using road tanker on weighbridge as

reference for LNG mass can be performed by careful measurements and that the method gives

good results in field.

6. Acknowledgements This work is carried out as part of a so-called Joint Research Project (JRP) under the

European Metrology Research Program (EMRP) that is jointly supported by the European

Commission and the participating countries within the European Association of National

Metrology Institutes (EURAMET e.V).

7. References [1] EMRP 2009, Joint Research Project Protocol, Annex Ia

www.lngmetrology.info

http://www.lngmetrology.info/

9

A.1. Detailed observations from testing 10/9

In one of the tests (10/9) some additional observations related to repeatability of the

weighbridge and the stability of the flow when unloading was done.

General

The test included three different major phases:

Phase no. 1: Filled road tanker semi-trailer (tractor disconnected and excluded) was

weighed at a non-automatic weighbridge at Stena Recycling.

. Figure 6: Weighing of road tanker semi-trailer at Stena

Recycling

Phase no. 2: The road tanker semi-trailer delivered LNG at Haukås bus terminal into a

vertical storage tank.

10

Figure 7: LNG transfer from road tanker to stationary tank at

LNG terminal

Phase no. 3: Empty semi-trailer (excluded the tractor) was again weighed at the non-

automatic weighbridge at Stena Recycling.

Additional activity:

Prior to phase no. 1 the non-automatic weighbridge was calibrated. The calibration is

documented in chapter 3 and A.3 in this report. During phase 1 three additional readings of

the total mass of the road tanker was made.

Below follows detailed description and observations from the different phases:

Phase no. 1: First weighing

The road tanker was placed on the non-automatic weighbridge and the tractor was removed

from the load plate and the weighbridge.

Between each of the three readings the tractor drove on and then off the load plate to obtain a

significant change in the load of the weighbridge.

The following observations were made:

Weighing of just the road tanker semi-trailer (d=2 kg)

Weighing no. Indication

Observation 1 39 958 kg



Mean of observation 39 954.7 kg

Std. Dev. 3.1 kg

Std. dev. of mean 1.8 kg

Weighing of complete vehicle including tractor and semi-trailer (without driver)

Total mass of vehicle 50 060 kg

Estimated mass of tractor 10 105.3 kg

11

Some remarks:

During this phase there were no rainfall and the surface of the vehicle was free from

droplets.

The platform was relatively clean and free from significant objects.

Phase no. 2: Flow metering At the LNG terminal there is a coriolis mass flow meter available:

CMF: As part of the fixed installation of the storage tank, there is a 2 inch mass flow

meter of type Micro Motion CMF200 which may be used for billing purpose.

The meter is read at the start and end of the filling.

Readings of meter (uncorrected)

Start End Difference

(End- Start)

CMF 1 490 736 1 513 282 22 546

During the filling of the tank, several parameters were observed and noted: Filling

(approx.)

[ton]

CMF Pressure

Mass flow

[kg/h]

Volume

flow [m3/h] Density

[kg/m3]

T

[°C]

Semi-trailer

[bar]

In front of

CMF

[bar]

Top of tank

[bar]

2 15 550 35.2 439.7 -148.7 8.2 4.37

7.9 15 840 35.6 440.1 -149.1 7.6 3.5

13 15 404 34.8 440.2 -149.1 7.8 3.3

17.5 15 140 34.2 440.3 -148.9 7.8 6.6 3.3

21.5 15 220 34.5 439.9 -147.9 7.6 6.6 3.3

Average 15 431 34.9 440.0 -148.7 7.8 6.6 3.4

The observations were made sequentially, so they cannot be compared directly. The data

shows the typical conditions during the filling. The readings at each degree of filling were

done within approx. 40 sec.

Each minute there was a reading of the flow rate of the CMF. The average flow rate (86

readings) was 251 kg/min, standard deviation was 6.7 kg/min, minimum flow rate 239 kg/min

and maximum flow rate 263 kg/min (one initial reading and three readings at the end are

removed). This indicates a stable flow rate. The observations in the table were not done

simultaneously.

Phase 3: Last weighing

The semi-trailer was again placed on the non-automatic weight and the tractor was removed

from the load plate of the weighbridge.

Again there were made 3 observations, and between each of the three readings the tractor

again drove on and off the load plate to obtain a significant change in the load of the

weighbridge.

12

The following observations were made:

Weighing of just the tank semi-trailer (d=2 kg)

Weighing no. Indication




Average of observations 17 394.0 kg

Std. Dev. 2.0 kg

Std. Dev. Of Mean 1.2 kg

Weighing of complete vehicle included tractor and semi-trailer (without driver)

Total mass of vehicle 27 490 kg

Estimated mass of tractor 10 096.0 kg

Estimated change in mass of tractor 9.3 kg

Some remarks:

During this phase there were no rainfall and the surface of the vehicle was free from

droplets.

The platform was relatively clean and free from significant objects.

Summary of observations

The table below shows all the readings uncorrected from errors:

Start End Difference

uncorrected

CMF 1 490 736 1 513 282 22 546

Mass tanker 39 954,7 17 394,0 22 560,7

Mass total 50 060 27 490

Mass tractor 10 105,3 10 096,0 9,3

The weight readings from the 3 repetitions both in filled and empty condition have relatively

small values for the repeatability.

General comments on different Influences

Stability of flow:

The flow rate was quite stable around 250 kg/min during the transfer. Both readings of the

volume flow rate, the density and temperature indicated stable conditions during the filling.

Weather conditions:

The temperature was approximately 15 °C, moderate wind conditions and showers. During

the weighing, the vehicle was almost dry and free from visible water on the surface.

The time schedule:

Weighing of filled tanker: 11:30 to 12:00

Delivery of LNG at bus terminal: 12:30 to 14:40

Weighing of empty tanker: 15:20 to 15:40

13

A.2. Road debris and road tanker semi-trailer weight (Dummy test)

To investigate the likely but unwanted influence of road debris on road tanker semi-trailer

weight during transport the following additional test was performed:

1. Weighing of road tanker semi-trailer using weighbridge as described earlier in this

report.

2. Road tanker is driven to Haukås LNG terminal but no mass transfer is performed, then

back again to weighbridge location at Stena Recycling.

3. Weighing of road tanker semi-trailer as in 1.

Since there is no transfer of LNG mass from the road tanker during this additional test any

change of mass of the semi-trailer as observed from the weighbridge readings has to be due to

road debris during transport or instability of the weighbridge. As described in appendix A1

were three repeated observations of semi-trailer weight readings were performed the

repeatability of the weighbridge is relatively good with a standard deviation at about 2 kg so

we expect that any significant mass difference is related to road debris during transport.

Results:

Weighbridge reading before transport to LNG terminal at Haukås: 17 466 kg

Weighbridge reading after driving to terminal and back to Stena Recycling: 17 460 kg

There is an observed 6 kg difference in LNG semi-trailer weight from weighbridge readings.

The difference is in the upper range of the weighbridge repeatability so it is expected that part

of the difference is related to road debris. More measurement data on this effect is needed in

order to estimate the size of this contribution and its uncertainty. A 6 kg difference is equal to

0.03% in the relative error of CMF related to weighbridge.

A.3. Weighbridge calibration

The weighbridge used for the testing described in this report was calibrated both before and

after the 5 reproduced tests. The calibration was performed by Justervesenet on the 30th

of

May and the 27th

of November 2012. Result from the calibrations is given in figure 8 and

table 2 below.

During the tests with the road tanker there were a total of 10 readings of the weighbridge, the

full and the empty semi-trailer for the 5 reproduced tests. All of the readings are in the

“unload” category as the tractor is driven of the weighbridge after it is disconnected from the

semi-trailer. Table 2 below shows the 10 readings and the absolute corrections as seen from

the result of calibration for the unloading data from 30/5 and 27/11 in figure 8.

14

Figure 8: The weighbridge error calculated as weighbridge reading minus mass of reference

weights.

Table 2: Weighbridge error for the 10 readings of weighbridge indications. Absolute errors of

single weighing and mass difference error for full minus empty weighing.

Date and type of weighing Weighbridge reading

Absolute

error [kg]

Mass

difference

error [kg]

30/5 27/1

1

30/

5

27/1

1

5/9 full 40088 -4 -14 -2 -2

5/9 empty 20038 -2 -12

10/9 full 39955 -4 -14 -3 -3

10/9 empty 17394 -1 -11

18/9 full 39594 -4 -14 -3 -3

18/9 empty 17392 -1 -11

8/10 full 39868 -4 -14 -3 -3

8/10 empty 17484 -1 -11

15/10 full 39606 -4 -14 -3 -3

15/10 empty 17438 -1 -11

As can be seen from table 2 the error in the semi-trailer mass difference measurement is for

this data independent of the calibration date. This can also be seen from figure 8 as the

calibration curves from 30/5 and 27/11 have the same shape in the 17 000 to 40 000 kg range.

The weighbridge error as the distance between the curves is close to constant at 10 kg in this

range.

-20

-15

-10

-5

0

5

10

0 10000 20000 30000 40000 50000 60000

We

igh

bri

dge

err

or

[kg]

Weighbridge reading [kg]

Weighbridge error (mass)

27/11 - load

27/11 - unload

30/5 - load

30/5 - unload

15

The discontinuity of the calibration curves just below 30 000 kg is due to the use of tare

weight used during the calibration. There is a good overlap in the transition area so this is of

minimal concern for the calibration result.

fltough

Stamp

13st International North Sea Flow Measurement Workshop 22 – 25 October 2013, Tonsberg, Norway

1

Assessment of LNG Sampling Systems and Recommendations

Asaad Kenbar, NEL, Scotland

1 INTRODUCTION

The work described in this paper is part of the European Metrology Research Programme (EMRP) project for Liquefied Natural Gas (LNG) which is jointly funded by the European Commission and participating countries within Euramet and the European Union. The objective of this project is to improve the uncertainty associated with the calculation of LNG energy transfer by developing new techniques and methods. This will be achieved by looking at the complete LNG custody transfer process including volume and composition measurements along with density and gross calorific value calculations. Detailed description of all work packages is found in the project website [1]. The work described in this paper falls under Work Package 3 of the project. Currently, the calculations of the density and the gross calorific value of the LNG transferred are made on the basis of the average composition of the LNG. This composition is obtained from LNG sampling and subsequent chromatographic analysis. The sampling stage is the most important point of the LNG measurement sequence. The sampling procedure must be consistent throughout the whole operation to ensure representative results. Unrepresentative samples are responsible for the majority of errors that occur in the energy transferred calculation. Sampling of LNG must be completed at both the loading and unloading terminals to take account of the ‘ageing’ phenomenon. This ‘ageing’ process occurs over time due to the effects of ‘boil-off’. The boil-off gas is when the lighter components of LNG vaporise and exit the tank. When this occurs, the composition and thus the properties of the LNG will change. This change occurs naturally over time and has a significant consequence on the gross calorific value and density of the transferred LNG and thus its value. The accuracy of LNG composition obtained from sampling will have direct influence on the accuracy of calculated density and gross calorific value and subsequently the accuracy of LNG energy transferred. The LNG shipment value is often in the range of €40 - €50 million. A small error in the determination of the gross calorific value and density of the LNG has a significant financial impact on the exporter/importer. An error of 1% in energy transferred equates to €400,000 - €500,000 in misallocation during custody transfer. Therefore one of the main work packages of the EMRP project focuses on LNG sampling systems and looks at the current sampling technologies used in the LNG custody transfer measurements. Information was gathered on continuous and intermittent sampling systems, LNG vaporisation systems, practices in Middle East (LNG production plants), European countries and North America (LNG receiving terminals). Information also gathered on retention of samples during LNG loading or unloading and on the uncertainty associated with measured composition. This was achieved by visiting two LNG terminals, one in Spain (Cartagena LNG Import Terminal) and the other in the USA (Sabine Pass LNG Terminal). Information from other sites (Isle of Grain LNG import terminal, UK and RasGas LNG export terminal, Qatar) was collected by emailing a detailed survey form. The main objective of this

fltough

Stamp


2

survey is to review sampling and vaporisation practices in different parts of the world. More information on all these sites can be found in the detailed report [2]. The gathered information enabled detailed assessment of current LNG sampling systems, highlighting associated issues and challenges, providing an overview of uncertainty in measured gas composition and finally making recommendations for improvement where applicable.

2 CURRENT STATE OF THE ART

LNG is currently traded in the form of energy transferred based on measurement of the volume in the ship’s tanks, measurement of composition from sampling and subsequent calculations of density and gross calorific value from the measured composition. This also requires measurement of energy of displaced gas and where applicable the energy of boil-off gas consumed by the ship’s engine. Reference [3] describes fully how LNG energy transferred is determined. In order to determine the composition of the LNG it is first necessary to condition the fluid sampled from its initial state, liquid at low temperature, to a final state, gas at ambient temperature, without partial vaporisation or loss of components. The conditioned vapour sample is then analysed by gas chromatography. The LNG industry has gained significant experience with LNG sampling and therefore developed new equipment as well as improved sampling procedures. The spot (discontinuous) sampling system has become almost obsolete for custody transfer measurements and the GIIGNL handbook recommends use of this system as a back-up system in case of failure of the main system or for impurity analyses only. The sampling processes currently used in the LNG industry are mainly of two types; continuous and intermittent as defined in ISO 8943 [4]. The intermittent sampling is also referred to as discontinuous sampling in other publications such as the EN 12838 standard [5] and the current GIIGNL handbook [3]. LNG sampling systems always sample and vaporise LNG on a continuous basis and the terms continuous and intermittent sampling are related to the analysis of gas phase, that is, after vaporisation of the sampled liquid stream. The continuous sampling system sample vaporised LNG at constant flowrate while the intermittent system samples at predetermined intervals. An example of continuous sampling system is shown in Figure 1 [6]. It was observed that some of the continuous sampling systems witnessed on visited sites deviate slightly from those described in ISO 8943 by having a direct feed of conditioned gas sample from the accumulator and mixing vessel to gas chromatograph or in other cases directly from vaporiser to gas chromatograph as shown in Figure 1. The gas containers (or cylinders) are filled either manually or automatically during the whole duration of stable sampling. The purging of the system is performed by running the sample through the system (including gas containers in some cases) for a specified period of time.


3

TT1 PT1 FT1

LNG

PT4 TT6

Boil off

FICIVaporiser

LNG Probe

Automatic filling

cylinders

TT5

Waterless

type gas

sample

holder

Chromatograph

- Composition

- Properties

- Statistical

parameters

WT1

TT3

TT2

PAL2

Instrument DescriptionTT1 LNG temp. at probe inletPT1 LNG pressure at probe inletFT1 LNG flow indicationPAL2 Vacuum pressure (probe system)TT2 Vaporiser inlet temp.TT3 Vaporiser indoor temp.WT1 Vaporiser powerTT5 Vaporiser outlet temp.PT4 Vaporiser outlet pressureTT6 Chromatograph inlet temp.FICI Vaporised sample flow indication

Control &

Treatment

Software

Figure 1 Example of continuous LNG sampling system [6]

2.1 Sampling System Requirements

The current requirements of LNG sampling systems for achieving representative sampling and accurate compositional analysis of vaporised LNG are described in [7] as follows. - The LNG sampling system must meet or exceed the following:

� ISO 8943-2007, Refrigerated light hydrocarbon fluids- Sampling of liquefied natural gas– Continuous and intermittent methods [4].

� ISO 10715-2001- Natural gas sampling guidelines [8]

� BS EN ISO 12838-2001 Installations and equipment for liquefied natural gas- suitability testing of LNG sampling systems (section 8) [5].

� API 14.1 (2006)- Collecting and Handling of Natural Gas Samples for Custody Transfer.

- The system must automatically collect samples over the duration of a batch providing an 'averaged' sample of the pipeline contents and eliminating the risk of biased manual samples.

- The system must be suitable for sampling a complete LNG batch in a loading cycle when LNG is stable in the liquid phase. The sampling system must be unaffected by any changes in line pressure for main line flow rates.

- The system must extract three samples as specified in ISO 8943:2007 to provide redundancy and enable validation of reproducibility and sampler performance.

- The automated system must be designed to minimise the risk of operator errors and ensure a representative sample is provided for analysis.

- The system must provide a method to fully purge all interconnecting process lines to prevent contamination from atmosphere and previous sample.

- When an on-line gas chromatograph is used, the sample conditioning system must have a capability to provide a stable homogenous vaporised gas feed to the chromatograph. It must include a facility to extract manual samples at specific points throughout the batch.

- In the case of intermittent sampling system, the system must be able to take samples at sufficient frequency and configured to re-pressurise the gas samples


4

into constant pressure containers with a sampler discharge pressure up to 150 bar. Samples must be maintained above the dew point to prevent any condensation.

- The system must be configurable for any duration of sample period and sample volume, providing the flexibility to match the sampling to future quality requirements.

- The system must be simple to maintain.

The main elements of LNG sampling system are briefly described below with main focus on achieving representative sampling.

3 MAIN COMPONETS OF LNG SAMPLING SYSTEM

3.1 Sampling Probe and Piping to Vaporiser

The first point of LNG sampling system is the sampling probe (see Figure 1). The sampling probe is installed at a right angle to the axis of the LNG transfer line. In the case of multiple transfer lines, the sample probe is located downstream of the manifold, if one exists, otherwise, each line will be provided with a sampling point [4]. The sampling probe must be installed at a point on the transfer line where the degree of subcooling is high. Subcooling must be maintained when the sample is transferred through the sampling probe in order to prevent formation of bubbles or vaporisation of LNG. This is normally achieved by careful insulation of the probe to minimise ambient heat gain through the probe. The degree of subcooling at sampling point is ascertained by observation of the temperature and pressure of the LNG at that point (Figure 1) and comparing the observed temperature with the boiling point temperature calculated from LNG pressure and composition. Sampling probes are normally constructed from stainless steel due to its robustness and ability to handle extreme temperatures. ISO 10715 [8] specifies that the sampling probe must be equipped with a shut-off valve. Vacuum insulated sampling probe with a mechanism to monitor and maintain the vacuum (e.g. 1.10 to 4 torr) and fitted with a cryogenic valve remotely shut-off by an actuator is an effective way to draw a representative sample. Often the sampling probe is fully assembled with LNG sample pipe to vaporiser and supplied as one unit. An example of this type of probe with a vacuum insulated sampling pipe that connects directly to the vaporiser is shown in Figure 2 [9].

Figure 2 Vacuum insulated LNG sampling probe with connection line to vaporiser [9]


5

Detailed description of all the elements making the sampling probe shown in Figure 2 is found in [9]. Figure 3 shows an example of sampling probe, with vacuum jacket insulation (right), installed on an LNG transfer line in an import terminal compared with another probe with inefficient insulation resulting in ice formation (left).

Figure 3 LNG sampling probes with effective (right) and less effective (left)

insulation [Courtesy of Enagas]

It is important that the liquid sample line, between the sampling probe and the LNG vaporiser, have a short length, small inside diameter (e.g., 2 mm) and provided with efficient insulation so that the LNG is kept in a sub-cooled condition until it reaches the vaporiser. The design of this line must take in consideration all possible unloading conditions. Calculation of the maximum length of the liquid sample line between the probe and the vaporiser is given in ISO 8943 [4].

3.2 Sample Vaporiser and Control Devices

ISO 8943 specifies the following requirements for LNG sample vaporiser: • “The heat exchange capacity of the LNG sample vaporiser shall be sufficient to

gasify the whole volume of LNG which is being withdrawn for sampling”. • “The sample vaporiser shall be so constructed that the heavier components of

the LNG shall not remain in the vaporiser”. • “Where a compressor transferring vaporised LNG is provided, the maximum

gasifying capacity (heat input) of the LNG sample vaporiser shall be greater than the capacity of the compressor”.

From the above it is clear that the vaporiser must be designed carefully in order to avoid fractionation, especially if the gas sample is directly taken for analysis. To that effect heating to a sufficiently high temperature, e.g. 50°C or greater, is required to ensure immediate vaporisation of even the heaviest trace components. Vaporisers are normally electrically based but other types using water heated by steam or by steam directly are available. More details are given in [3]. An example of electrically heated vaporisers (using heated rods) is shown in Figure 4 [10].


6

Figure 4 Electrically heated vaporiser with four heating rods [10] The condition of vaporised LNG must be controlled; firstly in order to monitor the sample vaporisation condition and secondly in order to protect the equipment used. Control devices are required before inlet to vaporiser, within the vaporiser and at vaporiser outlet. These devices are described in detail in [3 and 4]. On vaporiser inlet, a check valve (prevents back flow of vaporised components), restriction orifice (to achieve flash vaporisation), needle valve (to control LNG or gas flow), sample filter, isolation valves and bypass system (for maintenance) are installed. To control conditions within the vaporiser, a temperature regulator, thermometer, thermostats and control of the power supply of the transformer or of the (submerged) resistance heating element are installed.

On vaporiser outlet, a pressure regulator (controls LNG flow to vaporiser independently of LNG transfer pipe pressure), anti-pulsation vessel or mixing accumulator, impingement chamber, Flow meters and / or flow limiters, pressure instruments, temperature detection switches and safety valves are installed. Other safety measures must be taken to meet requirements for operation in hazardous conditions.

3.3 Gas Sample Collection (Sample Holder and Sample Containers)

There are two types of gas sample holders; water-seal type or waterless type. These can hold gas volume between 500 and 1000 litres and designed to store a representative portion of the vaporised LNG during the transfer operation. A sampling holder of constant pressure floating piston (CF/FP) type is also available and typically designed to accumulate 25 litres sample volume. A gas compressor is normally used to deliver small portions of vaporised LNG into the CP/FP container. The characteristics of the gas contained in these holders after completion of sampling operation is aimed to be a well mixed sample that is representative of the LNG loaded or unloaded.


7

ISO 8943 provides technical specifications of gas sample containers used in continuous and intermittent sampling systems and provides a description of the filling and cleaning procedure. Figure 5 shows a picture of typical sample containers used in continuous sampling system.

Figure 5 Example of gas sample containers [Courtesy of Enagas]

3.4 Gas Analysis System

The next step after collection of vaporised LNG sample is to analyse this sample in order to determine its composition. The current practice is to perform the analysis by gas chromatography. Gas analysis by Raman Spectroscopy is also available where the composition of LNG is measured directly in the LNG transfer line but this method still in the development and verification stage. The energy content (gross calorific value) of loaded/unloaded LNG is determined from the gas composition. A direct energy content measurement by e.g. calorimeter is less precise and also will not give the compositional information needed for calculating other properties such as density or Wobbe index. The gross calorific value and density are both required for the LNG energy transfer calculations in the LNG custody transfer trade. Detailed description of gas chromatography can be found in; ISO 6974, Energy Institute IP 377, ASTM D 1945 and GPA 2261.

3.5 Data Acquisition and Processing System

A dedicated data acquisition system is installed in order to monitor and control the sample condition from the point of collection to the point of analysis, process the analysed data, apply data consistency checks and filter the data to provide a final reliable measurement of LNG composition. This system normally consists of the following main hardware and software elements: • Remote data collection unit: collects data from all instruments, control devices

and gas chromatograph and sends it to the computer system for analysis and treatment.


8

• Software application for communication: facilitates communications between the remote control unit and computer to visualise and store the data acquired from the remote unit.

• Alarms software and database: insure all parameters operate within predefined limits.

• Software for data processing and treatment: process the data and eliminate LNG compositions produced during unstable periods (i.e. outside predefined limits).

After performing this data processing step, a subset of acceptable LNG compositions is available for further treatment. The purpose of data treatment is to obtain, from a statistical point of view, a consistent result that best reflects the quality of the discharged LNG. The data treatment is achieved by firstly applying a statistical test called “GRUBBS test” for each LNG component as detailed in ISO 5725-1, secondly calculate the average composition of the LNG and finally normalise the LNG composition. This will results in a final LNG molar composition from which the relevant LNG properties such as density, gross calorific value and Wobbe index can be calculated.

4 SAMPLING PROCEDURE

4.1 LNG Flowrate

Sampling of LNG must take place continuously throughout the sampling period at a constant LNG transfer flow rate regardless of whether the sampling method is continuous or intermittent [4]. For example, in Figure 1 the flow monitoring device (FT1) is used for this purpose. In this process the initial period corresponding to the starting of transfer pumps is excluded until the full flow rate is established. Similarly the final period corresponding to stopping of transfer pumps is also excluded, Figure 6(a).

(a)

Unstable Flow Rate

(b)

Figure 6 LNG sampling period

When significant changes in pressure or flow rate occur in the transfer line, Figure 6(b), the sampling must be suspended temporarily. Sampling can only be conducted during stable unloading/loading flow rate.

4.2 Sample Condition from Probe Inlet to Vaporiser Inlet

The sampling probe must be installed at a point in the LNG transfer line where the LNG is under sub-cooled condition. The degree of subcooling is ascertained by comparing the LNG temperature at the sampling point (TT1) with the LNG boiling point calculated at pressure (PT1), Figure 1. The calculation of boiling point also


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requires knowledge of LNG composition. If the LNG temperature in the transfer line is lower than the boiling point then the sampled LNG is under sub-cooled condition.

Figure 7 (top part) shows natural gas liquid-vapour equilibrium curve for an example of LNG from Qatar. For LNG to be in sub-cooled condition, it has to be on the left hand side of the liquid-vapour equilibrium curve. The LNG condition during the unloading operation is normally within the zoomed area shown by the dotted line.

Figure 7 Natural gas liquid-vapour equilibrium curve [6]

The zoomed area in Figure 7 (bottom part) shows the condition of LNG in the ship’s tanks where the pressure is close to ambient value and LNG is very close to the saturation line (boiling point). When LNG unloading pumps start the pressure increases (in this example) to about 3 bar resulting in increase in LNG boiling point (from -160oC to about -146oC). If for example the unloaded LNG temperature is -160oC then there will be subcooling of about 14oC. In the LNG sample probe and transfer line to vaporiser, any ambient heat gain or pressure variation may result in LNG partial vaporisation. In the case of ambient heat gain, two main factors play major role in this process, the first is the degree of sub-cooling that is available and the second is the quality of insulation used for the probe and sampling line to vaporiser. In order to maintain the LNG sample in sub-cooled


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condition before entering the vaporiser it is essential to use an efficient insulation such as vacuum jacketing to minimise the heat ingress into the system. The degree of subcooling is a parameter that has to be monitored continuously and an alarm must be set when the LNG temperature in the line is closely approaching the boiling point. If boiling takes place in the probe and sampling line then the collected LNG sample entering the vaporiser may not be representative of LNG unloaded due to a preferential vaporisation of components with lowest boiling point such as nitrogen and methane. To illustrate how the condition of LNG is changing from the point of sampling to vaporiser inlet, the following example for LNG unloading case is given as shown in Figure 8. Note that this case is different from the one shown in Figure 7.

KeyP Pressure in kPaH Enthalpy in J/Kg1 Degree of sub-cooling2 Pressure drop3 Heat absorption through

sampling line4 Saturated liquid

• Sampling pointo Inlet of LNG sample to vaporiser

4

a

b

Figure 8 LNG pressure enthalpy diagram

During the unloading period, an LNG sample is taken at point “a” of Figure 8 from the LNG transfer line. The LNG is at sub-cooled condition which means that the whole substance is in liquid state. Through the probe and sample line, a pressure drop together with a warming due to heat absorption will occur resulting in change of sample state from point “a” to point “b”. In this example it can be seen that the enthalpy change is less than sub-cooling degree and therefore the LNG will remain in sub-cooled liquid state before entering the vaporiser. As indicated above this condition has to be monitored continuously. Representative sampling with minimum subcooling of 0.5o K is achievable according to [11].

4.3 Sample Vaporisation Process

The LNG sample entering the vaporiser must be completely vaporised before being analysed in a gas chromatograph device. In particular heavy components of the LNG must not remain in the vaporiser. Figure 9 shows the pressure / temperature diagram of natural gas. It is important to note that this figure is for illustration of the vaporisation process in particular point 1. This point in fact represents point b in Figure 8.


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1

2

TT3

Figure 9 LNG vaporisation path [6] The objective is to transform LNG from liquid to gas state in supercritical conditions at a pressure higher than the Cricondenbar point (i.e. higher than 75 bar) so that the LNG sample goes directly (flash) into the desired gas state without fractionation. Such transformation is represented in the Figure 9 (left) by the dotted line. Within the vaporiser the following processes are taking place, Figure 9: • When LNG sample enters the vaporiser at the condition described in Figure 9

(point 1), it first passes through a restriction device such as a small volume pressure relief valve to allow LNG to flash into natural gas in supercritical state (illustrated in green colour in Figure 9 (right)).

• The process of LNG flashing will result in higher pressure and temperature of around 80 bar and -100oC (point 2 in Fig. 9).

• In the vaporiser, the flashed LNG will enter heated environment (e.g. heating rod or coil) and the gas sample temperature at the vaporiser outlet (point 3) will be controlled to obtain gas sample temperature > 20oC.

For the example given in Figure 1, the measured vaporiser parameters (TT2, TT3, WT1, TT5 and PT4, FIC1) will be monitored in order to control this process of vaporisation. The LNG inlet temperature (TT2) ensures that the liquid is sub-cooled and can be flashed. The outlet temperature and pressure (TT5 and PT4) ensures that gas leaving the vaporiser is at the correct condition for analysis by chromatography. The electrical power (WT1) ensures that the vaporiser is at the right power. Finally the gas flow from vaporiser (FIC1) indicates that the LNG flow to be vaporised is at the required rate. These parameters are monitored by the alarm software.

4.4 Purging and Filling Process

As indicated in section 2.1, the LNG sampling system must provide a method to fully purge all interconnecting process lines to prevent contamination from atmosphere and previous sample. An example showing complete purging process can be found in [9].


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The number of required purging cycles for sample containers is defined in annex D (sampling by fill and empty method) of ISO 10715 [8] according to the final pressure in the gas cylinder, see Table 1.

Table 1 Number of purge cycles for sampling by fill and empty method [8]

Final Pressure in cylinder, MPa Number of purge cycles 0.1 to 0.2 13 0.2 to 0.4 8 0.4 to 0.6 6 0.6 to 1 5 1 to 3.5 4 ≥ 34 3

The "LNG Measurement Study" of N.B.S. [11] has shown that the sample containers initially containing air should be purged and filled at least 14 times to remove all the air from these containers. If continuous purging is adopted, the sample container must be purged the equivalent of 14 or more purge and fill cycles. The purge gas throughput is estimated by monitoring the pressure, flow rate and time. For intermittent sampling the purging and filling of sample containers (CP/FP) apply different procedures. According to [4], these containers need to be cleaned and purged by helium gas, take a sample of helium present in the container and run lab analysis for trace amounts of hydrocarbon or impurities. If hydrocarbons remain present after extensive purging, a solvent cleaning may be required.

4.5 Data Processing and Treatment

At the end of LNG unloading process, analysis report is normally completed as described in [3]. The main output of this report is LNG composition. As indicated above, the data provided by gas chromatography must be processed and treated in order to eliminate LNG compositions produced by analyses in a period of time during which some operating parameters were outside their preset limits. The valid data will be then treated by applying the “GRUBBS statistical test”, calculate the average composition of the LNG and normalise the final LNG composition. An example of how the data is processed and treated is fully described in [6 and 2]. 5 UNCERTAINTY IN SAMPLING AND GAS ANALYSIS

Detailed information on the estimate of uncertainty of measured composition of LNG from sampling appears to be limited to the NBS study [11] and ISO 6974-2 [12]. The latter focuses only on the uncertainty of composition measured by gas chromatography. The GIIGNL manual [3] also refers to the NBS study for estimation of uncertainty in measured composition and uncertainty in calculated LNG density and calorific value. In this work the focus is placed on the total uncertainty in measured gas composition. The uncertainty in the gross calorific value and density are covered in separate work within the EMRP project as part of estimate of overall uncertainty in measured LNG energy transferred [13]. The uncertainty in the measured LNG composition can be considered to be composed of two main elements. The first is from the sampling and vaporisation


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system and its operating conditions and the second is from gas analysis by chromatography. The first element (sampling system and its operating conditions) has been investigated in detail by the NBS study [11]. In this study, the lack of field performance information and data meant that the approach taken for estimating the uncertainties was based on assumed conditions at a typical LNG import terminal. In this case the estimate of total uncertainty is composed of an allocation for known sources of systematic error plus random error. The study considered many factors which could affect the precision and accuracy of the composition of samples withdrawn from a flowing LNG stream. Precision is defined as the closeness with which the results of independent replicate measurements agree and usually quantified by the estimated standard deviation. This will be referred to as “repeatability” hereafter. Accuracy denotes the closeness of computations or estimates to the exact or true value. The factors considered in the NBS study covered:- • Three probe designs • Two vaporiser designs • The following operating variables:

- Flow rate of the liquid stream in the transfer line, - Flow rate through the sampling system, - Amount of subcooling, - Temperature of LNG, - Pressure drop (between sampling probe inlet and vaporiser inlet) - Time-averaging the sample, - Vaporiser outlet temperature, and - Composition, particularly the effect of pentane and higher hydrocarbons

The effect of above listed factors on sampling repeatability and accuracy is grouped below:- • Variables affecting both sampling repeatability and accuracy:

- Heat leak to liquid sample line. • Variables affecting sampling repeatability but not accuracy:

- Sampling flowrate, - Vaporiser design, - Time-averaging the vaporised sample, - Sample rate transients, - Sampling probe design.

• Variables not affecting sampling repeatability and accuracy:

- Temperature and pressure at the sampling point, - Degree of subcooling - Flow rate past the sampling point, - Pressure drop (between sampling probe inlet and vaporiser inlet) - Composition of the liquid being sampled.

There is no mention in the NBS study what would be the total uncertainty associated with the factors related to the sampling system only (i.e. the first element of uncertainty indicated above). The uncertainty associated with the second element (i.e. gas analysis) is reported by the NBS study. The estimate of total uncertainty given below for this element is


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composed of an allocation for known sources of systematic error plus random error [11]. The random error for the gas analysis process is estimated to be ±0.02% and expanded to ±0.06% using a coverage factor of 3 to achieve confidence level of 99.7%. This value represents the maximum value obtained from over 100 measurements of three or more repetitive analyses employing a properly operating gas chromatograph with programmable integrator system. The known sources of systematic error are the uncertainty in the composition of calibration gas which is estimated as ±0.03%. This represents the uncertainty in the weighing process used to prepare the calibration gas mixtures. Great care must be taken to assure the purity of the components of the calibration gas in order to maintain this uncertainty value. From above, the total uncertainty in gas analysis is worked out from the sum of random error and known sources of systematic error (i.e. 0.09%) rather than from the root mean square (RMS) of these values. This means that the estimate of this uncertainty is conservative. The value worked out from the RMS is 0.067%. It can be concluded based on the above study that the uncertainty in gas analysis should be within ±0.09%. The GIIGNL handbook [3] indicates by reference to the NBS study [11] that the uncertainty in measured LNG composition due sampling and analysis is ±0.3% (this includes the ±0.09% indicated above). However, close examination of reference [11] revealed no mention of this but in several sections, it was stated that “the total uncertainty of a single measurement in sampling and analysing LNG mixtures can be less than ±0.3 percent in the computed calorific value”. Therefore the ±0.3 refers to the uncertainty in calculated calorific value and not in the gas sampling and analysis only. However, the only additional parameter that contributes to the uncertainty of calculated LNG gross calorific value is the gross calorific value of the components used in the mixture which is given by [11] as ±0.04%. Since this uncertainty is very small, it can be concluded that the uncertainty associated with gas sampling and analysis is ±0.30% (using a coverage factor of 3 to achieve confidence level of 99.7%). This uncertainty should be lower when a coverage factor of 2 is used to achieve confidence level of 95%.

6 SUMMARY AND RECOMMENDATIONS

6.1 Selection of Sampling System

When selecting LNG sampling system, it is extremely important to adhere to the requirements listed in section 2.1. It appears that the continuous LNG sampling system uses less equipment with moving parts than the intermittent sampling system and the purging process of sample containers may be less involved, however the use of CP/FP containers (fixed pressure containers) minimises atmospheric or cross batch contamination and ensure that the sample is stored at the correct process conditions. Some of the continuous sampling systems witnessed on visited sites deviate slightly from those given in ISO 8943 by having a direct feed of gas sample from the accumulator and mixing vessel to gas chromatograph or if an accumulator is not used then the gas sample leaving vaporiser is fed directly to the gas chromatograph and to gas sample holder (e.g. Figure 1). The gas containers are filled either manually or automatically from the accumulator or sample holder during the whole duration of stable sampling. For example the first cylinder is filled one hour after start


15

of stable sampling conditions, the second half way through sampling duration and the third before stopping the LNG pumps. In conclusion, provided that the LNG sampling requirements indicated above are fulfilled, the choice of sampling system type (continuous or intermittent) is left to user preference.

6.2 Sampling Probe and Sample Line to Vaporiser

To achieve representative sampling, the LNG flow through the sampling probe and sampling line to vaporiser must be maintained in liquid phase. This can be achieved by:- • Installing the probe at a point on the transfer line where the degree of subcooling

is high. The probe access point is normally located on the top of horizontal pipe and this appears to be the current practice [3]. The probe tip should be at least 0.3D away from the tube wall (where D is the tube diameter) and preferably at the centre of the pipe. However, the NBS study [11] indicates that use of side tap probe mounted horizontally with the probe flush with the wall of the LNG pipe produced best results in that study. It is recommended to avoid this arrangement due to possible effect of a boundary layer at the surface of the main pipe, which can lead to samples of the LNG that are not representative.

• The probe inner diameter should be as small as feasible and reference [11] recommends the diameter to be less than 0.25 in (6.4 mm).

• The LNG subcooling must be maintained through the probe and sample line to vaporiser to prevent LNG boiling. This can be achieved by using efficient insulator that minimises ambient heat gain and withstands atmospheric conditions. Experience has shown that use of vacuum jacketed insulation is ideal and therefore recommended for use. However this option will be more expensive than other types of insulation and the vacuum level has to be monitored.

• The length and diameter of the sample line to vaporiser should be designed according to ISO 8943. A tube diameter of 2 or 3 mm is commonly used.

• If length of sample line to vaporiser is greater than the design length, it is recommended to install two temperature instruments and one pressure instrument. The first temperature instrument is at probe exit and the second at vaporiser inlet to monitor LNG temperature. The use of pressure instrument and second temperature instrument are required to evaluate the degree of subcooling which is dependent on LNG composition. The second temperature instrument will also provide indication on the gradual loss of vacuum when vacuum jacketing is used for insulation. This temperature in addition to formation of ice around piping indicate significant loss of vacuum.

• Other considerations: The probe must be constructed from stainless steel material designed to

withstand the temperature and pressure and equipped with automatic shut-off valve. The piping to vaporiser must be equipped with pressure relief devices set at the correct relief pressure in case of LNG boiling.

6.3 Vaporiser and Control Devices

The vaporiser must be designed to meet the requirements of ISO 8943. The main requirement is to vaporise the LNG from liquid to gas phase directly without going through the 2-phase region. Therefore LNG flashing and subsequently heating to a sufficiently high temperature, i.e. 50°C or greater , is required to ensure immediate vaporisation of even the heaviest trace components.


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It is not recommended to use open tubular atmospheric vaporisers in which the heat comes from the ambient air as these devices will not guarantee LNG vaporisation with no fractionation. Laboratory results [11] indicated that a small diameter tube vaporiser was better than a vaporiser with a large cross sectional area. Both steam and electrically heated vaporisers produced comparable results. Experience has shown that some types of electrical vaporisers fed from the bottom of vaporiser may not achieve complete vaporisation, but when LNG is fed from the top and flow to vaporiser is carefully restricted, flash vaporisation with no fractionation can be achieved and gas temperature leaving the vaporiser can be controlled at the desired temperature. In order to achieve the controlled vaporisation process described in section 4.3, control devices must installed at vaporiser inlet, within vaporiser and at vaporiser outlet as described in section 3.2.

6.4 Sample Condition

The main parameter that must be controlled when the liquid sample flows through the probe and sample line to vaporiser is degree of subcooling. The NBS study [11] indicates that representative sampling can be achieved with subcooling as low as 0.5o K. However this should be considered as the minimum value. The main parameters that must be controlled when the gas sample leaves the vaporiser are; temperature, pressure, sample homogeneity and flowrate. The gas sample temperature is controlled by combination of heat regulation device and temperature instruments. The vaporiser electrical power is also monitored in order to achieve this. Reference [11] recommends gas sample temperature leaving the vaporiser in the range of (27oC to 38oC) and this must be maintained (e.g. by use of heated sample line) to prevent condensates in particular water vapour condensation. There is no advantage in having the vaporiser outlet temperature above 38oC. Both pressure and flowrate regulation are used in order to control the LNG flow to be vaporised independently of pressure or flow rate in the main LNG transfer pipe. In general, reference [11] recommends to maintain the sample flowrate above 1200 l(n)/h and the sample pressure greater than 1.4 bar to provide sufficient gas sample pressure for purging and filling of sampling containers. An anti-pulsation vessel (or a mixing accumulator) is also used to absorb the pressure pulses and to maintain gas homogeneity. The minimum residence time necessary to obtain representative results depends on the sampling system. Residence times of 20 to 120 seconds gave good results in the NBS study [11]. Flow meters and/or flow limiters are also required to control the maximum sample flowrate. In addition to pressure and temperature instrumentation pressure and temperature interlock systems are required for auto-shut-off the main LNG isolation valve if the gas sample temperature at vaporiser outlet drops to the application set point or if pressure at vaporiser outlet exceeds set pressure.


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6.5 Purging Process

In order to prevent contamination of newly sampled LNG by the previous sample or by presence of air in the system, it is extremely important to perform through purging of the sampling system. The purging of sampling system is performed according to ISO 8943 [4] and ISO 10715 [8]. The information given in ISO 10715 is relevant to natural gas sampling rather that LNG sampling and therefore care must be taken when following this standard for purging process. The information given in ISO 8943 lack details on purging process and gives only very brief guidance. For purging the sample containers, the NBS study [11] have shown that the sample containers initially containing air should be purged and filled at least 14 times to remove all the air from these containers. If continuous purging is adopted, the sample container must be purged the equivalent of 14 or more purge and fill cycles. The purge gas throughput is estimated by monitoring the pressure, flow rate and time to achieve this requirement. An automated process for both continuous and intermittent sampling systems is recommended except when CP/FP sample containers are used, these must be manually cleaned as detailed in section B4 of ISO 8943.

6.6 Data Processing and Treatment

After achieving stable sampling conditions, the composition of the conditioned sample is measured by a gas chromatograph. Before producing the analysis report which is required after completion of LNG unloading process, the data provided by gas chromatography must be processed and treated in order to eliminate LNG compositions produced by analyses in a period of time during which some operating parameters were outside their preset limits. The valid data will be then treated by applying the “GRUBBS statistical test”, calculate the average composition of the LNG and then normalise the final LNG composition.

During this process the data acquisition system must incorporates software for monitoring the sampling conditions (and when necessary raise an alarm) and software for data processing and treatment. The monitoring software checks that each measured parameter is operating within its limits, otherwise an alarm will be triggered and this means that the LNG composition data must not be taken into account for processing and treatment. The data processing and treatment software eliminates LNG compositions produced by analyses in a period of time during which some operating parameters were outside their preset limits.

6.7 Uncertainty in Sampling and Gas Analysis

During this study, it was found that the information available on the uncertainty associated with the LNG sampling and vaporisation is limited to the NBS study [11] which has been reported in 1985. Although all the parameters contributing the uncertainty associated with sampling and vaporisations were explored and their effects were summarised, a figure on expected overall uncertainty in sampling and vaporisation is not given directly. However the uncertainty in gas composition associated with gas analysis by chromatography has been detailed and given as ±0.09% (using a coverage factor of 3 to achieve confidence level of 99.7%). In the NBS study it is stated that “the total uncertainty of a single measurement in sampling and analysing LNG mixtures can be less than ±0.3 percent in the


18

computed calorific value”. This covers the uncertainties in sampling and vaporisation, gas analysis as well as the uncertainty in gross calorific value of the components used in the calibration gas mixture which is given by [11] as ±0.04%. Since the latter uncertainty is very small (i.e. only ±0.04%), it can be concluded that the overall uncertainty in sampling and analysis can be achieved within ±0.30% (using a coverage factor of 3 to achieve confidence level of 99.7%). It appears that this value is currently accepted by the LNG industry as it is indicated in the GIIGNL hand book [3] and uses a high value for confidence level. A breakdown of all uncertainty elements associated with sampling and analysis as well as the uncertainty in the density and gross calorific value is covered in separate work [13] as part of estimate of overall uncertainty in measured LNG energy transferred.

6.8 ISO 8943:2007

From the above review and assessment of LNG sampling systems, it appears that a practical guidance document on sampling system design and operation is required. The ISO 8943 provides general guidelines and requirements that must be met to achieve representative sampling of LNG but lacks detailed information and examples on operating conditions and limits of the main parameters. It is understood that some parameters will depend on the design of the system and its associated elements but providing practical examples of typical LNG sampling systems recently installed at an import and export terminals and range of operating parameters within which representative sampling can be achieved would be extremely helpful. Some of these shortfalls are addressed by the GIIGNL handbook [3] but this document is neither a standard nor a specification and therefore an enhancement to ISO 8943 is recommended. The following are proposed to enhance ISO8943: • More examples of continuous and intermittent sampling systems are required.

Current LNG sampling systems in operation deviates from those in the standard and cause confusion whether the system can be described as continuous or intermittent. The standard defines both continuous and intermittent systems but during the survey conducted in this work it was observed that a system which has direct feed to a gas chromatograph is being regarded as intermittent by one visited site although the sampling, vaporisation and analysis are continuous. This is simply caused by showing direct feed of sample from vaporiser to analyser in the ISO 8943 example of the intermittent sampling system only (Figure 3 of ISO8943).

• Recommendations on inner diameter of probe and sampling line to vaporiser.

• The selection of insulation type for probe and sampling line to vaporiser is left to the user. Despite cost implications, it is highly recommended to indicate that vacuum insulation should be considered first, in particular when subcooling degree is low, as it provides best insulation efficiency and withstands weather conditions when compared to traditional insulation materials. However the vacuum level must be monitored and maintained.

• More information on type of vaporisers used and advantages and disadvantages of each type. Typical recent examples would be helpful.


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• More guidance on sampling procedure is required, the following questions are raised:

- What is the minimum degree of subcooling that should be maintained before sample enters vaporiser?

- What is the minimum/maximum sample pressure? Typical examples

- What is the minimum/maximum sample flowrate? Typical examples

- What is the criterion for achieving complete purging process? More details required on how purging is conducted, the number of purge cycles required for each element of the system (in particular sample containers). If continuous purging is adopted, for how long the purging should be carried out? Typical examples for purging would be helpful.

- For how long the water-seal-type gas sample holder should be subjected to bubbling and what is the criterion for achieving complete purging.

It would be beneficial to take the above suggestions in consideration in the next update of ISO8943:2007.

6.9 Development of Reference Standard for LNG Composition Measurement

A traceable reference standard for measurement of LNG composition with defined low uncertainty does not exist yet. Such a standard is required for two reasons:

1- To benchmark current and newly developed LNG sampling systems, 2- Conversion of primary mass flow measurement to volumetric flow

measurement with sufficiently low uncertainty through traceable calculation of LNG density from composition.

A draft proposal has been put forward by VSL supported by industry and national authorities to develop such a reference system within a newly developed LNG mid-scale (200 m3/hr) flow meter calibration facility. The work involves detail design of the mid-scale system and LNG composition measurement standard (currently put as an option for approval) based on experience from industry using the most up to date techniques for sampling and composition analysis.

ACKNOWLEDGMENT

• The work described in this paper was carried out by NEL Ltd under contract to the Department for Business Innovation & Skills as part of the National Measurement System’s Engineering & Flow Programme.

• The research leading to these results has received funding from the European Union on the basis of Decision No 912/2009/EC. The EMRP is jointly funded by the EMRP participating countries within Euramet and the European Union.

NEL wish to thank all members of the advisory group of this EMRP-LNG project for their support and the information and assistance received from:-

� Angel Benito, Enagas, (Spain) � Dominique Ingrain, elengy, (France) � Ken Thompson, Mustang Sampling/Valtronics Inc, (USA) � Michael Scott, RasGas Company Ltd, (Qatar) � Stuart Hill, Grain LNG, (UK)


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REFERENCES

[1] ENG03 LNG, Metrology for Liquefied Natural Gas (LNG), EMRP Joint Research Project, 2009, (www.lngmetrology.info).

[2] Kenbar, A. (2012), Assessment of LNG sampling systems, EMRP ENG-03 LNG Project, NEL report number 2012/349, downloadable from (www.lngmetrology.info).

[3] G.I.I.G.N.L., (Groupe International des Importateurs de Gaz Naturel Liquéfié), LNG Custody Transfer Handbook, third edition, version 3.01 (2011).

[4] ISO 8943: 2007, Refrigerated light hydrocarbons fluids –Sampling of liquefied natural gas –Continuous and intermittent methods.

[5] EN 12838:2000, Installations and equipment for liquefied natural gas – Suitability testing of LNG sampling systems.

[6] Benito A., (2009), Accurate determination of LNG quality unloaded in Receiving Terminals: An Innovative Approach, IGU, Buenos Aires, Argentina, P1 - P23.

[7] Jiskoot- IsoFraction automatic LNG sampling and GC sample conditioning system, System specification, (www.jiskoot.com).

[8] ISO 10715-2001- Natural gas sampling guidelines.

[9] Opta-Periph (http://www.opta-periph.com).

[10] Thompson K. (2011), LNG sampling- pipeline to analyser, Power Point presentation, 2nd International Workshop-Metrology for LNG, NEL, East Kilbride, UK.

[11] LNG measurement, A User's Manual for Custody Transfer, NBSIR 85-3028 - First edition 1985.

[12] ISO 6974-2, Natural gas – Determination of composition with defined uncertainty by gas chromatography – Part 2: Measuring system characteristics and statistics for processing of data.

[13] Graham E. M. and Kenbar A. (2013), LNG energy transfer uncertainty- sensitivity to composition and temperature changes, FLOMEKO 2013, 24-26 Sept., Paris, France.

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EXPERIENCES WITH SAMPLERS ON COLD LIQUIDS

Author: Ole-John Melkevik, Statoil ASA

ABSTRACT

This abstract will tell Kårstø Gas Processing Plant experiences with samplers on cold products starting from 1984 up to 2013. It will start with the first grab samplers where one did not have any back pressured cylinder. The sample of Propane did vaporize as soon as it entered the sample line. The result was no level in the sample can.

The next generation came with Argon back pressured cylinder. The start problem was that the grab was too small. The sample did vaporize with the original grab of 1ml. The grab was replaced with a 4ml cup. Now the sampler started to function. But still it needs a lot of maintenance to keep the cylinder functioning. Due to low temperatures one sees that the O-rings sealing rolls of the piston and start leaking.

The third generation was based on fast loop and a pump located in a cabinet. It was now easy to do maintenance and replace parts since everything was located in a cabinet. A drawback was that there was a need for a recirculation pump. Many of the components were of the same construction as in the previous versions. This again are leakages in the components due to dry product i.e. the can and the pump.

The fourth generation of sampler for cold products are based on standard parts and put together in a cabinet. The pump is based on a Haskell pump which fills up the pump chamber by means of the process pressure and empties with air pressure. This means that we fill up the cup for every sample grab. And all the parts are robust and easy to get when they fail. These samplers operate as expected on cold products from -40ºC to +2ºC. It operates on pressures from 5 to 50barg.

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THE HISTORY OF SAMPLERS FOR COLD PRODUCTS AT KÅRSTØ.

1. GENERATION

When the plant at Kårstø was started up in 1985 there were installed liquid samplers on all liquids metering stations. They were all located at the jetty. The samplers were set up for LPG and Naphtha. The LPG included Propane and Butanes which had a temperature band from -40 to 0°C. During the commissioning and startup we struggled to get liquids into the sampler cans. This was managed when we vented the tubing and the sampler was immediately started. If it was a large batch it could take some time between the samples. If it was a bit too long waiting time the liquid vaporized and the sampler stopped working. Then it was to struggle with draining and venting to get it running again. Since this was cold, -40, the valves froze and the valves were not able to close again. So the personnel were hidden in a gas cloud. Gas alarms and blue light was the result. During these hazardous operations we also managed to fill our boots with liquids. This then ended up in a Propane dance on the Jetty. The words spoken will not be quoted here.

The result of this sampler installation ended up with demolition.

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Figure1. An overview of 1st generation sampler at Kårstø

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2. GENERATION

In 1998 there were a new project going on at Kårstø. There should be installed new metering stations with samplers. This time it came with backpressure on the can and also a pressurized system on the sample side. This was set up with pressure relief valve with a set point around 26barg between the sample pump and the can. The cup was 1ml and we struggled to get any sample into the can.

Maybe we should increase the size of the cup. It was possible to increase the size to 4ml without changing any other part. New cups were purchased and installed. Now we managed to get enough sample into the can. We reduced the number of samples to 2500. We saw that there is a bit different in operating on oil and operating on LPG/NGL. Crude oil is lubricating O-rings etc. LPG/NGL doesn’t do any lubricating. This caused in twisted O-rings and leakages etc. Also the sample pump was covered in a Glacier, since the pipeline is always cold due to loading or recirculation. Had also some problems with the material in the cups and O-rings since they could not withstand fully chilled Propane they swelled up.

Figure2. An overview of 2nd generation sampler at Kårstø.

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3. GENERATION

In 2004 the people at the lab also wanted to have samplers on the old metering stations same type as the solution in 1998. But instead of inline sample pumps there were installed small sample pumps in a bypass loop. The loops should be operated by means of pitot probes with circulation without any circulations pumps. The size of the cups was as previous 4ml. It was also highlighted that the material in the cup must withstand both temperature and LPG.

We struggled with operation of the sample pumps. It was not able to get any flow in the loop although it was calculated that it should be able. Therefore there was a need for a pump circulation pump in the loop. The startup was then delayed with 6 months. In the meantime air operated circulation pumps were installed and tested out. Still we didn’t manage to get flow in the loop. The sampler pumps were opened and then we saw that the material had swelled out. The reason for that was that the material was not according to the spec. The O-rings and cups were replaced and then everything functioned very well. But also here we’re struggling with twisted O-rings due to lack of lubrication. A reduced diameter of the can would have helped a lot.

Figure 3 An overview of 3rd generation sampler at Kårstø.

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Figure4. shows the dimensions of the sampler pump. As one can see it is easy to maintain this pump compared to the inlinesampler pump.

Figure5. Definition of items on the pump on figure 4.

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Figure6. Shows how the different parts of the sampler are located on one back plate.

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4. GENERATION

The 4th generation came when it was time to replace the old metering stations for LPG. With references to the ASTM Designation D 1265-97: Standard practice for sampling liquefied petroleum gases there is accept for manual samples of cold products. This will be very complicated when there is a possibility to have 5 batches simultaneously. Therefore it was essential for us to make it a bit simpler. Together with our contractor we agreed upon an automatic manual sampler. It is possible to choose between 2 to 20 samples for a batch. The default setting is 10 samples. These are evenly spread over the batch. This will also give an indication to the lab people that the system is functioning.

The system is set up with fast loop over flow control valves downstream the station. The sample is taken out with a pitot probe. Since there can be up to 10 bar differential pressure we need a flow control in the fast loop. This is done with a coriolis meter and a control valve. The speed is set to approx. 1 m/s.

The standard pump then fills itself up with the process pressure which triggers the air to reset the pump. The pump continues to run as long as there is air on. And every sample is the same volume (if we could have controlled the number of strokes it would have been perfect)

The sample receiver also contains a level transmitter and a local level indicator. At the HMI we can monitor the level, flow, density, number of samples to be collected, number of samples collected. All this information is also available on the process explorer so that it can be monitored from the office or at the lab.

The sampler can is also a standard receiver containing 4 liter. Normally the can will be filled with 3 liter.

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Figure7. Illustration shows the principle of the sampler. With fast loop to the cabinet for the pump and back-pressured sample can.

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Figure8. General arrangement of the sampler

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Figure9. Illustration of the pitot probe for inlet of the fast loop.

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Figure10. How the sampler cylinder is build up.

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Figure11. A picture of the sampler pump. The process pressure fills the chamber of the pump and

then the air pressure transfer the trapped volume to the cylinder. In this way the chamber will always be filled. Because the piston has to go all the way to the end before it is pushed back by the air

pressure.

Figure12. An indication of the sampler behaviour during a batch. The stairs show the filling every 10%. The upper curve indicates the velocity of the liquid. The lower curve indicates the density. If water should occur the curve will bounce upward.

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Figure14. Indication on the process explorer with figures in addition to curves.

Conclusion: For cold products free of water this sampler really does the job every time. All the components are standard as delivered to all customers no special versions. Easy to read variation of the product to the sampler since we have the density available in the flow meter. And by using pressure drop over the control valve we avoid circulation pump. The maintenance will be to overhaul the sample pump. But the cylinders will be replaced with spare from the warehouse. The replaced cylinder will be shipped back to retailer for overhaul. A roughly estimate shows that this can save up to 3 man-year compared to manual sampling. The history also shows that this system will be more available than a system with inline sampler. The most obvious reason for this is the lack of lubrication when sampling LPG. References [1] Daniel Industries drawing 1984 [2] FMC Drawings 1998 [3] FMC Drawings 2004 [4] Proserv drawings 2010 [5]ASTM Designation D 1265-97: Standard practice for sampling liquefied petroleum gasses

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31st North Sea Flow Measurement Workshop Norway, October 2013

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Multiphase meter capable of detecting scale on the pipe wall

and correcting flow rate measurements

Arnstein Wee, MPM Øystein Fosså, ConocoPhillips

Vidar Rune Midttveit, Statoil 1 INTRODUCTION Scaling and wax deposits of oilfield equipment represent a major issue in oil and gas production as the decreased cross-sectional area leads to reduced fluid flow and hence reduced productivity. The deposits may further damage instruments or in best case lead to reduced functionality and increased measurent uncertainty. MPM has developed and patented a method for both scale detection inside the multiphase meter and measurement of the scale thickness. The method further encompasses corrections to be performed, in order for the MPM meter to provide reliable flow rate measurements even with significant amounts of scale on the pipe wall. Once scale has been detected by the meter, the operator is left with several choices. The meter can be used to monitor the effect of corrective actions initiated by the operator to clean or to prevent further build-up of scale in the piping. The MPM meter sensor calibration parameter can also easily be corrected for scale build up such that the measurement integrity of the meter is maintained during these operations. Alternatively, the meter can be removed from service and cleaned once scale is detected. This paper present the work performed over several years in JIP projects with leading oil companies, including Statoil and ConocoPhillips. A first study, sponsored by ConocoPhillips, was performed in 2009 where scale was artificially created at laboratory conditions. Based on the results from this study, some preliminary conclusions were made and lessons learned. One conclusion was that studies from real scale should be sought investigated. In 2012, an MPM Meter, which had been in operation for several years at a Statoil operated field, was returned for service. It was found that the meter had a scale layer of approximately 2 mm. Statoil agreed the meter could be used for extensive studies as a part of the ongoing JIP with participation by 9 oil companies. The meter was installed in the MPM flow loop and flow tested in order to study the measurement effect with scale on the pipe walls. The paper describes the test results and the experience with artificial generated scale versus scale from a real field application. Pictures and measurement of the scale on the meter internals is presented together with the method for detecting and measuring scale on the pipe wall. The scaled-up meter was flowtested before and after cleaning and the method for correcting the calibration parameters of a multiphase meter to account for the presence of scale on the pipe walls has been tested and verified. 2 SCALE IN OIL PRODUCTION FACILITIES Oilfield scale is generally found as inorganic salts of the alkaline metals calcium (Ca), strontium (Sr) and barium (Ba), examples of which are carbonates and sulfates. Scale may also be complex iron salts such as sulfides, hydrous oxides and carbonates.

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Carbonate scales usually form through precipitation due to the change in condition. Sulfate scales on the other hand, arise when two incompatible waters mix together. Lastly, iron scales derive from iron-rich sources such as pipe work and vessels and hence reflect the corrosion in these. Mineral scales deposition depends on several variables, among which are: - Degree of water saturation - Rate of change in temperature - Impurities - Change in pH of the solution - Change in pressure Scale problems can arise under the following circumstances [18]:

During drilling and well completion, if the drilling mud or completion fluid is incompatible with the formation water

At the commissioning stage of new injectors, if the injection water is incompatible with the formation water

During production, when a well starts to produce formation water with the hydrocarbons During wellstream processing, when significant quantities of produced water put process

equipment at risk Commingled production, where wellstreams from various formations, reservoirs or

individual wells are mixed together, can make matters worse. Scaling can thus occur in both production and injection wells and scale removal is therefore a common well-intervention operation. Figure 1 below shows a picture of extreme buildup of scale in pipe lines [18].

Figure 1: Example of significant scale buildup A multiphase meter, which is going to be used for flow for conditions where there is risk for scale build-up, needs to be able to provide reliable measurements of the oil, water and gas flow rate in the presence of some scale on the pipe walls. This is particularly important when multiphase flow


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meters are used to measure the production of comingled wells or fields since the risk of scale build-up then can be quite large. 3 MPM TECHNOLOGY 3.1 3D Broadband™ The MPM meter measures multiphase flowrates with no separation or mixing device. A combination of a Venturi flow meter, a gamma-ray densitometer, a multi-dimensional, multi-frequency dielectric measurement system and advanced flow models are used. These are combined to form a multi-modal parametric measurement system [10], [11], [12], [13], [14]. The 3D Broadband system is a high-speed electro-magnetic (EM) wave based technique for measuring the water/liquid ratio, the water salinity and the liquid/gas distribution within the pipe cross section, as illustrated in Figure 2. By combining this information with the measurements from the Venturi, the flowrates of oil, water and gas are determined. The measurement is based on permittivity measurements performed at many frequencies in many planes within the sensor simultaneously. The measurement frequencies cover a range of 20 to 3700 MHz. The MPM meter has a dual mode functionality, which means that the meter is a combined multiphase and a wet gas meter [16]. At ultra-high GVFs (typically > 99% GVF), the liquid volume is extremely small compared to the gas volume. Under these conditions, the Droplet Count® functionality improves the measurement resolution of the liquid fraction. This function is also highly tolerant towards changes in fluid PVT properties, such as the oil and gas densities and water properties. Liquid droplets flowing with the gas stream in a pipe causes statistical variations in electromagnetic measurement signals. The statistical variation is primarily a function of the liquid droplet size, the number of droplets, and the permittivity of the droplets.The DropletCount® is a patented method whereby statistical electromagnetic measurements are combined with measured water fractions from the 3-Phase wetgas mode, venturi models, and empirically derived correlations to calculate the liquid fraction related to the liquid droplets. MPM uses electromagnetic measurements with ultra fast and sensitive responses, which are scaled to the pipe diameter and the permittivity of the material within the pipe. The measurement field is uniformly distributed within the cross section of the pipe with low sensitivity to the liquid film along the pipe wall and more towards where the droplets flow. The measurements are extremely sensitive to small variations in permittivity caused by droplets flowing in a pipe. [17]. 3.2 In situ measurement of fluid properties The MPM meter has three methods for in situ measurement of fluid properties that represent further increased robustness against uncertainties of the PVT properties for the MPM technology [4], [7], [15].

Figure 2: 3D Broadband multi-planar measurement


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Measurement of water salinity of the water phase. This is an in-line continuous

measurement which is performed while the well is flowing. Separate methods are used for multiphase and wet gas flow conditions. The method used in multiphase flow conditions covers water-continuous flow conditions only (typically WLR > 50%) and the method used in wet gas flow conditions covers both oil- and water-continuous liquid emulsions.

Measurement of gas density and permittivity by utilising the DropletCount method [17] to

detect periods with pure gas within the pipe. During these periods, the permittivity and density measurement is used to measure, verify and correct the PVT calculated values for permittivity and density. The method can also be used to measure the permittivity and density of oil.

Multi Mode Analysis. In wet gas, the MPM meter incorporates three different methods for

measurement of the fractions and flowrates of the wet gas which can be used to determine PVT properties. This is an in-line continuous measurement which is performed while the well is flowing based on recalculation of the following measurement modes:

a. three-phase mode with Droplet Count b. three-phase mode without Droplet Count c. two-phase mode with GOR Input

These three methods behave differently when errors are introduced in the PVT configuration data which can be used to derive an estimate of the PVT configuration data. The methods used for in situ measurements of fluid properties are further described in [3], [4], [6], [7], [15]. 3.3 In situ measurement of scale build-up Funcionality for measuring scale has been available in the MPM meter since 2007. A parameter named “Scale Index” was developed prior to supplying a early version of the MPM meter to ConocoPhillips in 2007 at the Ekofisk platform. At this location, scale build up was assumed to be a potential problem, and MPM therefore developed a first version of scale detection method in order to monitor potential scale build-up. In 2007, there was no experimental data available to test the scale detection function. During the first two years of operation no scale build up was seen on the meter and therefore ConocoPhillips initiated a separate development project in 2009 to investigate the scale detection function further. The MPM meter at Ekofisk has now been in continuous operation since October 2007 and still no scale build up of any significance has been seen in the meter. Test results from this meter has been presented at the North Sea Flow Measurmeent Workshop in 2009 [2]. Based on the function which was initially developed in 2007 and the experience from laboratory tests and tests of a meter which has been scaled in a field location (Gullfaks A), MPM has developed, tested and patented a new function for detection and measurement of scale build up. But even more importantly, the tests have demonstrated that the MPM meter is able to provide reliable flow rate measurements with significant amount of scale build-up inside the meter. The scale detection function first detects a known state within the pipe, such as water or gas filled sensor. Based on the measured frequency response of the 3D BroadBand antennas, and comparing these frequency sweeps with factory values, scale build up can be detected. For some conditions, such as gas filled pipe, the scale thicknes can also be determined. Based on the determined thickness of scale, the empty pipe calibration parameters, pipe diameter and venturi discharge coefficient for the sensor can easily be corrected to account for scale build-up. This method has been verified by performing flow loop testing of a MPM meter which was returned


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from the Gullfaks A for service. Hence the meter used during the test contained significant amount of scale from a real oil-field installation. After testing and cleaning the meter, the meter was returned to Gullfaks A and put in operation. 4 TEST SETUP 4.1 Test equipement Early prototype versions of the MPM meter has been used for all the testing described in this document. The 3D BroadBand sensor contains 9 single pin antennas where 3 antennas acts as transmitters and 6 antennas are used as receivers and one 3-pin antenna where one antenna act as a transmitter and two as a receiver (salinity antenna). All the antennas are coaxial conductors which penetrates slightly into the pipe, which is a well known antenna for transmitting and receiving electromagnetic signals (pipes/waveguides, mobile phones, radios etc). The insulating material may either be PEEK or glass. PEEK is used for low pressure applications and glass is used for high pressure and high temperature. A picture of the internal antennas are shown in the figure below.

Figure 3: Picture of the 3D BroadBand antennas 4.2 Laboratory experiements with artificial generated scale A first study was performed in 2009, sponsored by ConocoPhillips, where the main purpose was to find out if the 3-pin antenna could be used to detect presence of scale in the sensor. Barite scale was artificially created under laboratory conditions and was conducted by Intertek WestLab in Stavanger. Initially the scale was created by placing the antennas in a bath container and adding BaCl2 and Na2SO4 to form BaSO4. Throughout the execution of the experiments it proved to be difficult to build up sufficient scale with this procedure. It was therefore decided to carry out a series of experiments where thick layers of barite deposits were made on the probes manually. The latter method resulted in a porous crystalline layer rather than the hard deposits one would expect in a real field installation. The layer of scale on the antennas was gradually increased with 6 different layers, where the tickest scale layer was approximately 5 mm. For each layer of scale, the antennas was installed in a MPM meter and the frequency response was then measured and the scale layer thickness was documented by measurements and pictures.


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4.3 Field experiemts with real scale A 3” MPM meter has been installed in series with the test separator at Gullfaks A and has been in continuous operation since December 2006 for testing of the Gullfaks A platform wells. The initial meter was a prototype of the current MPM meter which was used to test and qualify the MPM meter. The test results and experience with this meter has been published by Statoil [9]. The meter has also been upgraded with many of the improvements and changes of the present version of the MPM meter. Figure 4 below shows a picture of the installation at Gullfaks A from December 2006.

Figure 4: Picture of installation at Gullfaks A During the spring of 2012 it became evident that there was an offset in the flow rate measurements of the Gullfaks A MPM meter. Since the meter at that time was a first prototype of the MPM meter which has been in continuous operation since December 2006, it was decided to send it back to MPM for inspection. Also the meter has been exposed to the pretty rough flow conditions that will occur on the inlet of a test separator which also is used during clean up and start up of wells new wells. Inspection of the meter revealed that there was significant amount of scale inside the meter, and figure 5 below shows a picture inside the meter which was taken when the meter was recived at MPM.


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Figure 5: Picture of scale in meter at Gullfaks A

Since MPM now had obtained a meter which contained real scale, a test program, as a part of the on going In-Situ Verification II project, was initiated to test the scale detection functionality developed during the project. The purpose was also to investigate the effect of scale on the flow measurement and determine if it would be possible to correct the calibration of the meter for presence of scale. The meter was tested with scale in air, salt water (15 different salinities) in adition to 44 test points in the MPM flow rig which included single phase of oil, water and gas in adition to multiphase test points over a wide GVF and WLR range covering the expected flow range for the Gullfaks A field. 5 TEST RESULTS WITH ARTIFICIAL GENERATED SCALE 5.1 Scale levels Figure 6 below shows pictures of scale layer 1-6 for the 3 pin antenna with the artificial generated scale by Intertek Westlab. The thickness for layer 6 is approximately 5 mm and scale layer 5 is approximately 3-4 mm. The porosity of the artificial generated scale is higher compared to scale in a real field applications.

Figure 6: Picture of 3-pin antenna with artificial generated scale

Figure 7 below shows corresponding pictures of one of the glass version of the single pin antennas for scale level 4-6. Similar scale levels was introduced on the PEEK version of the


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antenna used for low pressure applications. Scale level 1 was omitted on the single pin antenna since the layer of scale was “microscopic”, as seen on figure 6.

Figure 7: Picture of 1-pin antenna with artificial generated scale 5.2 Frequency response of sensor with scaled single-pin antennas The scaled antennas was installed in a MPM meter sensor and the frequency response was measured from 0 to 2.500 Mhz

0 500 1000 1500 2000 2500 3000 3500-140

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Scale 2Scale 4Scale 5Scale 6

Figure 8: Antenna (Glass) frequency in a MPM sensor with scale level 2-6

The signal frequency (MHz) is plotted shown on the x-axis of figure 8 above and the y-axis is the measured power attenuation (dBm) on the longest signal path (the receiver which has the longest distance from the transmitter). As expected, there is a small left-shift of the response as the scale layer thickens due to an increase in permittivity within the pipe as a result of the scale present. However, the shape of the frequency response and the coupling efficiency of the antennas are completely unaffected by all scale layers and hence scale have no impact on the measurement integrity or functionality of the glass antennas.


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0 500 1000 1500 2000 2500 3000 3500-140

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Scale 2Scale 4Scale 5Scale 6

Figure 9: Antenna (PEEK) frequency in a MPM sensor with scale level 2-6

Figure 9 above shows the same type of response as figure 8 with PEEK antennas and another MPM sensor. The PEEK antennas were tested in a 3” sensor and the glass antennas were tested in a 5” sensor, hence there will be some difference in the frequency spectrum in the two cases related to the difference in pipe diameter. However, the PEEK antennas show exactly the same behaviour as the Glass antennas, i.e. the shape of the frequency response and the coupling efficiency of the antennas are completely unaffected by all scale layers and hence scale has no impact on the measurement integrity or functionality of the PEEK antennas. The laboratory experiments demonstrates that the integrity of the single pin antennas are completely unaffected by scale build up in the experiments which means that the MPM meter will maintain its full functionality even with significant amounts of scale on the pipe wall. The scale will influence the calibration of the sensor since the pipe diameter of the sensor then is reduced. However, this can easily be accounted for when the thickness of the scale layer is known. 5.3 Frequency response of salinity antenna with scale Figure 10 below shows the measured loss in scale layer 1-6 for the longest signal path on the salinity probe.

Figure 10: Frequency response of 3-pin antenna with scale level 1-6


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Scale level 5 and 6 are covering the entire antennas and area between the antennas. Scale level 6 has in addition a thick level of scale above the antennas such that the total thickness is approximately 5 mm. The x-axis is the signal frequency (MHz) and along the y-axis is the measured power attenuation (dBm). As seen from the figure above, the salinity probe behaves slightly different compared to the single-pin antenna. For the thin layers, the impact is marginal. For the thickest scale layer (layer 6), there is a distinct reduction in the measured loss. The single-pin antennas transmit energy into the entire pipe’s cross- and longitudinal section, hence the permittivity impact of scale is proportional to the total volume fraction of scale in the pipe cross-section. This causes a small left shift in the responses with scale for the single pin antennas when scale is added. For the 3-pin probe, there is a local effect of scale since scale is covering both the antennas and the material in-between the antennas. The combination of scale on antennas and scale in the medium in-between the antennas cause a reduction in the measured loss (i.e. more efficient signal transfer between the antennas). The reason for the loss reduction is caused by an increased coupling efficiency of the antennas into the scale medium compared to when the sensor is filled with air/gas. Based on the test performed in the laboratory, it appears that a scale layer of 5 mm is required in order to be reliably detected by the 3-pin antenna. Reducing the length of the antennas will most likely enable the meter to detect thinner layers of scale. Scale levels less than 5 mm seems to have virtually no effect on the measured response with the 3-pin antenna. However, reducing the length of the antennas, or even making the antennas flush with the pipe wall, will most likely enable the meter to detect thinner layers of scale if this would be required by a field installation. 6 TEST RESULTS WITH SCALE FROM A FIELD INSTALLATION 6.1 Investigation of scale layer in the meter Figure 10 below shows a picture of the layer on the meter returned from Gullfaks A vs. a clean meter.

Figure 11: Picture of Gullfaks A meter with scale vs a clean meter

From figure 11 above it is seen that the scale is evenly distributed across the entire section of the meter, and it looks like the meter has been “spray-coated” with a scale layer inside.


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Figure 12 below shows two pictures of the 3-pin antenna. All three antennas and the area between the antennas are covered with scale in a similar way as the scale which was created artificially in the laboratory. However, the scale layer in the Gullfaks A meter was much more evenly distributed and significantly harder (less porous) compared to the scale created artificially in the lab.

Figure 12: Picture of 3-pin antenna in Gullfaks A meter with scale The thickness of the scale layer was measured at 11 locations along the meter as shown in figure 13 below.

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1 00 2 00 3 00 4 00 5 00 6 00 7 00 8 00 9 00 10 00 11 00 12 00 13 00 14 00 15 00

Scale thickness [m

m]

Figure 13: Measurement of the scale thickness at 11 different locations

From figure 13 above it is seen that the scale layer was slightly thicker in the throat of the venturi (2.4 mm) compared to the scale layer upstream and downstream the throat (2.0 - 2.2 mm). Table 1 below shows the average thickness in the upstream section, throat and downstream section of the venturi.


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Location Average scale thickness [mm]

Upstream venturi throat 2.2 mm Venturi throat 2.4 mm

Downstream venturi throat 2.0 mm Average 2.2 mm

Table 1: Measurement of the scale thickness

The measurements shows that for any practical purposes one can assume that the thickness of the scale level is evenly distributed in the entire sensor with an average thickness of 2.2 mm (within ±0.2 mm of the average value). Figure 14 below shows a picture of the dP tapping for the venturi. From the picture it is seen that the hole has been rounded off by the scale and that the scale layer also extend into the channel which leads to the differential pressure transmitter of the venturi.

Figure 14: Pictures of the antennas inside the sensor

Figure 15 below shows detailed pictures of the nine 3D BroadBand single pin antennas. From figure 15 below it is seen that there are virtually no scale build-up on the antennas and the PEEK insulator for the antenna.


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Figure 15: Pictures of the antennas inside the sensor

A closer look at the picture of the single pin antennas reveals that for three of them there is some scale on the antenna tip whereas the remaining 6 antennas are clean. It also appears that the scale is building up from the metal of the sensor and then “creeps” towards the antenna. It is quite likely that all the single pin antennas would have been completely covered with scale if the scale layer had grown further to a thickness of 4-5 mm. It is also quite interesting to note that whereas the antennas were scaled down in the lab experiment, real-life experiments from the field revealed that the antennas are the very last location to experience scale build-up. However, both the field and laboratory experiments showed that scale on the antennas had no effect on signal level of the single pin antennas. 6.2 Test of methods for scale detection Figure 15 below shows the measured loss for one of the paths for the 3-pin antenna. Clearly, the frequency response of the Gullfaks A meter with 2.2 mm of scale build-up behaves in the same way as the 3-pin antenna with 5 mm layer of artificial scale. From figure 15 below it is seen that a 2.2 mm layer of scale reduces the loss (increased signal level) of approximately 10 dB. This is in the same order of magnitude as the 5 mm layer of artificial generated scale as seen in figure 10. Since the artificial scale has a much higher porosity, it would be expected that more artificial scale build-up is required in order to get the same amount of signal increase as for real scale. The experience from real-life scale with the 3-pin antenna confirms that the method developed for scale detection based on measurement of the broadband loss between the antennas of the 3-pin antenna can be extended to real field installations.


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Figure 16: Measured loss, 3-pin antenna of the Gullfaks A meter

Based on experiments with the sensor containing real scale from a field installation, the following methods for detecting scale have been identified. Use of 3-pin antenna during in-situ gas periods

The MPM meter automatically detects periods with pure gas in the meter based on the patented DropletCount® function [17]. When pure gas is detected, a frequency response of the 3-pin antenna is stored and compared towards factory value. Scale on the pipe wall will reduce the loss between the antennas (e.g. increase the signal coupling between the antennas) compared to the factory value. Field tests has shown that this method is able to detect a scale layer of 2 mm. Smaller scale layer may also be detectable, however further tests is required to confirm this. The method can be extended to detect smaller levels than 2 mm by shortening the antennas of the 3-pin probe.

Comparison of measured gas density from gamma meter vs. PVT calculated value

This method is also based on the function for automatic detection of pure gas. During these periods, the gamma densitometer is used to measure the density of the gas which then is compared to the PVT calculated value. If the gamma densitometer measures a significantly higher gas density compared to the PVT calculated value, this could be caused by scale formation on the pipe wall. This method can also be used to confirm the scale measurement obtained by the 3-pin antenna.

Check of Bradband WLR measurement for water filled sensor

Gas dominant wells (e.g. wetgas wells and wells with high GVF) frequently have periods where there is pure gas in the multiphase meter. This may happen during normal operation in connection with slug flow or in connection with shut downs and start ups of the wells. For wells with high WLR (e.g. water flooded wells), the multiphase flow is dominated by water and for these wells is quite common to experience longer periods where the


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multiphase meter is filled with no gas and even pure water for shorter periods. This typically occurs during shut down or starts up of the wells. The 3D Broadband system of the MPM meter is a high-speed electro-magnetic multi-frequency technique for measuring the water/liquid ratio, the water salinity and the liquid/gas distribution within the pipe cross section, as illustrated in Figure 2. The meter has capability for performing measurements in up to 28 different measurement directions (paths) in the meter at many simultaneous measurement frequencies. For water continuous flow conditions, typically 10-15 of these measurement directions are used. When the sensor is filled with pure liquid (e.g. water), there is no annular concentration of gas in the meter and hence all the measurement directions should measure the same WLR with a gas fraction of 0%. Test with the scaled meter from Gullfaks A, revealed that all the measurement directions, apart from the directions where all the signal paths are in the cross section of the pipe, is very tolerant to scale build up. The three measurement directions where the all signal path is in the cross section were measuring a significantly lower WLR for a water filled sensor compared to all the other measurement directions. Hence, it is possible to detect scale build-up on the pipe wall by comparing the three cross sectional measurement directions with all the other 7-12 measurement directions for water continuous conditions with no gas in the pipe. Scale build up will cause the three cross sectional measurement directions to calculate a significantly lower WLR compared to all the 7-13 other measurement directions. If scale is detected, the cross sectional measurement directions can easily be disabled in the software of the meter. This configuration change would slightly increase the measurement uncertainty of the meter for some conditions, but on the other hand, make the measurements from meter less influenced by scale build up.

Three independent methods for scale detection has been identified and tested, which means that the operator can be reasonable well assured that scale build up in the meter is present before any expensive operational procedures are executed in order to rectify the situation. If there is scale build up in the multiphase meter, it is also quite likely that there is scale in other parts of the well and production system which may lead to expensive clean-up operations. It is therefore important that any scale alarm issued by the meter can be verified with several independent methods. 7 FLOW TEST OF THE METER WITH SCALE The meter which was returned from Gullfaks A with 2.2 mm of scale, was flow tested with the same test matrix as was used during the FAT of the meter in 2006. This test matrix is representative for the flow rates of the Gullfaks A platform wells. The flow test was performed in the MPM flow loop at an average operating pressure of 8 barg. Since this is a low pressure test, larger variation on the measurements is expected compared to high pressure conditions, particularly for the gas measurement. This is also reflected in the uncertainty specification for the MPM meter which is valid for pressures above 20 barg. For lower pressures, the uncertainty is quoted on a case by case basis. A total of 44 test points was performed, covering both oil and water continuous flow conditions with a GVF and WLR range for 0 – 100%. Exxsol D140 was used as oil and salt water (NaCl) with a salinity of 3.5 % (typical sea water conditions). Figure 16 below shows the composition map for the flow test with scale. All the test points with WLR above 40% are most of the time water continuous whereas all the test points below 40 % WLR are most of the time oil continuous. Some of the test points in the 30-50% WLR range


16

switches (varies) between oil and water continuous flow during the test since the switching point between oil and water continuous with Exxsol D140 lies in this WLR range.

Figure 17: Composition plot (WLR vs GVF) for flow test with scale

Table 2 below shows the change in pipe diameter and venturi beta due to scale build up. Figure 17-19 below shows the measured liquid, gas and WLR with the meter without correcting for any scale build up on the walls of the meter. Figure 20-22 shows the measured liquid flow rate, gas flow rate and WLR when the calibration parameters of the meter have been corrected for presence of 2.2 mm of scale. When scale is detected, the following corrections are performed:

1) The sensor pipe diameter and venturi beta ratio is corrected for the presence of scale (ref table 2 below).

2) The three measurement directions where all signal paths lies in the cross section of the pipe are disabled since these directions are most influenced by scale.

3) The venturi discharge coefficient is reduced by 5% due to increased wall roughness. ISO 5167 [19] section 5.5.3 and 5.5.4 implies that a 1% reduction in venture discharge coefficient is expected due to increased pipe roughness of a casted venturi vs. a machined venturi. A venturi which is covered by scale, will have an even rougher surface compared to a casted venturi, and a 5% reduction of the discharge coefficient has been found to be reasonable.

Item Without scale Corrected with scalePipe diameter 73.7 mm 69.3 mmThroat diameter 52.5 mm 48.1 mmVenturi beta 0.71 0.69

Table 2: Sensor data with and without scale


17

Figure 17 below shows the uncorrected measured liquid flow rate with a scale layer of 2.2 mm. There is a consistent positive difference between the liquid flow rate of the multiphase meter and flow loop reference of approximately 30% over the entire GVF range.

Figure 18: Measured liquid flow rate with 2.2 mm scale (uncorrected) Figure 19 below shows the uncorrected measured gas flow rate with a scale layer of 2.2 mm. At low GVF there is a positive difference of approximately 30% between the measured gas flow rate of the multiphase meter and at high GVF (above 80%), there is a negative difference of approximately 30%. For pure gas, there is a positive difference of approximately 20%. Some of the difference is also caused by the low operating pressure in combination with low venturi dP. At higher operating pressures, the scatter in the gas measurement would have been far less since the flow then has a stronger kinetic energy such that the impact of the friction loss due to the rough scale surface is mitigated.


18

Figure 19: Measured gas flow rate with 2.2 mm scale (uncorrected)

Figure 20 shows the measured WLR for the flow loop and the uncorrected measurements from the multiphase meter for a scale layer of 2.2 mm, plotted on the composition map (WLR vs GVF plot). The switching point between oil and water continuous is in the WLR range 30-50%. For the oil continuous test points, 2.2 mm of scale has a marginal influence on the measured WLR. For the water continuous points, there is some negative bias at high GVF which further increases when the GVF is reduced.

Figure 20: Measured WLR with scale (uncorrected)

Figure 21 below shows the measured liquid flow rate when the pipe diameter and venturi beta has been corrected for 2.2. mm of scale and the cross sectional measurement directions has been disabled. The liquid flow rate measurement of the multiphase meter is now generally well within ±5% of the flow loop reference value.

Figure 21: Measured liquid flow rate with scale correction


19

Figure 22 below shows the measured gas flow rate when the pipe diameter and venturi beta has been corrected for 2.2. mm of scale and the cross sectional measurement directions has been disabled. At medium GVFs, the gas flow rate of the multiphase meter is within 10% of the reference. For pure gas, the gas flow rate of the multiphase meter is within ± 5% of the reference value. For low and high GVFs, the gas flow rate of the multiphase meter is within ± 20% of the reference value.

Figure 22: Measured gas flow rate with scale correction Figure 23 below shows the measured WLR when the pipe diameter, venturi beta has been corrected for 2.2. mm of scale and the cross sectional measurement directions has been disabled. Apart from a few test points in the switching area between oil and water continuous flow, the difference between the WLR of the multiphase meter and flow loop reference value is within a few percent [abs]. For wetgas conditions, the impact on the measured WLR is slightly larger, which is also due to the low operating pressure of the flow loop. At higher operating pressures, the difference would have been smaller.

Figure 23: Measured WLR with scale correction


20

11 SUMMARY AND CONCLUSIONS Based on the work conducted in close co-operation with leading oil companies, a method for detection of scale build up in a MPM meter has been developed, verified and patented. Testing has demonstrated that the MPM meter is very tolerant towards scale build up and the new functionality enables the meter to easily correct the flow rate measurement for presence of scale on the pipe wall. The MPM meter, with scale inside, can then be used on a continuous basis while the operator initiates actions to clean the meter or to prevent further build-up of scale in the meter. On a more detail level, the conclusions of the work performed are as follows:

The MPM meter works well with scale inside the meter when the meter calibration is corrected for scale build up.

The 3D Broadband single pin antennas are very tolerant to scale build up. No signal degradation was observed neither in the laboratory scale experiments nor in the field scale data.

3D Broadband measurement performed by the salinity probe can be used to detect scale build up, and determine the thickness of the scale layer.

Results from laboratory experiments with artificial scale agree reasonable well with the test experiments performed with real scale from a field installation. Scale from the field is easier to detect compared to artificial scale generated in the lab.

Several methods for detecting scale have been tested and verified. A combination of methods for scale detection provides a high degree of confidence in the scale detection functionality.

12 ACKNOWLEDGEMENTS The authors want to thank the participants of the In-Situ verification JIP projects (ConocoPhillips, Dong, ENI, GdF, Petronas, PDO, Shell, Statoil, Total and Woodside) for their technical and financial support, the Gullfaks A operation for allowing us to use the meter for scale experiments and finally the Norwegian Research Council / Demo 2000 for sponsoring the projects.


21

REFERENCES [1] B. Hasebe, A. Hall, B. Smith, J. Brady and P. Mehdizadeh, “Field Qualification of four

Multiphase Flowmeters on North Slope, Alaska,” SPE Paper 90037, 2004 [2] Ø. Fosså, G. Stobie, A. Wee, “Successful Use and Implementation of MultiPhase

Meters,” NSFMW, 2009 [3] A. Wee, L. Scheers, “Measurement of Water in a Wet Gas,” NSFMW, 2009 [4] A. Wee, Ø.L. Bø, “In-situ measurement of fluid properties and integrity verification for

Multiphase and Wetgas metering applications,” NSFMW, 2010 [5] L. Scheers, A. Wee, “Challenges at High Accuracy Multiphase and Wetgas

Measurements”, Multiphase Metering Roundtable, Galveston, 2008 [6] A. Wee, L. Farestvedt, “A combined multiphase and wet gas meter with in situ

measurement of fluid properties,” Americas Workshop, 2010

[7] MPM White Paper #. 8, “in situ Verification” [8] G. Stobie, B. Sættenes, “Closing the gaps in subsea multiphase and wet gas metering,

Multiphase Metering Roundtable,” Galveston, 2007

[9] E. Aabro, H. Berentsen, V.R. Midttveit, “Field Test Results of the Topside MPM MultiPhase Meter,” StatoilHydro Well Informed Newsletter, December 2007

[10] M. van Werven and H.R.E. van Maanen, “Modelling Wet-Gas Annular/Dispersed Flow through a Venturi,” AIChE Journal, Vol. 49, No. 6, June 2003

[11] H.R.E. van Maanen, “Measurement of the Liquid Water Flow Rate Using Microwave

Sensors in Wet-Gas Meters: Not As Simple As You Might Think,” NSFMW, 2008 [12] J.P. Couput, G. Salque, P. Gajan, A. Strzelecki, J.L. Fabre, “New Correction Method For

Wet Gas Flow Metering Based on Two Phase Flow Modelling: Validation on Industrial Air/Oil/Water Tests at Low And High Pressure,” NSFMW, 2007

[13] R. de Leeuw, “Liquid Correction of Venturi meter Reading in Wet Gas Flow,” NSFMW,

1997 [14] A. Wee, H. Berentsen, V.R. Midttveit, H. Moestue, H.O. Hide, “Tomography powered

multiphase - wet gas meter providing measurements for fiscal metering,” NSFMW, 2007 [15] MPM White Paper #. 2, “Water Salinity Measurement” [16] MPM White Paper #. 3, “Dual Mode Functionality” [17] MPM White Paper #. 7, “Droplet Count” [18] Statoil web page, “Handling scale in oil production facikities” [19] ISO 5167-4 Venturi tubes

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Magnetic Resonance Technology

A New Concept for Multiphase Flow Measurement

Jankees Hogendoorn (Krohne), André Boer (Krohne),

Matthias Appel (Shell), Hilko de Jong (Shell) and Rick de Leeuw (Shell).

1. Introduction

This paper describes a magnetic resonance based multiphase flow meter. During the last

decades, magnetic resonance technology has enabled many industrial applications. One of the

most well-known developments can be found in medical imaging. High resolution, three

dimensional body scans can provide crucial information for medical diagnosis with many

innovations being published every year. Another important field of application is the oil and

gas industry. For many years, magnetic resonance based logging tools have been successfully

used for detailed formation evaluation[1]

. A scan of the vicinity of the bore hole provides

important information about the reservoir rock structure and saturation condition with pore

fluids, which is very important for oil production optimization.

Magnetic Resonance (MR) has specific advantages that can be used for multiphase flow

measurement as well. A motivation of these advantages is presented in this paper. After a

brief explanation of the principle of MR, a description is given how this principle has been

applied in a multiphase flow meter. The conceptual design of the MR-based flow meter has

been published previously[2] [3]

. Significant efforts have been made to translate the conceptual

prototype to an industrial design that meets oil-field requirements and specifications. A

detailed description of this design is given in this paper too.

Finally, this paper summarizes the experimental and calibration results obtained during three-

phase testing in an industrial flow loop. Magnetic resonance has been proven to have a lot of

diagnostic capabilities in addition to flow quantification. Some of these capabilities are

addressed at the end of this paper.

The paper finishes with a summary and conclusions.

2. The Challenge of Multiphase Flow Metering

Multiphase and wet gas flow meters provide essential online and continuous flow rate

information in upstream oil and gas production systems[4]

. During the last years, the

associated technology has been matured such that acceptable performance can be achieved

over a range of flow conditions. Nevertheless, a number of limitations persist and several

specific requirements have not been adequately addressed by existing technologies. A couple

of these limitations are listed below:

Meter hardware

- Most existing meters make use of gamma ray absorption technology for fluid

identification. There is a strong industry desire to have a multiphase meter without

radioactive sources.

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Stamp

- Many existing meters utilize differential pressure measurement, which, due to the

quadratic dependence to flow rate, typically limits the dynamic range of the flow

meter. This limitation often reduces the long-term applicability of a particular flow

meter since well production can vary considerably. Consequently, a flow meter that

does not depend on the measurement of differential pressure would have advantages.

- Related to the previous item, a full-bore design is preferred in order to minimize the

pressure loss created by the flow meter.

Technology

- Most multiphase flow meters require a priori knowledge of fluid properties at line

conditions. Typical examples are individual fluid phase densities, gamma ray

absorption coefficients and permittivity. These properties are difficult to predict

without fluid samples. In addition, it is often impractical or even impossible to fill a

flow meter with 100% of each fluid phase after field installation. MR technology

offers the possibility to determine the required magnetic fluid properties with an un-

separated fluid mix present in the meter.

Application needs

- At high Water Liquid Ratio (WLR), the uncertainty in the net oil rate measurement

increases exponentially. For many oil field metering applications, there is a

requirement for reducing the uncertainty in the high water liquid ratio region.

- High viscosity multiphase measurement is an additional challenge for existing

technologies as it typically requires a number of additional assumptions and

correlations, for example such as the Venturi meter discharge coefficient (Cd) as

function of multiphase Reynolds number, bulk viscosity of oil/water emulsions, bulk

liquid permittivity models, and updated slip models to mention a few.

- Adequate interpretation of flow meter data typically requires knowledge of the flow

models and closure correlations (i.e. slip models, permittivity mixture rules, etc.)

applied. However, the models and assumptions are typically proprietary for a

particular flow meter manufacturer. Therefore, a more direct, less model-dependent

measurement of individual phase velocities would be desirable.

3. Principles of Magnetic Resonance

By using the principle of Magnetic Resonance (MR), experiments are performed on the nuclei

of atoms, by exposing them to a strong magnetic field. The strength of the magnetic field used

in this technology depends on the application involved. Chemical applications utilize

magnetic field strengths that are up to one million times stronger than the Earth’s magnetic

field measured at the equator. The magnetic field applied in the flow meter prototype

described in this paper is approximately a thousand times stronger than the Earth’s magnetic

field.

MR is a consequence of the intrinsic magnetic moment of protons and neutrons. For most

atoms, such as 12

C and 16

O, the individual magnetic moments of protons and neutrons offset

each other, and the effective magnetic moment of such nuclei vanishes. Those atoms are

invisible to MR. Other nuclei possess either an odd number of protons (1H), or neutrons (

13C),

or both protons and neutrons (2H). If those atoms or isotopes have a sufficiently high natural

abundance, they can be utilized in MR experiments. Hydrogen protons (1H) (figure 1) provide

the strongest MR response and are targeted in most oil-field applications of magnetic

resonance[5] [6]

.

Figure 1

1H atom. The atom consists of a proton and an electron.

The spinning proton has a magnetic moment, .

Precession / Resonance frequency

Similar to bar magnets, when a magnetic nucleus is placed between the poles of an external

magnet, it too will try to align itself with respect to the externally applied magnetic field. The

static magnetic field exerts a torque on the axis of the spinning magnetic moments, which, in

turn, causes the axis to move perpendicular to the direction of the applied torque, and, hence,

around the direction of the background magnetic field (figure 2). This motion is referred to as

Larmor precession and is analogous to the motion of a spinning top in the Earth’s

gravitational field.

Figure 2

1H proton. Left-hand figure: no external static magnetic field. Right-hand figure:

with external static magnetic field; the 1H proton starts to precess along the magnetic field

direction with the resonance (Larmor) frequency f0 .

The Larmor frequency, 0f , of this precession around the direction of the background

magnetic field, 0B

, is determined by the gyromagnetic ratio, :

002

Bf

. (1)

The gyromagnetic ratio is a material constant. Commercial logging tools, as well as the

magnetic resonance flow meter prototype, utilize permanent magnets of such strength that

aligned protons precess with frequencies between several hundreds of kHz to a few MHz.

Magnetization

In the macroscopic world, magnets can be aligned in an infinite number of orientations (figure

3, left-hand figure). At the atomic level, however, only distinct alignments of the nuclei

relative to the direction of an external magnetic field (also called spin states) are possible

(figure 3, right-hand side). That is because direction and magnitude of the angular momentum

of a nucleus, I

, are quantized, i.e., they can only vary by integer multiples of (Planck’s

constant, h , divided by 2 ).

Figure 3 Left-hand figure: no external magnetic field; random orientation of protons, no net

magnetization. Right-hand figure: external magnetic field; protons start to precess with the

Larmor frequency. Net magnetization build up. The growth of the net magnetization (M) in

time is referred to as the longitudinal relaxation (T1).

For 1H, two distinct alignments are possible. The nuclei can be aligned at a certain angle

either with or against the external magnetic field. The population ratio of these two spin states

is given approximately by the ratio of magnetic to thermal energy: at room temperature, there

is approximately the same number of proton nuclei aligned with the main magnetic field as

counter aligned. The aligned position is slightly favored, as the nucleus is at a lower energy

state in this position. For every one million nuclei, there is about one extra nucleus aligned

with the 0B

field as opposed to the field. This phenomenon creates a net or macroscopic

magnetization, in this paper subsequently referred to as M

, pointing in the direction of the

main magnetic field (z-direction). This is illustrated in the right-hand picture of figure 3.

Figure 4 Time-dependent magnetization build-up. For a single-phase fluid, the magnetization

builds-up exponentially and can be characterized by the so-called longitudinal relaxation

time, T1.

The magnetization build-up is time-dependent. For a single fluid, this build-up of

magnetization can be characterized by a time constant called longitudinal relaxation time, T1,

as shown in figure 4. For mixtures of fluids, or more complex fluids, the magnetization build-

up is typically characterized by a spectrum of longitudinal relaxation times. Short T1-times

result in fast magnetization build up.

Radio-Frequency pulses

The alignment of magnetic moments can be disturbed by radio-frequency (RF) pulses

transmitted from an antenna to the fluids measured with the device (figure 5, left-hand figure).

As a consequence, the orientation of the macroscopic magnetization, M

, can be modified by

applying radio-frequency pulses with the appropriate intensity, duration, and frequency.

Changes in the orientation or intensity of the macroscopic magnetization can be detected as a

small voltage in an appropriate RF coil. It is this voltage which is measured as the MR signal

in magnetic resonance experiments (figure 5).

Figure 5 Hahn echo experiment. Left-hand figure: A P90 RF pulse flips the magnetization

vector Mz in the horizontal xy-plane. Right-hand figure: a) P90 pulse b) de-phasing signal

mainly due to differences in the local magnetic field, c) re-phasing signal after P180 pulse at

t=, d) echo at t=2.

Hahn-echo

A well-known pulse experiment is the so-called Hahn-echo (spin-echo) experiment[5]

. An RF

pulse is created with the intensity and duration such that the magnetization vector, M

, is

tilted along the x-axis by 90° (from the z-orientation to the xy-plane). This pulse is called a

P90 pulse. After this pulse, the magnetization vector is rotating with the Larmor frequency in

the xy-plane.

However, due to spatial inhomogeneity in the magnetic field strength (leading to slightly

differing Larmor frequencies of protons located in different positions), some protons lag

behind and others move ahead during their Larmor precession. This is illustrated in figure 5b.

As a consequence, the phase coherence slowly disappears, which, in turn, diminishes the

magnetization vector and leads to a fast decaying signal. By applying a P180 pulse at t=, all

protons are flipped along the y-axis by 180°. As a result, the slower moving protons are

placed ahead and the faster moving protons are placed behind. Since the individual resonance

frequency is unchanged, all protons start to re-phase (figure 5c), leading to an echo at t=2

(figure 5d). This echo is called the Hahn echo.

CPMG pulse sequence

Hahn echoes can be created multiple times by repeatedly applying P180 pulses. This pulse

sequence is called the CPMG pulse sequence and is illustrated in figure 6. The name CPMG

stands for the inventors of this sequence: Carr, Purcell, Meiboom and Gill[6]

. By varying the

time delay between subsequent P180 pulses, TE, different physical and chemical processes

can be studied. Inter-echo spacings between tens or hundreds of microseconds to several

milliseconds are typically applied during the CPMG sequence. Measuring several thousand

echoes in one run is not uncommon.

Figure 6 Illustration of the CPMG pulse sequence, creating multiple echoes.

During a CPMG sequence, the amplitudes of succeeding echoes are slightly attenuated

relative to the previous echo because the reversal of the direction of the Larmor precession by

a P180 pulse does not perfectly compensate for differences in the Larmor frequencies of

different spins. Therefore, the signal envelope of all echoes acquired during the CPMG

sequence eventually decays to noise level. This signal decay describes the return of the

magnetic moments to their equilibrium state prior to the disturbance by RF pulses and is

characterized by the so-called transverse relaxation time, T2. This is shown in figure 7.

Figure 7 The decay of the spin echoes (blue line) is characterized by the transverse relaxation

time, T2.

In weak, homogenous magnetic fields, and for simple, symmetric molecules, the T1- and T2-

relaxation times of fluids are similar, since both are governed by the molecular structure of

the fluid (such as inter- and intra-molecular distances between the hydrogen atoms) as well as

the ratio of viscosity to temperature[7]

.

Besides CPMG, a variety of RF pulse sequences have been developed for measuring T1 or

T2. By varying the timing of these pulse sequences, the measurement can be optimized to the

expected MR response of the flowing fluids.

Magnetization level

The intensity of the measured MR signal is determined by the magnitude of the macroscopic

magnetization, M

, that has been created by the aligned molecular moments in the zone of

investigation of the magnet. According to Curie’s Law [8]

,

0

22

3

)1(B

Tk

IINM

B

. (2)

In this equation, I symbolizes the magnitude of the angular momentum (spin quantum

number). To date, all oil-field MR applications exploit the magnetic resonance effect of

protons (1H), i.e., the magnetic moments in resonance are the hydrogen atoms associated with

hydrocarbon and brine molecules. The spin quantum number of protons is ½. Finally, kB

represents Boltzmann's constant and T is absolute temperature. Equation 2 shows that the

magnetization (and, hence, the intensity of the measured MR signal) is directly proportional to

the number of magnetic moments (spins), N, that are in resonance with the operating

frequency of the magnetic resonance device.

In order to determine fluid volumes from the measured MR signal, the number of magnetic

moments sensed by the magnetic resonance device needs to be related to fluid volume. This is

achieved by calibrating the MR device first to water at standard pressure and temperature

conditions, and subsequently to determine the required correction for differences in the

number of magnetic moments per unit volume between water and the measured fluid. This

correction is referred to as a hydrogen index correction. The derivation of hydrogen indices

for reservoir fluids has been extensively discussed in a variety of articles (e.g. [9]

).

4. Magnetic Resonance for Multiphase Flow Measurement

Conceptually, a magnetic resonance experiment usually consists of three sequential steps:

1. Creating a net magnetization by aligning the magnetic moments of hydrogen atoms in

an applied, constant magnetic field B0.

2. Perturbing the alignment of hydrogen atoms by employing electro-magnetic radio

frequency (RF) pulses.

3. Detecting the radio-frequency signal emitted by the hydrogen atoms during their

return to equilibrium alignment in the external magnetic field.

This measurement principle also forms the basis of magnetic resonance based flow metering [2][3]

.

Figure 8 Simplified drawing of the magnetic resonance multiphase flow meter. The flow meter

comprises two major components: the pre-magnetization section and the measurement

section.

Pre-magnetization

section Measurement

section

RF coil

Flow

The flow meter comprises two major components: a pre-magnetization section and a

perturbation and measurement section involving a radio-frequency coil (figure 8).

Pre-magnetization section – Water Liquid Ratio determination

In the pre-magnetization section, the hydrogen atoms are magnetized and a net macroscopic

magnetization is created. Figure 4 shows the magnetization of the protons as function of time.

In the flow meter, magnetization as function time can be transformed to magnetization as

function of distance to the start of the pre-magnetization section by substituting the simple

relation t=x/v where x is the position within the pre-magnetization section and v is the flow

velocity. The magnetization as function of x for a specific velocity and longitudinal relaxation

time, T1, is shown in figure 9.

Figure 9 Magnetization build-up as a function of position inside the pre-magnetization

section for a specific velocity and T1. (v= 2[m/s], T1 oil =0.15s).

An important property of oil and water, which is exploited in the MR multiphase flow meter,

is the difference in longitudinal relaxation time, T1. For most oil viscosities encountered in the

industry, the longitudinal relaxation time, T1, is significantly shorter than that of water. As a

consequence, oil magnetizes faster than water (see figure 10, left-hand figure). This difference

in the rate of magnetization build-up is used to create contrast between oil and water. By

varying the effective length of the pre-magnetization section, the signals originating from oil

and water, respectively, will be built-up to different levels, leading to quantitative information

on the water-oil ratio. This process is illustrated in figure 10. In the left-hand figure, the full

pre-magnetization length (labeled as configuration 111) is used. For the particular flow

velocity displayed, this length is sufficient to achieve complete polarization of the oil signal,

whereas the signal originating from water has been built up to approximately 30%.

Consequently, the oil-water signal ratio (S111oil/S111water) in the measuring section adjacent to

the pre-magnetization section is about 3. When the pre-magnetization length is reduced to just

the length of the measuring section magnet (labeled as configuration 000), both the oil- and

water signals are reduced but the signal from water is more strongly attenuated than the oil

signal. This leads to an oil-water signal ratio (S000oil/S000water) of about 10 (right-hand side).

Figure 10 Build-up of magnetization and, hence, signal levels, for oil and water achieved with

maximum pre-magnetization length (left-hand figure) and minimum pre-magnetization length

(right-hand figure). The magnetization build-up has been calculated for a flow velocity, v, of 2

m/s and assuming longitudinal relaxation times, T1, of 0.15 s and 2 s for oil and water,

respectively.

Figure 10 demonstrates that a strong contrast can be created between the signals originating

from oil and from water based on the difference in T1 times by varying the pre-magnetization

length. This contrast enables precise quantification of the oil-water ratio even for very high

water liquid ratios. This capability is illustrated in figure 11. In this figure, the signal ratio

S000/S111 of both oil (red) and water (blue) are plotted as a function of flow velocity.

Figure 11 Signal ratio S000/S111 as function of flow velocity for various oil-water ratios. The

signal intensities have been calculated assuming longitudinal relaxation times of 0.15 s for oil

and 2.3 s for water. A strong signal contrast between oil and water can be achieved even for

high flow rates.

For multiphase flow of oil and water, the S000/S111 curve will always fall between the pure oil

and pure water curves. The shape of curves for different water liquid ratios can be calculated

and has been added to figure 11 for water liquid ratios of 10%, 50% and 90%, respectively.

Figure 10 also shows that the sensitivity of the MR flow meter for determining the water

liquid ratio increases with increasing water liquid ratio, demonstrated by an increased

separation of the water liquid ratio lines with increasing water liquid ratio. This method

remains robust even when phase slip between the oil and water phases is present.

The water liquid ratio determination method is largely independent of the gas volume fraction

(GVF) up to high GVF’s because the gas signal is weak compared to the liquid signal. Once

the gas fraction has been determined, as discussed in subsequent chapters, the gas signal can

be subtracted from the respective signals S000 and S111. This process produces liquid-only MR

signals, which are used for water liquid ratio determination

Liquid velocity determination

The flow velocity is determined by using the so-called convective decay method. In the

measurement section, the alignment of hydrogen atoms relative to the direction of the external

magnetic field is perturbed by irradiating RF energy using a RF coil and a pre-defined pulse

sequence, such as a CPMG sequence introduced earlier. The spin echoes produced by the

sample are detected by the same RF coil also used for irradiating the RF energy. The

amplitude of the initial Hahn echo has the highest value since all excited protons are still

inside the RF coil. In contrast, when the second Hahn echo is formed about 1 ms later, a

certain fraction of excited protons has already flown out of the RF coil. Consequently, the

amplitude of the second echo will be somewhat lower than that of the first echo, even if there

was no transverse relaxation. All following echoes are successively attenuated, since more

and more protons that were excited by the initial RF pulse have left the RF coil. This

‘convective decay’ of the echo signals is shown in figure 12.

Figure 12 Velocity determination using the so-called convective decay method. Ignoring

magnetic relaxation effects, the measured echo amplitude decreases linearly with time since

the excited protons are leaving the RF coil due the flow. At a certain time all excited protons

have left the RF coil. The RF coil length divided by the time at which the red dashed line

intercepts the horizontal axis yields flow velocity.

The signal decay is proportional to the flow velocity. The higher the flow the faster the echo

decay. The flow velocity is equal to the length of the RF coil, Lc (typically 10 cm), divided by

the time at which the red dashed curve intercepts the x-axis, tS=0, so v = Lc/tS=0 [10]

.

Combining the convective decay method with varying the pre-magnetization length enables

the direct measurement of velocity slip between the flowing liquid phases. As shown in figure

10, signal contrast can be created between water and oil by modifying the effective pre-

magnetization length. For a long pre-magnetization length, the initial signal, S0, in figure 12 is

a composition of signal contributions originating from both oil and water.

The velocity that is obtained from the convective signal decay is a composition of the oil and

water velocities. In contrast to this, the initial signal amplitude corresponds predominantly to

oil if a short pre-magnetization length is selected, cf. figure 12. Consequently, the velocity

that is being measured for this pre-magnetization configuration predominantly reflects the oil

velocity. This, in combination with the measured water liquid ratio, makes it possible to

determine both the oil and water velocities independently.

Gas volume fraction and gas velocity determination

The MR flow meter is capable of directly measuring the Gas Volume Fraction (GVF) and the

gas phase velocity. However, as patents on this are still pending the background of this

technique will not be further discussed in this paper.

5. Mechanical design of the MR multiphase flow meter

The current flow meter is operated in a horizontal configuration. The design is an

industrialized version of the improved and tested prototype version and has a 4” 600lbs, full

bore pipe. No specific upstream flow conditioning measures are required. The connecting

flanges are made from stainless steel (SS) or carbon steel (CS). The overall flange-to-flange

length is about 3m. Maximum process temperature is 100°C and the minimum and maximum

ambient operating temperatures range from -40°C to +65°C. All electronics are mounted

directly on the flow meter in two flame-proof boxes (see figure 13).

Figure 13 External mechanical design of the industrialized version of the MR multiphase flow

meter. All electronics are mounted on the flow meter in explosion-safe boxes. The overall

length is about 3 m.

Tube design

An important feature of the MR flow meter is that there are no protrusions into the pipe.

Inside the flow meter, the transition is made from steel flanges to a glass fiber reinforced

epoxy (GRE) pipe (see figure 14). A non-conductive pipe section is required to allow high

frequency electromagnetic waves to be transmitted into the pipe. All measurements are made

from outside of the tube. There is no feed-through or anything else puncturing the tube.

The selected GRE pipe and flanges are frequently used in the oil and gas industry and possess

all required certifications and approvals. The forces from the connected piping system are

guided through the external housing in order to prevent mechanical loading of the GRE pipe.

Figure 14 The internal mechanical design of the industrialized MR multiphase flow meter.

The flow meter has a full bore design.

Pre-magnetization section

The pre-magnetization section consists of three individual pre-magnetization units. A pre-

magnetization unit consists of two concentric rings; an inner and outer ring. The outer ring

can be rotated around the inner ring using electro-motors. The magnetic field strengths of the

outer and inner rings are equal. By putting both rings in the upward direction (+/+), full

magnetic field strength is obtained. By rotating the outer ring by 180° (+/– ) the magnetic

field created by the inner ring is cancelled out by the outer ring. Each pre-magnetization

section has one motor mounted in a flame-proof housing.

Main magnet – Measurement section

The magnetic field strength in the main magnet is equal to the magnetic field strength in the

pre-magnetization section. However, the magnetic field homogeneity of the main magnet is

orders of magnitude higher than the homogeneity of the pre-magnetization section. The high

field homogeneity is necessary for proper proton resonance measurements.

In contrast to the magnetic field strength inside the pipe, the magnetic field strength outside

the meter housing is very weak (< 0.5 mT). This level is below the maximum field strength

legally specified by standards. The value 0.5 mT is about 10 times stronger than the Earth’s

magnetic field strength.

In between the main magnet and the GRE pipe, the radio frequency coil is located. The RF

coil is transmitting and receiving the signals and is matched and tuned to the Larmor

precession of flowing phases by a matching and tuning circuit mounted in the electronics

housing.

Outlet section

In the outlet section, the GRE pipe is coupled to a SS or CS flange section. In the metal spool

piece a pressure transmitter is mounted. This pressure transmitter can be replaced without

depressurizing the system. The body temperature of the pipe section is measured on the

outside of the metal spool piece inside the external housing. Thermal insulation makes sure

that the temperature measurement is representative for the process fluid temperature.

6. Tests at commercial flow loop with improved prototype

Prototype versions of the magnetic resonance flow meter has been tested at a commercial

flow loop located at the Southwest Research Institute multiphase test facility in San Antonio,

Texas, USA, in February and March, 2013 (figure 15).

SwRI Test facility

The operation of the flow loop consists of a multiphase flow stream from a pump discharging

into a separator where the flow components are divided into gas and oil and water streams

that can be separately metered and routed to the test section. The liquid flow rate is measured

with a Coriolis meter. Specified uncertainty is less than 0.5% at the high flow rates to around

1% at the lower flow rates.

Figure 15 Tests with an improved prototype version of the magnetic resonance flow meter at

the multiphase test loop of SwRI at San Antonio, February/March 2013.

The small amount of gas present in the liquid due to imperfect separation is measured with a

separate device. The uncertainty of this GVF meter is 5% of reading.

The water liquid ratio of the liquid mixture was monitored through collection (figure 16) and

the averaging of two liquid samples taken at each test point .

Figure 16 Manual water liquid ratio determination by means of samples.

The reference gas flow rate was measured using an orifice meter for the higher flow rates and

a small bore (½”) Coriolis meter for the lower flow rates (< 12 m3/h). The typical uncertainty

specified for the dry gas orifice flow meter is 0.5 to 1.0% (dependent on the flow rate) and

0.35% for the Coriolis meter. Fresh water, oil (Regal R&O 32 VSI, viscosity about 43 cSt @

32°C) and methane have been used as test fluids. The operating pressure and temperature was

around 83 bar(g) bar(g) and 32°C respectively. The liquid flow rates have been varied from

0.64 to 46 m3/h and the gas flow rate from 2 to 107 m

3/h in varying ratios (see table 1).

Test Conditions Range

Static pressure, bar(g) 82.7

Temperature, °C 32

Liquid Flow Rate, m3/h 0.64 – 46.3

Gas Flow Rate, m3/h (actual) 2.0 – 107.0

GVF, % 0, 25, 50, 70, 90 and 100

Water liquid ratio, % 0, 30, 60, 80-95 and 100

Flow regimes Single phase, stratified, stratified

wavy, plug and slug flow

Table 1 Overview of the multiphase test conditions.

Tests at six different gas volume fractions (GVF) have been carried out: 0%, 25%, 50%, 70%,

90% and 100% (see figure 17).

Figure 17 Gas flow versus total liquid flow at various gas volume fractions. A value of 1.0

actual cubic feet per second (acfs) corresponds to about 102 m3/h. The value of 200 gallons

per minute (gpm) corresponds to about 45.4 m3/h.

The Water Liquid Ratio has been tested at 0%, 30%, 60%, 80%, 95% and 100%. Since it took

significant time to precisely adjust the WLR to a certain desired value, the WLR test points in

the range of 80 to 95% have been approximately set by the test loop operator. The WLR value

as obtained was accepted without further adjustment. This procedure saved valuable test time.

An overview of the test points for GVF versus WLR is given in figure 18.

Figure 18 Test points plotted as gas volume fraction versus water liquid ratio. The blue dots

indicate the three phase test points, the red dots are marking the two phase test conditions.

Test results

As an example, figure 19 shows the amplitudes of successive Hahn echoes as function of time

during a CPMG sequence for a three phase flow situation (WLR=77%, superficial liquid

velocity 1.64 m/s, superficial gas velocity 0.53 m/s, plug flow according flow map). By means

of a linear fit through the data points the bulk flow velocity could be determined (as explained

in figure 12).

Figure 19 Echo amplitudes as function of time during a CPMG sequence for a three phase

flow situation. WLR=77%, superficial liquid velocity: 1.64 m/s, superficial gas velocity 0.53

m/s (plug flow regime according flow map).On basis of the linear fit through the data points,

the bulk velocity could be determined.

In chapter four, it has been explained that contrast between oil and water (which is used for

water liquid ratio determination) can be created by exploiting differences in the magnetization

build up associated with different pre-magnetization lengths. It is essential that the

magnetization build-up corresponds to the theoretically predicted levels. Experimental and

theoretical results are compared in figure 20. This figure shows the magnetization build-up for

different pre-magnetization lengths as function of flow velocity. The signal with maximum

pre-magnetization length is used to normalize the data. The symbols represent experimental

data, whereas the solid lines display the curves predicted by theory. The left hand figure is the

result of gas measurements, the right hand figure is an experiment on water.

Figure 20 Magnetization build-up as function of velocity for different pre-magnetization

lengths. The maximum pre-magnetization length (Lp111) has been taken to normalize the

data. Left-hand figure: single phase gas, right-hand figure: single phase water. The symbols

are experimental data, the solid lines are the curves predicted by theory.

A similar graph for a multiphase situation (WLR=28%, GVF=50%.) is shown in figure 21.

This graph is comparable to figure 11. The dark blue line corresponds to single phase water

(equal to light blue line in figure 20 right-hand side), the red line corresponds to single phase

oil. The blue dot represents the value that has been measured, leading to a WLR reading of

29%. The green line is the reference WLR value, which is equal to 28%.

Figure 21 Signal ratio acquired with minimum and maximum pre-magnetization lengths,

S000/S111, as function of flow velocity for various oil-water ratios. The blue dot represents the

value that has been measured, leading to a WLR reading of 29%. The green line is the

reference WLR value, which is equal to 28%. The GVF in this experiment was 50%.

Next to the WLR, the total liquid flow rate and the gas flow rate were determined. On the

basis of these parameters, the GVF, oil flow rate and water flow rate were derived. Two

examples of analysis are shown in figures 22 and 23.

Figure 22 (left-hand) shows the superficial gas and liquid velocities of both the reference and

the MR multiphase flow meter. The right-hand figure shows the WLR versus the superficial

liquid velocity for both the reference and the flow meter reading (WLR=28%, GVF=50%).

Figure 23 is analogue to 22 but for a WLR of 92% and a GVF of 90%.

Figure 22 Superficial gas and liquid flow velocity (left-hand) and WLR versus superficial

liquid velocity (right-hand). Both the reference and MR multiphase reading are shown.

WLR=28%, GVF=50%.

Figure 23 Superficial gas and liquid flow velocity (left-hand) and WLR versus superficial

liquid velocity (right-hand). Both the reference and MR multiphase reading are shown.

WLR=92%, GVF=90%.

This analysis has been carried out for all 41 multiphase test points. The results for the total

liquid flow, gas flow, oil flow and water liquid ratio are shown as a cumulative performance

plot[11]

in figure 24. All 41 multiphase tests points have been included. In this graph we find

the deviation between the reference and the MR multiphase flow meter on the x-axis, and the

cumulative percentage of the test points that fulfill the deviation criteria on the y-axis. The

full scale value is 2 m/s. This full scale value is based on the range tested.

Figure 24 Results on total liquid flow, gas flow, oil flow and water liquid ratio shown in a

cumulative performance plot. This plot provides the percentage of test points that show a

particular deviation between experimental and reference data displayed on the x-axis. All

three phase test points (N=41) are included in this graph.

A number of comments need to be made:

For the gas flow measurements, a simplified interpretation model was used. This

model has a number of known limitations. A new model is available, but was not

implemented at the time of testing the flow meter. Implementation of the new gas

model will significantly improve the results.

WLR: Analysis of these results provided us with input for further model improvement.

Calculations indicate that a significant improvement in the accuracy of water liquid

ratio determination can be expected too.

Liquid flow: A similar improvement is expected for the determination of liquid flow

rates.

In order to quantify the performance of the MR flow meter at different flow conditions, the

data points, as shown in figure 24, could be split up in different quadrants; lower and higher

WLR, lower and higher GVF. Preferably, a split in four different GVF ranges is made[11]

.

However, due to the limited number of data points, this would lead to statistically unreliable

curves. For this reason the data set has been divided into two GVF ranges, a higher and lower

GVF range with a division at GVF=75%. In the higher GVF range (10 experiments), all

measurements on the different water liquid ratios are combined. The lower GVF range is split

in three water liquid ratio ranges: an oil-continuous phase (0<WLR<50%), a transition zone

(50%<WLR<85%), and a high water liquid ratio range (WLR>85%). The results are shown

in figure 25.

Figure 25 Results on total liquid flow, gas flow, oil flow and water liquid ratio shown in a

cumulative performance plot, divided into four quadrants. The data set was divided at a GVF

of 75%. For GVF>75% all test points are plotted in one graph; for GVF<75%, the test points

are divided into three WLR ranges. The full scale value is 2 m/s.

On basis of these results, we observed that there is not a specific range in which the meter

shows a particular weakness. At the same time, we observe an improved performance with

increasing WLR. This corresponds to the expected performance as explained in figure 11,

which shows an increasing sensitivity of the MR flow meter with increasing WLR.

Consequently, based on the theoretical characterization of the MR flow meter and the

extensive three-phase flow tests, the sweet spot of the meter was identified in the high WLR

range at lower GVF’s.

As mentioned before, analysis of the experiments should lead to significant model

improvements. These improvements shall be implemented in the new industrialized version of

the MR multiphase flow meter (shown in figures 13 and 14).

An industrialized version of the MR multiphase flow meter (see figure 26), as conceptually

described in chapter 5, will be tested in various commercial flow loops in Q4, 2013.

Following successful flow loop tests, a field test at a hydrocarbon production facility near

Rotterdam, The Netherlands, has been scheduled. This oil field has a newly equipped test

separator for providing reference data. The field trial will in addition test the field-robustness

of the flow meter. The various wells in production offer a reasonable range of flow rates and

water liquid ratios.

Figure 26 Picture of the industrialized version of the MR multiphase flow meter. The flow

meter is mounted in a production frame that will be removed during installation. 7. Diagnostic capabilities and future potential

In addition to flow measurements, the MR multiphase flow meter has strong diagnostic

capabilities. The matching and tuning of the electronic circuitry contains valuable information

about the process conditions inside the tube. The same holds for the spin echo shape. Another

example is the possibility to acquire a T2 relaxation time spectrum analysis. An illustration is

given in figure 27.

By using the data obtained as shown in figure 7, a T2 relaxation time spectrum can be

generated for either static fluids or if the flow velocities have been determined. As addressed

earlier, the T2 relaxation time is directly related to molecular structure. Figure 27 shows a

spectrum of transverse relaxation time, T2, which is generated from a bottle filled with

approximately 70% air (no signal), 27% water and 3% oil. The composition of the synthetic

oil was not known. The relaxation time spectrum shows a strong peak at about 2.2 s, which

corresponds to the transverse relaxation expected for bulk water.

Figure 27 Example of a T2 relaxation time spectrum analysis using prototype 1. There is a

clear distinction between the MR relaxation times associated with water and oil. This analysis

enables an estimation of the water-oil composition.

Two additional peaks were observed at 65 ms and 300 ms, respectively, corresponding to a

viscosity of about 12 and 4cSt. Standard T2-viscosity correlations have been used. According

to this analysis, the oil sample under test is a composition of two oils (about 70% of 12 cSt oil

and about 30% of 4cSt oil). This composition has subsequently been confirmed by the

manufacturer of this oil. Besides typical flow metering applications, this specific example also

illustrated the future potential of MR technology when considering additional diagnostic

capabilities.

8. Summary and Conclusions

This paper describes the concept and industrialization of a magnetic resonance based

multiphase flow meter. After introducing the magnetic resonance principle, its application to

multiphase flow measurement has been described.

It is demonstrated that magnetic resonance technology enables a very elegant and direct way

of measuring multiphase flow. In fact, it is the molecular flow of each individual phase that is

being measured.

The capabilities of the magnetic resonance based multiphase flow meter has been

demonstrated with two prototypes that have been extensively tested at various multiphase

flow conditions.

The experimental data acquired during these measurements, show good performance of the

flow meter across the entire application range with respect to the determination of both the

gas volume fractions, water liquid ratio and the individual phase flow rates. In addition, data

analysis shows that further improvements to the interpretation models are feasible. The

improved models are expected to be available for the next series of tests.

While the magnetic resonance flow meter was found to work well across the entire

application range, the experiments indicated that the meter is most sensitive for high water

liquid ratio conditions (WLR>85%) in combination with lower gas volume fractions

(GVF<75%). This sweet spot is in accordance with the theoretically expected performance.

In addition to multiphase flow metering capabilities, magnetic resonance offers many

powerful diagnostic features with significant future potential. Some of these features have

been illustrated in this paper.

An industrialized version of the MR multiphase flow meter is currently built. All options for

improving the performance of the flow meter identified during three-phase flow tests in

commercial flow loops will be taken into consideration for this model. It is expected that the

first industrial version of the magnetic resonance flow meter will be available for testing in

various flow loops and oil field installations in the fourth quarter of 2013.

9. Acknowledgements

The authors of this paper like to express their appreciation to the entire MR development team

and are particularly grateful to Marco Zoeteweij and Olaf Bousché from KROHNE for their

contribution.

10. References

[1] M. Appel, Nuclear Magnetic Resonance and Formation Porosity, Petrophysics, Vol.

45, No.3, (May-June 2004); p296-307.

[2] M. Appel, et al., Robust Multi-Phase Flow Measurement Using Magnetic Resonance

Technology, Society of Petroleum Engineers, MEOS, Manama, Bahrain, 6-9 March,

2011.

[3] Jankees Hogendoorn and Matthias Appel, Magnetic Resonance for the Future; A New

Methodology to Measure Multiphase Flow, 4th

International EMBT Conference,

Hannover, 20.-21. March, 2013.

[4] G. Falcone, et al., Multiphase Flow Metering, Principles and Applications, Vol. 54,

Developments in Petroleum Science, Elsevier, 2010.

[5] E. L. Hahn, Spin Echoes, Phys. Rev. 80 (1950) 580.

[6] P.T. Callaghan, Principles of Nuclear Magnetic Resonance Microscopy, Clarendon

Press, Oxford, 1991.

[7] N. Bloembergen, E. M. Purcell, R. V. Pound, Relaxation Effects in Nuclear Magnetic

Resonance Absorption, Phys. Rev. 73 (1948) 679-712.

[8] A. Abragam, The Principles of Nuclear Magnetism, Oxford University Press, New

York, 599 p. (1961).

[9] Y. Zhang, et al.: Oil and Gas NMR Properties: The Light and Heavy End, 43rd Annual

SPWLA Annual Logging Symposium, (June 2-5, 2002), Oiso, Japan, 2002.

[10] T.M. Osán, et al. : Fast measurements of average flow velocity by Low-Field 1H NMR,

Journal of Magnetic Resonance (1969) 209(2):7, 2011.

[11] Handbook of Multiphase Flow Metering, Norwegian Society for Oil and Gas

Measurement, revision 2, March, 2005.

http://pubget.com/journal/0022-2364/journal-of-magnetic-resonance-1969

http://pubget.com/journal/0022-2364/journal-of-magnetic-resonance-1969?issue=2&volume=209

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Stamp

Flow swirl and flow profile measurement in multiphase flow

Anusha Rammohan1, Aditya Bhakta1, Vinay Natrajan1, John Ward2, and Manoj KumarKM1

1GE Global Research, Bangalore, India2GE Oil and Gas - Measurement and Control, Groby, UK

Abstract

Accurate multiphase flow measurement hinges on the ability to measure flow parameters such as compo-

nent phase fractions and velocities with high accuracy. Since, the fractions and velocities are not always

uniformly distributed in the measurement cross section, any measurement system’s inability to account for

spatial variations can result in a high degree of uncertainty in the estimated flow rate. This paper describes

a method using an impedance based measurement system using which the profiles of phase fraction and

velocity for a vertical pipe downstream of a blind tee can be measured and characterized. Using the same

measurement, a modified cross correlation technique provides the vertical and horizontal components of the

velocity in a swirling flow. This velocity information along with the phase fraction profile characterizes

the flow profile completely. In addition to this analysis, the potential effect of swirl on differential pressure

measurements is addressed briefly.

Nomenclature

τi jmax Time shift corresponding to maximum cross correlation for pixel i j, page 6

P Pressure, page 13

Ri j,klxy Cross correlation of series x at pixel i j with y at pixel kl, page 10

Ri jxy Cross correlation of series x and y at pixel i j, page 6

Vi j Velocity measured at pixel i j, page 6

ρ Density, page 12

1

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Stamp

τ Time shift for cross correlation, page 6

dP Differential pressure, page 12

di jrad Radial distance traveled by pixel i j, page 10

dax Axial distance between cross correlation planes, page 10

r,θ ,z Cylindrical coordinates, page 12

t Time instant, page 6

t Time instant, page 12

ur Radial component of velocity, page 12

uθ Angular component of velocity, page 12

uz Vertical/Axial component of velocity, page 12

xi j,yi j Time series at two planes of measurement at pixel i j, page 6

GVF Gas volume fraction, page 4

WLR Water in liquid ratio, page 4

2

1 Introduction

One of the biggest challenges in multiphase flow metering is the presence of varying spatial distributions of

the different fluids within the cross section of the pipe. Any measurement technique employed in a multi-

phase flow meter is expected to measure the flow parameters of interest with the same fidelity irrespective

of the cross sectional distribution including asymmetries and non-uniformities in the flow profile. This

requirement is extended to the velocities as well as component fractions. Additionally, the implicit but ubiq-

uitous assumption in most flow rate measurement systems is that the fluid flow is always parallel to the pipe.

For example, for vertically placed flowmeters, the flow is assumed to be restricted to the vertical direction.

These assumptions and approximations can result in significant inaccuracy when the flow is asymmetric or

irregular.

Most multiphase measurement methods tackle this problem in one of two ways. One approach is to condition

the flow with some sort of a flow conditioner to ensure flow profiles that are favourable for measurement.

A popular configuration is to use a blind tee with the meter placed in the vertical section of the flow. But

the effectiveness of flow conditioners such as the blind tee has not been proven conclusively under all flow

conditions. For instance the blind tee can cause gas holdup or induce swirl. The only alternative is to

correct for the existence of non-uniform flow profiles. This in turn requires two pieces of information, a)

the actual flow profiles and flow direction at the measurement section and b) a translation of the flow profile

into a suitable measurement model. Generally speaking, accurate flow profiles are difficult to measure.

This is especially the case for sensor measurements that do not cover the entire cross section or are unable to

resolve radial or even temporal variations. Given this potential lack of information, the measurement models

have to be tweaked using empirical calibration instead of measured flow profiles in the hope of capturing

the missing information. As with any data based technique, the performance of the corrected models can

become inaccurate at best and unpredictable at worst.

An important step towards understanding the impact of the errors due to flow profile and flow direction

variation on multiphase measurement is to first characterize it. This paper proposes a method to measure

and characterize the flow by quantifying the variation of velocity (magnitude and direction) and component

fractions in the flow cross section. For the purpose of this paper, we have considered vertical three phase

flows consisting of gas, water and oil. The measurement system consisted of two sets of electrical impedance

sensors placed axially separated in the vertical section downstream of a blind tee. Section 2 describes the

process of flow profile measurement and characterization. Section 3 describes the detailed analysis of the

phase fraction and velocity profiles measured with the proposed method under different flow conditions for

two different configurations. Section 4 focuses specifically on measuring and quantifying flow swirl and

3

how errors due to swirl can be corrected. Section 5 concludes this paper with a summary of the key learning

from this work and possible opportunities for future studies.

2 Flow profile measurement

Accurate multiphase flow measurement, invariably needs flow physics or semi-empirical models in con-

junction with suitable measurement technologies for estimating the fractions and velocities of individual

components. A detailed description of these models are provided in Rammohan et al. [9]. While typical

flow models are well grounded in physics, certain approximations are made either because they make the

analysis easier or because the necessary information is missing or hard to determine. One such assumption

is about symmetry and uniformity of flow profiles. Apart from flow models, several sensor measurement

models also make the same assumption. Ideally, the flow is expected to be uniform and time-invariant but

that is rarely the case especially under real field conditions. Not only does the flow change over space and

time, the changes are rather hard to predict even if the flow conditions are completely known. While em-

pirical models can be built with historical data to predict non-uniformities or correct for them, there is no

guarantee that these corrections will work under conditions outside the limits of the historical data. Thus,

the only way to accurately and reliable correct for profile non-uniformities in the model, is to measure the

actual flow profile using a suitable measurement technique.

Earlier attempts have been made to measure the phase fraction and velocity profiles using tomographic

techniques (Abdulkadir et al. [1], Ohnuki and Akimoto [8], Thiyagarajan et al. [10]). Researchers have also

quantified the shape of the measured flow profiles for different flow conditions, which can then be readily

used in flow models. Detailed though these studies are, there are several key pieces of information missing:

7 Flow profiles are mostly defined and characterized for fully developed flow. There is little information

on profiles in developing multiphase flows (downstream of a blind tee in specific).

7 Potential asymmetries in the flow have been explored for inclined pipes (where asymmetries are ex-

pected to occur) but not for vertical flows. (Al-Hinai [2])

7 Flow irregularities such as flow swirl have not been measured or characterized for multiphase flow.

In this paper, we hope to address these important aspects with the aid of a suitable measurement technique

followed by a detailed analysis of the measurement data.

4

2.1 Impedance measurement system and experimental setup

Electrical sensors have been widely used for measurement of phase distributions in multiphase flows. This

is mainly due to the accuracy of the measurement and ease of interpretation of the data. Moreover, using

a multitude of impedance sensors, the cross sectional distribution of the dispersed phase can be recon-

structed with higher resolution and accuracy. The details of a preferred reconstruction method are disclosed

in Mahalingam et al. [6] and Mahalingam et al. [5]. This method is fast, less computationally complex

than traditional reconstruction, robust and accurate in reconstructing the spatial distribution of the dispersed

phase, which in this case is the gas phase. The measurement system consists of 2 sets of electrodes along the

circumference of the pipe placed spatially apart in the direction of the flow. A high speed data acquisition

and data processing system provides reconstructed fraction phase images at around 2000 frames/s. This con-

figuration with the capability for high speed acquisition is conducive for not only capturing instantaneous

changes in the flow cross section, but also for velocity measurement with high resolution. The subsequent

sections describe detailed analysis using the measurements from the same impedance measurement system

but placed with different pipe configurations, at different distances from the blind tee and with test data

collected at different loops including the National Engineering Lab (NEL) in Scotland, UK and the South

West Research Institute(SWRI) at Texas, USA. The configuration and test setup for the data presented in

this paper are listed in Table 1.

Test site NEL SWRI

Measurement distance from blind tee 35D 5D

GVF range 0-95% 0-95%

WLR range 0-100% 0-100%

Liquid flow rate 15000 bbl/day 26000 bbl/day

Gas flow rate 1.3 acfs 1.7 acfs

Pressure 5 bar 10-100 bar

Horizontal run to blind tee 35 m 1 m

Table 1: Test conditions

All data for the test points presented in this paper are averaged over 2 minutes, after it was ensured that the

reference readings were stabilized. It is important to note here that the stability established in this manner

is indicative of the inlet conditions only. Once, the flow is mixed, it is hard to guarantee stability at the

measurement location.

5

2.2 Phase fraction and velocity profile measurement

Typical flow profiles as reconstructed using the impedance measurements are shown in figures 1a and 1b at

two different gas volume fractions. Each pixel in the image is an average phase fraction for the area covered

by that pixel. The best possible pixel resolution is related to pipe diameter and the number of impedance

sensors placed on the circumference of the pipe.

(a) GVF=50% (b) GVF=20%

Figure 1: Phase fraction profiles in the pipe cross section reconstructed from impedance measurements (NEL

test data)

As is evident from these figures, the cross sectional phase fraction profile thus generated provide valuable

information about the distribution of the gas and in turn the liquid as well. A more detailed analysis of phase

fraction profiles under different flow conditions is covered in section 3.

With the measurements from two spatially separated sets of impedance sensors, there is the added possibility

of obtaining the flow velocity using cross correlation of the images. Image cross correlation is a recently

explored technique for estimating the distribution of velocity within the cross section. A straight forward

implementation of the image cross correlation technique involves the following steps:

1. For a particular pixel in the ith column and jth row in the image, create time series in plane 1 and 2

from the reconstructed phase fraction

6

2. Cross correlate the two time series using the expression

Ri jxy(τ) =

N

∑t=0

xi j(t)yi j(t− τ) (1)

3. From the time shift τ for which the cross correlation is maximum, find the velocity at location i j, given

the distance between the sensors using

Vi j = dax/τi jmax (2)

4. Repeat steps 1-3 for all pixels to obtain a Velocity Map

Figure 2 shows typical velocity maps obtained using the method described above. Section 3 explores the

variation in the velocity profile under different conditions in more detail.

(a) Total superficial velocity = 2.7 m/s (b) Total superficial velocity = 6.8 m/s

Figure 2: Velocity maps using image cross correlation; color-map shows magnitude of velocity(NEL test data)

3 Phase fraction and velocity profile analysis

3.1 Developed flow

Developed flow conditions are ideal for studying and characterizing the variations of phase fraction and

velocity profiles for different flow rates. Hence, this topic has formed the theme for several prior work that

7

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n) (o) (p)

(q) (r) (s) (t)

Figure 3: Phase fraction and velocity profiles for developed flow (NEL data): (a) to (d) at 15% GVF, (e) to (h)

at 30% GVF, (i) to (l) at 50% GVF, (m) to (p) at 70% GVF, (q) to (t) at 90% GVF

8

involved characterizing and quantifying flow profiles (Abdulkadir et al. [1],Ohnuki and Akimoto [8],Thiya-

garajan et al. [10]). A significant portion of published literature on this topic focused on smaller diameter

pipes; the applicability of the conclusions needs to be considered carefully for larger diameters (say more

than 4"). If there is a dearth of references on phase fraction profiles, there is even less work on velocity pro-

files in multiphase flow. This is due to the fact that unlike phase fraction distributions which can be obtained

by several different types of sensors, the velocity distribution especially in a multiphase flow is generally not

very easy to measure using simple sensors and/or data acquisition systems. Al-Hinai [2] analyzed both phase

fraction and velocity distributions in vertical as well as inclined pipes using impedance based measurements

but for solid-liquid flows.

The experimental data collected at NEL fits the category of developed flow as the distance from the blind

tee to the measurement location was close to 35 times the diameter. Figure 3 shows the experimentally

determined phase fraction and velocity profiles in the cross section for increasing GVF. For the profiles

shown in figures 3a through 3t, the WLR was kept almost a constant at 80% in order to study the effect of

gas and liquid flow rate variations. The first column of figures are images of phase fraction reconstructed

from the impedance data. The second column shows two perpendicular radial cross sections (cross hairs

indicated in figure 1a). The third column shows the corresponding velocity distributions obtained through

cross correlation. The fourth column shows extracts of this distribution for two perpendicular radial cross

sections.

Several interesting observations can be made from figure 3 for developed flow under the flow conditions

tested at NEL.

* Phase fraction profiles are radially symmetric and well defined. They follow patterns described in

literature for developed flow.

* At lower phase fractions (and lower GVF), the difference between the gas phase phase fraction near

the wall and at the centre is not as pronounced in comparison with higher GVF flow conditions. This is

consistent with the expectation that the flow transitions from bubbly to slug to churn and churn-annular

and this transition is approximately related to increasing GVF.

* Velocity profiles are well defined but for certain flow conditions (pronounced at higher total flow rates)

there is undeniable radially asymmetry in the distribution. This asymmetry is consistently seen only

along one of the radial cross sections.

* All the flow conditions depicted in figure 3 are likely to be in the turbulent regime, a fact that is

corroborated by the nearly flat velocity profiles for most of the conditions tested.

9

3.2 Developing flow

Mutliphase flow meters are rarely given the luxury of sufficient development length due to constraints on the

piping and placement of the meter. Thus, from a practical point of view, meters are more likely to encounter

under developed or developing flows. Developing flows are not the most most favourable conditions from

a measurement perspective. Hence, a flow conditioner such as a blind tee is typically placed upstream

of the meter in order to ensure mixing of the different phases. An ideally mixed flow has advantages for

measurement as it allows for certain assumptions such as radial symmetry in flow profiles and minimal slip

between the phases to name a few. But these assumptions are only valid if the blind tee does result in an

ideally mixed flow. With the above described techniques for measurement of flow profiles, such assumptions

on the flow profile can be easily investigated.

(a) GVF=40% (b) GVF=25%

Figure 4: Phase fraction image from impedance measurements (SWRI test data, no development length)

The data presented in this section have been acquired during a month long test at the South West Research

Institute (SWRI) for varying flow conditions. For this particular analysis, a subset of the data was used

where the WLR was again kept almost invariant at 80% and the line pressure was maintained at 100 bar in

order to study the variation with flow rates.

The phase fraction images shown in figure 4 were obtained using the same method as the one used for the

analysis of the NEL test data. Figures 4a and 4b clearly show radial asymmetry in the distribution of gas.

To understand this asymmetry, one needs to understand the configuration of the measurement system with

respect to the blind tee. Figure 5 shows the view of the reconstructed phase fraction image from the top

(looking down the vertical section).

10

Figure 5: Configuration of blind tee with respect to the reconstructed phase fraction image (view from top)

Quite evidently, the asymmetry is in the direction of the inlet to the blind tee. This would make sense as the

blind tee may not be the ideal mixer that it is touted to be. It is not hard to imagine a situation where there

is stratified, plug or slug flow in the horizontal section before the blind tee with the gas being concentrated

predominantly on the top part of the pipe above the liquid. If the blind tee doesn’t mix the contents as is it

expected to, then this flow would continue undisturbed onto the vertical section where the asymmetry would

be apparent with the gas being more concentrated in the section on the same side as the inlet to the blind

tee. This theory would then explain the radially asymmetry seen in the figure 4. 6 shows more such phase

fraction distributions and profiles in the figures in columns 1 and 2. The profiles along the two selected radial

cross sections show a pronounced asymmetry in stark contrast to the profiles from the NEL data (figure 3).

Clearly the asymmetry is systemic and is seen for most of the flow conditions tested.

The third and fourth columns of figure 6 depict velocity profiles for varying GVF. There are small asym-

metries for some of the data, but these seem insignificant, especially in comparison with the asymmetries in

phase fraction. Also, in comparison with the velocity profiles for developed flow, (figure 3), the profiles in

figure 6 for a location next to the blind tee are flatter and show little variation going from the center to the

wall. This could quite easily be a characteristic of developing flows.

Even though the flow conditions tested at NEL and SWRi were different (Eg, fluids, ranges of flow rates,

differences in loop configurations), two aspects of interest were the development length and the upstream

horizontal run upto the blind tee. From the data presented here as well as that in literature, development

length is quite likely to be a determining factor for the phase distribution within the cross section. But

more importantly, with the kind of distributions seen in figure 6, mutliphase models cannot be justified in

assuming radially symmetric profiles.

11

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n) (o) (p)

(q) (r) (s) (t)

Figure 6: Phase fraction and velocity profiles for developing flow (SWRI data): (a) to (d) at 15% GVF, (e) to

(h) at 40% GVF, (i) to (l) at 50% GVF, (m) to (p) at 70% GVF, (q) to (t) at 80% GVF

12

4 Flow swirl analysis

While the phase fraction distribution and velocity distributions provide a clearer picture of the evolution of

the flow at the measurement location, flow characterization is incomplete without analyzing the direction of

the velocity. Conventional wisdom dictates that a developed flow in a vertical pipe would flow only in the

vertical direction with little deviations in the horizontal directions. This assumption would definitely ease

the burden of flow measurement as it simplifies the problem to a great extent. But the existence of horizontal

components to the velocity could potentially affect multiphase measurement systems including sensors such

as the differential pressure sensors. Gibson and Reader-Harris [3] and Gupta and Kumar [4] analyzed swirl

in single phase flow using both CFD simulations as well as measurements from particle image velocimetry

techniques. Gibson and Reader-Harris [3] also looked at the effect of swirl on a venturi and an orifice plate

placed downstream of a double bend and characterized the variation of the discharge coefficient with the

swirl angle.

In this paper, we specifically focus on measuring swirl in mutliphase flow downstream of a blind tee. Further-

more, we demonstrate the potential effects of swirl on differential pressure measurements across a venturi.

4.1 Flow swirl measurements

By its very design, the image cross correlation method described in the section 2.2 estimates only the axial

(vertical) component of the velocity and the drawback here is the assumption that the flow is parallel to the

pipe. When the flow is not unidirectional and there may be swirling flows, this can be a limiting assumption

that is likely to affect the accuracy of the velocity estimate. Moreover this method does not provide any

information about other components of velocity, for example, the in-plane (in the plane of the measurement

cross section) velocity component. If there existed a method that could measure a 3 dimensional velocity

map, this would undoubtedly be a powerful piece of information for not just understanding the flow better,

but also for augmenting simplistic flow models that assume unidirectional flow.

Mosorov et al. [7] described a method they called the "best-correlation pixels" to allow for particles trav-

eling possibly at different angles with respect to the pipe. The algorithm is a modified version of the steps

described in section 2.3 and proceeds as follows:

1. For a particular pixel in the ith column and jth row in the image, create time series in plane 1 and 2

from the reconstructed phase fraction

13

2. Cross correlate the two time series using the expression

Ri jxy(τ) =

N

∑t=0

xi j(t)yi j(t− τ) (3)

3. Repeat steps 1 and 2 with i and j changing for plane 2 such that it scans a region in the image around

pixel ij using the following modified cross correlation

Ri j,klxy (τ) =

N

∑t=0

xi j(t)ykl(t− τ) (4)

where k ∈ [i−m, i+m] and l ∈ [ j−m, j+m]

4. Find pixel l and k which maximizes Ri j,klxy (τ) and the corresponding time shift τ

5. From the time shift τmax for which the cross correlation is maximum, find the axial velocity, given the

distance between the sensors using

Vaxiali j = dax/τi jmax (5)

The radial component of the velocity is given by

V radiali j = di jrad/τ

i jmax (6)

The radial displacement drad can be calculated as the distance between pixel i j and pixel lk

6. Repeat steps 1 through 5 for all pixels to obtain a Velocity Map in both the axial and radial directions

The original method proposed by Mosorov et al. [7] was aimed at estimating the axial component of the

velocity more accurately as this is the component of velocity that contributes directly to flow rate estimation.

Although Mosorov et al. [7] hinted at the possibility of obtaining the direction of the velocity, this was

not analyzed in any great detail. Moreover, any difference between the results of the normal image cross

correlation and the proposed method was attributed to changes in the flow pattern between plane 1 and 2, an

example being a bubble changing its shape, orientation or otherwise transforming itself as it passes through

the measurement volume.

But it is not hard to imagine a swirling flow which could also cause pixels to get correlated to their neighbours

without the bubble rotating or changing its shape in any way at all. Thus, applying the above described

method, a map of the movement of pixels can be obtained for the pipe cross section. Figure 7a shows the

direction of the velocity vectors between the two planes of measurement. Figure 7b shows the same data in

3 dimensional form clearly showing the swirling nature of the flow.

Figure 7 only show the direction of flow velocity and not the magnitude in the radial direction. A detailed

quantitative analysis follows in the next section.

14

(a) Velocity vectors (b) Velocity vectors in 3D

Figure 7: Swirl measurements from impedance cross correlation (SWRi test data)

4.2 Swirl characterization

The modified image cross correlation technique from the previous section results in a measure of the mag-

nitude and direction of the velocity vector at each pixel in the cross section. Additionally, this information

can also be tracked over time. Figure 8a shows the magnitude of the in-plane velocity and 8b shows the

deviation (in degrees) of the velocity vector from the vertical direction also called the swirl angle. The swirl

angle at a particular pixel i j is defined as

SwirlAnglei j = tan−1 |Vaxiali j ||V radiali j |

(7)

The reason why the two are almost identical is because in simple terms, what causes the deviation of the

velocity vector from the vertical direction is the magnitude of the in-plane velocity component. So, the

higher the in-plane velocity, the larger the angle of the velocity vector.

For an ideal vertical flow, both these graphs would read 0. So, in essence these two quantities characterize

the swirl in the flow. As is evident from figure 7, the information about swirl is multidimensional, and the

data needs to be reduced in a way that allows for comparison between different data sets. Swirl angle is a

15

(a) Magnitude of in-plane velocity (b) Angle of velocity vector with respect to vertical

Figure 8: Magnitude of in-plane velocity and angle of velocity vector

metric that is commonly used to quantify swirl. It is both easy to interpret and intuitive. With this particular

measurement method, the swirl angle is also very easily calculated as the average of the angle shown in

figure 8b. Apart from this, the average angle is an objective metric for two reasons. One is that it will always

be a positive number greater than or equal to 0, as is evident from its definition in equation 7. The second is

that the higher this number, the larger the deviation from an ideal flow (i.e flow confined only to the vertical

direction with no swirl).

Applying this metric on the SWRI test data, figure 9 shows the swirl angle as a function of GVF and gas

flow rate at measurement conditions. This data includes variations in flow rates along with the line pressure

varying between 10 bar and 100 bar.

(a) Swirl angle vs GVF (b) Swirl angle vs Gas flow rate (actual)

Figure 9: Swirl angle for various flow conditions

16

4.3 Error correction based on swirl measurement

Swirling flows are characterized by the presence of rotating velocity components perpendicular to the di-

rection of flow. In other words, there is a velocity component in the pipe cross-sectional plane along with

the velocity component along the pipe axis. Such velocity profiles could be a source of error in the flow

rate estimation models of different types of sensors. The discharge coefficient of the venturi, for example,

is based on a calibration with developed flows confined to the vertical direction. This relationship assumes

that the pressure measurements with gauge tapping at the cylinder wall can directly be related to the axial

flow since non-swirling flows do not have a pressure gradient in the radial direction. This section is aimed

at analyzing the effect of swirling motion on flow estimation, where an analytic model using the phase

fraction and velocity information measured from impedance sensors is proposed. This model estimates the

correction needed in the differential pressures (dP) to account for swirl.

As has been shown in the preceding sections, there both exist in-plane (cross-sectional) as well as axial

velocity components, possibly due to certain upstream conditions such as the blind tee. These give rise to an

in-plane pressure profile which needs to be accounted for when interpreting the pressure drop measurements.

(a) Fluid element schematic (b) In plane velocity vectors

Figure 10: In plane velocity profile for a swirling flow

Consider a spatial fluid element as shown in figure 10a. Starting from the Navies Stokes equation in cylin-

drical co-ordinates, for an incompressible (density ρ = constant), isothermal, inviscid flow and a velocity

vector−→V = (ur,uθ ,uz), in the radial direction we have:

17

ρ(∂ur

∂ t+ur

∂ur

∂ r+

uθ

r∂ur

∂θ−

u2θ

r+uz

∂ur

∂ z) =−∂P

∂ r(8)

It is not easy to find an exact solution for equation 8 to find the pressure distribution in the cross section. To

make the analysis easier, consider the special case of a steady state, axi-symmetric ( ur = 0, ∂

∂θ= 0 ) and

developed flow ( ∂

∂ z = 0), where equation 8 reduces to:

∂P∂ r

= ρu2

θ

r(9)

While some of these assumptions may not be valid for the conditions tested, the aim is to obtain an approx-

imate measure of the pressure distribution. Using equation 9, if the local density and in-plane velocities

are known, an approximation to the pressure profile in the plane perpendicular to the flow direction can be

derived. The local density can be easily obtained using the phase fraction distribution along with the fluid

densities. This information is already available to us as a result of the image reconstruction procedure men-

tioned in section 2.3. The pressure sensors for the differential pressure measurement are flush mounted to

the edge of the pipes and essentially capture the local pressure. In case of a non-swirling turbulent flow, this

is equal to the mean pressure across the cross-section. For swirling flows, as is evident from equation 9, a

radial pressure gradient exists and the pressure at the edges is higher than at the swirl center. The difference

between the pressure the centre and that closer to the wall for each pixel experiencing a sample swirling flow

is shown in figure 11.

Figure 11: In-plane pressure distribution for a swirling flow (SWRI test data)

18

Using this information, the correction required in the pressure measurement can be computed, at the location

that swirl is measured. For the SWRI experiments, using the in-plane pressure profile information in 11,

the pressure correction required as a function of GVF is shown in figure 12a. It is seen that the pressure

correction required increases with the swirl angle. A higher swirl angle suggests more in-plane velocity

contribution to the pressure measured and hence the greater the correction to the dP. The trend with GVF is

consistent with the trend of increasing swirl angle with GVF seen in 9a.

To estimate the effect on flow parameters, consider a hypothetical venturi (with beta=0.67) installed at the

location of the swirl measurement with dP measurements being made under the flow conditions tested.

Assuming that the swirl completely diffuses moving from the upstream of the venturi pressure tapping to its

downstream measurement location, the absolute pressure correction shown in figure 12a can be expressed

as a percentage of the pressure drop across the venturi. The plot as a function of GVF for the SWRI

experimental setup is shown in figure 12b. Since the expression used to determine the pressure distribution

is an approximation and not an exact solution, this gives us an order of magnitude for the error in mass flow

rate estimation when not accounting for the in-plane pressure gradients.

(a) DP correction vs GVF (b) DP error for a venturi measurement

Figure 12: Differential pressure correction based on swirl

19

5 Conclusions

There are many sources of errors that affect a multiphase flow measurement. The most insidious of these

errors are the ones buried in the assumptions either explicitly or implicitly built into the measurement. Flow

profile irregularities and asymmetries belong to this category. The problem may be exacerbated if such

effects are corrected for using data based calibrations. Such calibrations typically do not capture variations

in the flow characteristics with changes in say the flow regime or a fluid property such as the viscosity. Using

robust measurements from appropriate sensors and processing methods that can provide invaluable insights

about the flow, we have shown in this paper that the flow can be characterized in great detail including not

just the distribution of fractions and velocity but also the direction of the flow. The analysis presented in this

paper shows that the flow asymmetries and irregularities are an undeniable part of multiphase flow and it

would be prudent to acknowledge and account for them in flow rate estimation. While fraction and velocity

distribution information can be used in prediction and estimation models such as slip models, the direction

information can be used to correct for inaccuracies in sensor measurements such as the venturi differential

pressure. Such measurement based corrections are more robust and far more reliable than the empirical

corrections that are typically incorporated in flow estimation models to improve their accuracy. Future

work will involve analysis of flow profiles for a wider variety of operating conditions and incorporating this

information in multiphase flow measurement models to reduce the uncertainty in flow rate estimation.

20

References

[1] M. Abdulkadir, V. Hernandez-Perez, I. S. Lowndes S. Sharaf, and B. J. Azzopardi. “Exper-

imental investigation of phase distributions of two-phase air-silicone oil flow in a vertical

pipe”. In: World Academy of Science, Engineering and Technology 37 (Jan. 2010), p. 52.

[2] S. Al-Hinai. “Non-invasive velocity and volume fraction profile measurement in multiphase

flows”. Doctoral thesis. University of Huddersfield., 2010.

[3] J Gibson and M Reader-Harris. “Swirling flow through venturi tubes of convergent angle

10.5Âr and 21Âr”. In: ASME Joint U.S. - European Fluids Engineering Summer Meeting.

2006.

[4] A Gupta and R Kumar. “Three-dimensional turbulent swirling flow in a cylinder: Experi-

ments and computations”. In: Internation journal of heat and fluid flow 28 (2007), pp. 249–

261.

[5] S. Mahalingam, A. Banerjee, W. Basu, and H. K. Pillai. “Electrical network analysis of a

multiphase system”. US Patent 8121804. General Electric Company. 2012.

[6] S. Mahalingam, M. K. Koyithitta Meethal, A. Banerjee, W. Basu, and H.K. Pillai. “Electri-

cal network representation of a distributed system”. US Patent 8264246. General Electric

Company. 2012.

[7] V. Mosorov, D. Sankowski1, L. Mazurkiewicz, and T. Dyakowski1. “The "best-correlated

pixels" method for solid mass flow measurements using electrical capacitance tomography”.

In: Measurement Science and Technology 13 (2002), pp. 1810–1814.

[8] A. Ohnuki and H. Akimoto. “Experimental study on transition of flow pattern and phase

distribution in upward air water two-phase flow along a large vertical pipe”. In: International

journal of multiphase flow 26 (Mar. 2000), pp. 367–386.

[9] A Rammohan, A Dixit, V Natrajan, and M Kumar. “Detailed review of existing empirical

and analytical estimation models for multiphase flow”. In: 30 North Sea Flow Measurement

Workshop. 2012.

[10] T. K. Thiyagarajan, P. Satyamurthy, N. S. Dixit, A. Garg N. Venkatramani, and N. R.

Kanvinde. “Void fraction profile measurements in two-phase mercury-nitrogen flows using

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pp. 347–354.

21

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31st International North Sea Flow Measurement Workshop 22 – 25 October 2013

1

Uncertainty analysis of multiphase flow meters used for allocation measurements:

Field experiences and future challenges

Kjetil Folgerø, Christian Michelsen Research1 Eivind Lyng Soldal, Statoil2

Jan Kocbach, Christian Michelsen Research Kjell-Eivind Frøysa, Christian Michelsen Research

Kåre Kleppe, Statoil Eirik Åbro, Statoil

1 INTRODUCTION Multiphase flow meters (MPFMs) in combination with reference measurements in test separators are increasingly used for fiscal allocation purposes. This development is typically seen in fields which are developed as subsea production systems where unprocessed multiphase flows are transported to process platforms through pipelines. Statoil has experience from ownership allocation using multiphase meters from a number of different fields. CMR has performed uncertainty analyses for several Statoil operated fields; Alve, Morvin, Tyrihans, Visund Sør, Hyme and Skuld. When MPFMs are used for allocation purposes, the hydrocarbon mass output by the MPFMs is corrected using correction factors (also denoted K-factors) in order improve the accuracy. The correction factors are calculated by comparing MPFM measurements to reference measurements in a test separator (TSP) – usually during planned calibration campaigns. Correction factors are calculated either for total hydrocarbon mass or separately for oil/gas mass. The uncertainty in the corrected hydrocarbon mass is influenced by differences in the measurement systems and measurement philosophy. Factors influencing the uncertainty include the measurement instrumentation used in MPFMs and test separator, the representativeness of the reference measurements, and the approach used to calculate correction factors. Even though single phase and multiphase measurement instrumentation improves and becomes more reliable, failure and error measurement will still occur. It is therefore of great importance that calibrations of multiphase meters are traceable such that systematic errors can be corrected. As an example, the allocation metering system installed on Åsgard B in connection with tie-in of the Morvin field is shown in Figure 1. The ownership allocation between Morvin and Åsgard is based on multiphase metering of the Morvin flow line production, with two parallel topside multiphase meters installed at the Åsgard B platform. Subsea multiphase meters are installed for the 4 producing wells. The subsea multiphase meters are a part of the overall measurement system for Morvin, and are used both as back-up for the topside multiphase meters and for production optimization. Each topside multiphase meter can be directed to Åsgard B test separator in order to be individually calibrated. Correction factors are applied to the topside multiphase meters after each calibration. Calibrations are performed at regular intervals. The Åsgard B test separator was upgraded with traceable flow instruments prior to start-up of Morvin. Also, densitometers were installed at the oil and gas leg and water cut meters at the oil leg.

1 Christian Michelsen Research AS, 5892 Bergen, Norway (email: [email protected]) 2 Statoil ASA, 4035 Stavanger, Norway (email: [email protected])

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Stamp


2

Figure 1 Morvin measurement system combining multiphase flow meters with test separator measurements as reference measurement. The aim of the present paper is to present ongoing work and future challenges with regard to understanding and quantifying measurement uncertainties when utilizing multiphase metering systems for allocation measurements. This is done by exemplifying how differences in measurement system (e.g. measurement instrumentation and technology) may influence the uncertainty in hydrocarbon mass measured by the multiphase flow meters. In particular, the following issues are discussed:

Influence of uncertainty in PVT compositions on the overall uncertainty, Use of separate correction factors for oil and gas versus a single correction factor for

total hydrocarbon mass, Influence of flow rates and phase fractions, The representativeness of calibration measurements

The representativeness of the calibration measurements has significant impact on the uncertainty, and therefore special focus is put on methodology for estimating the uncertainty related to the representativeness of calibration measurements. 2 THEORY AND BACKGROUND A sketch of a typical measurement system for fiscal allocation using MPFMs in combination with reference measurements in a test separator is shown in Figure 2. The figure shows a case with two topside MPFMs and four subsea MPFMs, but the following discussion applies to the general case with arbitrary number of topside/subsea MPFMs. The topside multiphase flow meters (denoted MPFM 1 and MPFM 2 in Figure 2) are used for fiscal allocation purposes. In a typical setup these meters are calibrated periodically – one at a time - against the test separator. During ordinary operations the multiphase flow from the topside MPFMs is led directly to the production separator, whereas during calibration the flow from the MPFM under test is led to the test separator while the flow from other MPFM is lead to the production separator.


3

Gas lift in wells and riser base are measured by the topside MPFMs, and will influence the hydrocarbon mass uncertainty through changed uncertainty in the hydrocarbon composition.

Figure 2 Sketch of measurement system combining multiphase flow meter measurements with test separator measurements as reference measurement. Two different approaches can be used for correction of MPFM measurements by test separator measurements; (1) correction based on total accumulated hydrocarbon mass or (2) correction based on oil and gas mass separately. In the first approach calibration campaigns are made in which the total accumulated hydrocarbon mass measured by the MPFM under calibration ( ) is compared to the total accumulated hydrocarbon mass measured by the test separator ( ). A hydrocarbon mass correction factor for the MPFM under test is then calculated as

(1)

This correction factor ( ) is then applied to correct the MPFM measurements for the given MPFM until the next calibration campaign. The alternative approach is to use separate oil and gas correction factors where the oil mass measured by the MPFM is compared to the oil mass measured by the test separator giving a correction factor , and correspondingly a correction factor for gas mass. In fields operated by Statoil the production allocation and correction of MPFM measurements is typically based on hydrocarbon mass rate rather than separate oil and gas mass. Correction based on hydrocarbon mass rate is done in order to minimize the uncertainties in the allocations, as the hydrocarbon mass is independent of the different operating temperature and pressure (T&P) conditions of the topside multiphase meters and the test separator. Thus, there is no need to convert measured data to standard (or other equal conditions) before calculating the correction factor. As will be discussed later, the uncertainty associated with such convertion can be significant. The primary purpose of the subsea multiphase flow meters (denoted MPFM Subsea 1 to 4 in Figure 2) is production optimisation, but in addition the subsea meters function as back-up for the topside multiphase meters (Åbro 2009). While the topside meters are in normal operation, and the measured hydrocarbon mass is corrected against the test separator using the correction factors, the subsea meters are continuously compared to the topside meters. The total hydrocarbon mass rate measured by the topside meters is compared to the sum of the hydrocarbon mass rate measured by the individual subsea multiphase meters and the mass rate from the gas lift. By comparing continuous hydrocarbon mass and water rates measured by topside meters and subsea meters, including gas lift, any deviations between the topside measurements and


4

subsea measurements are revealed. To map the source of such deviations, more extensive testing by using the test separator may be required. The primary output parameters from the MPFM are component fractions and volume rates. Hydrocarbon, oil and gas masses are calculated by multiplying measured volumes with corresponding densities. Hence, the composition (PVT data) of the fluid must be known such that the densities at the operating conditions can be calculated. Any errors/uncertainties in the applied fluid composition can lead to large errors/uncertainties in measured and calculated rates. It is therefore of great importance to have accurate and updated PVT-data. The preferred approach by Statoil is to do all PVT calculations in a dedicated measurement computer, and update the MPFMs and test separator equipment with densities at their actual operating conditions. Statoil utilizes a multistage PVT model that describes the actual process. This ensures that the same PVT model is used for all calculations, such as subsea multiphase meters, topside multiphase meters, test separator and inlet separator. The densities and flow rates of oil and gas are measured at the output of the test separator during the calibration campaigns tests, and the composition can be updated iteratively by comparing these data with calculations from the PVT model. In addition, samples of the oil and gas phases are taken during the calibration campaigns tests and subsequently analyzed in a laboratory. Note that the MPFM also relies on knowing some fluid characteristics in order to calculate corrected volume rates and component fractions. Hence, frequent update of fluid compositions to the MPFM is still required even if component densities are calculated outside the meters. The description above summarises the present approach used by Statoil, and is based on practical experience from a large number of field implementations as well as theoretically based considerations. In the following chapters the influence the metering philosophy has on the uncertainty in measured quantities will be described in more detail, and example cases will be presented. Before this analysis is presented, a short summary of the methodology applied for uncertainty calculations is given in next chapter 3 UNCERTAINTY MODEL FOR HYDROCARBON MASS The uncertainty estimations made in the current work follow the method outlined in “Guide to the expression of uncertainties in measurements” (ISO-GUM, 2008). The approach used is described in Appendix A. The uncertainty of the measured hydrocarbon mass using the approach described in Chapter 2 can be divided into the following uncertainty sources:

Uncertainty in the primary output parameters from the MPFMs, e.g. gas volume rate, liquid volume rate and water liquid rate (WLR)

Uncertainty in the output parameters from the reference system (test separator), e.g. densities and volume flow rates

Uncertainty in fluid composition and uncertainties related to the PVT calculations The representativeness of the correction factors, based on e.g. change in process

parameters since last calibration. There are also other factors influencing the uncertainty indirectly, e.g. changes in flow rates, flow regime and temperature/pressure lead to a change in the uncertainty for the primary output parameters in both MPFMs and test separator. The total hydrocarbon mass is typically calculated using a scheme as described below and illustrated in Figure 3,

1. During ordinary operations, hydrocarbon mass is measured by each of the topside MPFMs.


5

2. Corrected hydrocarbon mass from each topside MPFM is calculated by multiplying the hydrocarbon mass from each topside MPFM with correction factors.

3. Total corrected hydrocarbon mass is calculated by adding the corrected hydrocarbon mass from each MPFM.

The hydrocarbon mass produced from the field can be calculated by subtracting gas lift (if present) from the total corrected hydrocarbon mass. In the following subsections the uncertainty related to each of these steps are discussed in more detail.

Figure 3 Schematic showing the approach for calculation of total hydrocarbon mass. 3.1 Hydrocarbon mass measured by one MPFM The uncertainty in the uncorrected hydrocarbon mass measured by a topside MPFM is dependent on the measurement technology used in the MPFM. Typically MPFM manufacturers state measurement uncertainties for liquid volume rate, gas volume rate and water-liquid ratio ( ) – although some MPFM manufacturers use other parameters. For the example case of liquid/gas volume rate and WLR as primary parameters, the hydrocarbon mass flow rate ( ) can be calculated as

1 WLR , (2) where is the hydrocarbon mass flow rate, is the oil mass flow rate, is the gas mass flow rate, is gas volume mass flow rate, is the liquid volume flow rate, is the water-liquid ratio, is the oil density and is the gas density. This leads to the following uncertainty model,

∙

∙ ∙WLRWLR

.

(3)

where is the water mass flow rate and is the water density. If water volume fraction ( ) is used for calculation of the hydrocarbon mass instead of WLR, the uncertainty model has to be modified. This will not be discussed here. Note that some multiphase meter vendors also report hydrocarbon mass and accompanying measurement uncertainties for hydrocarbon mass. Here the hydrocarbon mass is calculated in the MPFM based on oil and gas densities as input to the MPFM.


6

3.2 Hydrocarbon mass measured by one MPFM and corrected using test separator measurements

The correction factors are calculated based on planned calibration tests where MPFM measurements are compared to corresponding hydrocarbon mass measurements from a reference system, typically a test separator (see Figure 4 for illustration). For the example case of a common hydrocarbon mass correction factor KHC, the correction-factor is given in equation (1). The calibration typically takes from 12 to 24 hours, and in this period the PVT composition is iteratively updated based on test separator measurements. Typically oil/gas densities and/or PVT composition in the MPFM are also updated iteratively during the calibration cycle. Generally the corrected hydrocarbon mass rate , can be expressed by

, (4)

leading to the following uncertainty model

,

,

1

1

1

, , ,

, ,

,

,

2 ,

, ,

,

2 ,

,

,

,

(5)

where , and are the hydrocarbon mass flow rate, oil mass flow rate and gas flow mass rate measured by the test separator, respectively. Further on, , , , and , are the hydrocarbon mass flow rate, oil mass flow rate and gas mass flow rate measured by the MPFM during the calibration and , and are corresponding mass rates measured at ordinary operations. It is notable that there is a correlation between the hydrocarbon mass measured by the MPFM during ordinary operations and during the calibration which must be taken into account in the uncertainty model. This is done through correlation coefficients as introduced in equation (20) in Appendix A – one correlation coefficient for oil mass flow rate ( ) and a corresponding correlation coefficient for gas mass flow rate ( ). This correlation influences strongly on the uncertainty – this will be exemplified further below in Chapter 4. For the case with separate correction factors for oil and gas, the corrected hydrocarbon mass is

, (6)

The uncertainty model for this case has many similarities with the uncertainty model for the case with common hydrocarbon mass correction factor, but an extra uncertainty contribution must be included due to the need for converting the oil and gas masses from test separator


7

and MPFM to the same pressure/temperature. The detailed calculations for this uncertainty model are not included here.

Figure 4 Schematic showing how the correction factor is calculated during the correction process and is later used under normal operations. 3.3 Calculation of total corrected hydrocarbon mass The total corrected hydrocarbon mass for N MPFMs in parallel is calculated by adding the hydrocarbon mass from each MPFM (see Figure 3), giving

, . (7)

In the uncertainty model this gives correlation terms due to the correlation between the test separator measurements for each MPFM and due to the densities used in the different MPFMs. 3.4 Calculation of hydrocarbon mass produced from field The actual hydrocarbon mass produced from the field is also a quantity of interest. This quantity is calculated by subtracting the gas introduced by the gas lift from the total corrected hydrocarbon mass (see Figure 3),

, , , ,

(8)

This gives the following uncertainty model,

, ,

, ,

,

, ,

,

,

,

, ,

,

,

(9)


8

4 APPLICATION EXAMPLES In the following the developed uncertainty model is applied to some example cases in order to illustrate how different parts of the measurement system influence the uncertainty. 4.1 Example 1: Correction factor for hydrocarbon mass versus separate oil/gas

correction factors As discussed in Chapter 2, the correction factors can either be calculated as separate gas- and oil correction-factors or as a common hydrocarbon factor. One advantage of using two separate factors for oil and gas is that any proportional systematic errors in the primary output of the MPFM ( , , , or ) will be cancelled out in the calculated hydrocarbon mass when separate K-factors for oil and gas are used. This is not the case when using a common hydrocarbon K-factor. The main drawback of using two K-factors is that the measurements from the MPFM and the test separator must be converted to the same pressure/temperature conditions All PVT calculations have uncertainties, which depend on different process conditions (e.g. pressures, temperatures, fluids). According to Statoil’s PVT tool supplier the general uncertainty of PVT calculation has been estimated to 3% for oil and gas densities. This is here interpreted to be relative expanded uncertainty. Thus, this additional uncertainty must be included in the overall uncertainty analysis. This conversion is not needed if a correction factor for hydrocarbon mass is used, as the hydrocarbon mass is conserved at different conditions. PVT calculation uncertainties in oil and gas densities must be included as part of the calculations of the hydrocarbon mass measured by the MPFM as the densities are input parameters used when calculating the hydrocarbon mass from the primary output parameters. It should be noted that there is a need for more accurate knowledge about the uncertainty associated with the PVT calculations. It is for example very unlikely that the relative expanded uncertainties in oil and gas densities are as large as 3 % if changes in temperature or pressure are small. Hence, more work should be done on mapping the uncertainty of the PVT calculation. An example illustrating how the additional PVT calculation uncertainty affects the combined uncertainty is shown in Figure 5, which is taken from an uncertainty analysis for a Statoil operated field. In the case considered here the measurements from the test separator are converted to MPFM T&P conditions before the oil and gas correction factors are calculated. In Figure 5 the relative expanded uncertainty for oil-, gas- and hydrocarbon mass rate measured by the test separator are shown at test separator T&P conditions (upper three uncertainty bars) and converted to MPFM T&P conditions (next three uncertainty bars). It may be noted that the conversion from TSP T&P conditions to MPFM T&P conditions gives a significant contribution in the relative expanded uncertainty for gas and oil mass rate, whereas the relative expanded uncertainty for hydrocarbon mass rate is not influenced. The uncertainty budget for the corrected hydrocarbon mass when using separate oil/gas correction factors and a common hydrocarbon correction factor is shown in Figure 6 using the data from Figure 5 as input. For the case where separate correction factors for oil and gas mass rates are used, test separator oil and gas mass rates must be converted separately to MPFM T&P conditions, leading to a large uncertainty contribution (blue uncertainty bars). For the case where a common correction factor is used, the relative expanded uncertainty in hydrocarbon mass rate is invariant to T&P conversion, leading to a significantly smaller uncertainty contribution (red uncertainty bars). The relative expanded uncertainty (95 % confidence interval) is estimated to 3.6 % when using separate correction factors and 2.8 % when using a common correction factor. It may also be noted that if densitometers are not present at the test-separator’s oil and gas-legs, the oil and gas densities must be calculated from PVT-data, and thus the 3% uncertainty


9

in density will give a large influence on the calculated hydrocarbon mass from the test separator. This example thus also illustrates the importance of having densitometers at the test separator.

Figure 5 Relative expanded uncertainty for TSP oil, gas and hydrocarbon mass rates at different operating conditions – including PVT contribution due to P&T conversion.

Figure 6 Example of the contributions to the relative expanded uncertainty in corrected hydrocarbon mass comparing the use of separate oil and gas correction factors (blue bars) and common hydrocarbon mass correction factor (red bars). The relative expanded uncertainty in corrected hydrocarbon mass is divided into contributions from TSP (based on data from Figure 5) and MPFM. 4.2 Example 2: Representativeness of calibration Calibrations are usually performed at regular intervals, In addition calibrations are initiated if there are significant changes in operating conditions. As illustrated in the following example, it is important to keep the operating conditions and process parameters during calibration as close to the normal operating conditions as possible as this will reduce the uncertainty in corrected hydrocarbon mass. Examples of process parameters that may change with time are temperature, pressure, fluid composition, densities, flow rates and flow regimes.


10

Shortly after a calibration, the correlation between MPFM measurements under normal operations and MPFM measurements during the recent calibration will be very high, and the uncertainty of the corrected data will be close to the uncertainty of the test separator measurements. As time goes, this correlation will be reduced due to changes in the process parameters and due to changes (e.g. drift) in the measurement instrumentation. Thus, the uncertainty of the measurements will increase as the representativeness of the calibration is reduced with the changes in process parameters. This is illustrated in Figure 7. According to ISO GUM (2008) this representativeness can be described by a correlation coefficient (see Appendix A). This coefficient is equal to one when there is a full correlation between measurements under calibration and normal operation, and is equal to zero if there is no correlation between the measurements. Figure 8 compares the relative expanded uncertainty in the corrected hydrocarbon mass measured with one MPFM (solid blue line) with the corresponding uncertainty in the uncorrected hydrocarbon mass for one MPFM (dashed blue line) as a function of the correlation coefficient. Corresponding lines are also shown for two MPFMs in parallel (red lines). In this example separate correction factors for oil and gas are used. It may be observed that the influence of the correlation coefficient is significant. The uncertainty of the corrected hydrocarbon mass is equal to the uncertainty of the test separator for fully correlated measurements, whereas the uncertainty of the corrected hydrocarbon mass is larger than the uncertainty for the uncorrected hydrocarbon mass for the extreme case of fully uncorrelated measurements. Significant amounts of statistical data are needed in order to quantify the actual value of the correlation coefficient accurately. The approach suggested and applied in this work is to do an analysis of data available from subsequent calibrations. As described above, any process variations between two calibration measurements will be accounted for by a change in correction factors K (assuming that the drift and uncertainty in the MPFM between calibrations are small). For the time interval between two calibrations, both the pre-interval and post-interval correction values should give adequate correction. Thus, the changes in subsequent K-factors will be a measure of the representativeness of the calibrations for the given time interval. The relative variation in accumulated hydrocarbon mass between two calibrations calculated with subsequent K-factors for a sample case from a Statoil operated field is shown in Figure 9. The variation is calculated as

Δ , , , , , , , ,

, , , , (10)

For the case shown in Figure 9 it is observed that the variations using subsequent K-factors change with time, but are typically within 3 – 3.5 %. Comparing this with the uncertainty analysis in Figure 8 this corresponds to a correlation coefficient of 0.8-0.9. At day 171 it is observed that the deviation is above 6 % for MPFM1, corresponding to a correlation coefficient below 0.3. A closer examination of the process data for this period shows that there are significant changes in process parameters: A new well was put in production in this period, and there had been a change in hydrocarbon mass flow rate of approximately 70 % between the two calibrations of MPFM1. Thus, this illustrates that the uncertainty associated with the present correction method is very dependent on the stability of the process. It is therefore recommended to perform new calibration measurements and update the correction factors when significant changes in the process occur. For the case using common hydrocarbon correction factors, the variation is calculated directly as

Δ , , , ,

, ,

, ,

, (11)


11

The capacity of a test separator is limited, and may be lower than the actual operating flow rate through the multiphase flow meter to be calibrated. In order to operate the test separator within its operation range, it is tempting to reduce the flow through the multiphase meter during calibration. However, this will reduce the representativeness of the calibration (i.e. the correlation coefficient) significantly, and thereby increase the uncertainty of the measured hydrocarbon mass. Note that in this case the method outlined above for estimating the correlation coefficient is not applicable due to the lack of adequate reference data. With limited test separator capacity it is recommended to install two multiphase meters in parallel, where each multiphase meter can be calibrated separately at full operating range. If this is not possible, it is worth considering operating the test separator somewhat outside its operating range. This will reduce the separation process and increase the uncertainty of the test separator measurements. However, the MPFM measurements during calibration will be representative for the MPFM measurements during normal operation. By including water-in-oil monitoring equipment on the oil leg, the uncertainty of the oil and gas mass measurements out of the test separator may also be reduced.

Figure 7 Illustration of how the uncertainty in corrected hydrocarbon mass may vary with time.

Figure 8 Relative expanded uncertainty (95 % confidence level) in corrected hydrocarbon mass as a function of correlation coefficient.

0 0.2 0.4 0.6 0.8 10

1

2

3

4

5

6

7

8

Correlation coefficient

Rel

ativ

e ex

pand

ed u

ncer

tain

ty (%

)

One MPFM, correctedOne MPFM, uncorrectedTwo MPFMs, correctedTwo MPFMs, uncorrected


12

Figure 9 Relative variation in hydrocarbon mass using two subsequent correction coefficients 4.3 Example 3: Flow and process conditions In this example some aspects related to how flow and process conditions influence the measurement uncertainty are discussed, and it is illustrated how the main contributions to the total uncertainty can be identified. The example is based on data for a Statoil operated field where estimated flow rates for a span of 17 years is used as input. Figure 10 shows the mass rates over the lifetime of the field, and Figure 11 shows the GVF and WLR. The field has a production profile which is typical for many oil fields in which the majority of the hydrocarbon mass is produced the first 5-6 years (see Figure 10). Produced water is increasing with time, with the WLR rising steeply to 80% after 5 years, and from there slowly increasing further to around 95% in the last years of operation. GVF is above 70 % over the lifetime of the field. Based on the uncertainty model given in Chapter 3, the relative expanded uncertainties (95% confidence interval) for corrected and uncorrected hydrocarbon mass are calculated and shown in Figure 12. The calculations are based on the MPFM specifications given in Table 1. The uncertainty curves for both corrected and uncorrected hydrocarbon mass show some interesting characteristics,

A local maximum at year 6 A steady increase in relative expanded uncertainty for hydrocarbon mass towards the

end of the operating time of the field In the following these characteristics will be explained by investigating the contributions from different parts of the measurement system.

Table 1 Specifications for the MPFM analysed in example 3

Uncertainties (95% conf. int.) Liquid Volume Flow 2.5 % for GVF< 80%

5 % for GVF>80% Gas Volume Flow 5 % Water Liquid Ratio 2 % absolute for WLR< 85 %

1 % absolute for WLR>85% Densities (oil and gas) 3 %

0 100 200 300 400 500 600 7000

1

2

3

4

5

6

7

Day no.

Var

iatio

n in

HC

-mas

s [%

]

MFM 1MFM 2


13

Figure 10 Mass rates for the Statoil-operated field considered in example 3.

Figure 11 GVF and WLR for the Statoil-operated field considered in example 3.


14

Figure 12 Relative expanded uncertainty (95 % confidence level) in corrected and uncorrected hydrocarbon mass for the Statoil-operated field considered in example 3. Break-down of uncertainty for uncorrected hydrocarbon mass The relative expanded uncertainty in uncorrected hydrocarbon mass is given in Equation (3) as a sum of contributions which can be related to uncertainty contributions from WLR, gas density, oil density, gas volume rate and liquid volume rate. Thus, equation (3) can be written as

u

u u u u

(12)

where the uncertainty contributions are:

Gas density contribution: u∙ (13)

Oil density contribution: u∙ (14)

Liquid rate contribution: u (15)

Gas rate contribution: u∙ (16)

WLR contribution: u∙

WLRWLR

. (17)

In Figure 13 the relative expanded uncertainty in uncorrected hydrocarbon mass (red curve from Figure 12) is compared to each of the contributions. From the comparison it can be observed that there are two main contributions to the uncertainty in uncorrected hydrocarbon mass; the uncertainty related to liquid volume rate in the first 3 years until the shift downwards in year 4, and the uncertainty related to WLR in the remainder of the operating time of the field. The reduction in contribution from the liquid volume rate in year 4 is due to the GVF being reduced below 80 %, in which case the relative expanded uncertainty in liquid volume


15

rate measured by the MPFM is reduced by a factor 2 according to the MPFM specifications (see Table 1). Break-down of uncertainty related to WLR The uncertainty contribution related to the WLR is further investigated in Figure 14. Here the contribution in uncertainty related to the WLR given in equation (17) is broken down into the relative expanded uncertainty in WLR (from the MPFM specifications) and the relative sensitivity coefficient3, From Figure 14 it can be observed that the downshift in relative expanded uncertainty in uncorrected hydrocarbon mass is caused by a shift in the uncertainty in the WLR (from the MPFM specifications) occurring between year 6 and year 7. This shift is due to the fact that the WLR increases above 85 % in year 7, for which the uncertainty in WLR is reduced by a factor 2 according to the MPFM specifications (see Table 1). Figure 14 shows that the steady increase in relative expanded uncertainty in uncorrected hydrocarbon mass is due to a steady increase in the sensitivity coefficient. By analyzing the sensitivity coefficient in more detail (see equation (17)), it can be observed that the hydrocarbon mass is the main contributor to the steady increase in sensitivity coefficient. This is evident from Figure 15 in which the inverse of the hydrocarbon mass is compared to the relative sensitivity coefficient. Absolute versus relative uncertainty In Figure 16 the relative expanded uncertainty for corrected hydrocarbon mass (blue curve from Figure 12) is compared to the corresponding absolute expanded uncertainty curve. Note how the absolute measurement uncertainty is constant during the last production years even though there is a steady increase in the relative expanded uncertainty. The reason for this increase in relative uncertainty is that the hydrocarbon mass decreases with time. This confirms the observation from Stockton and Wilson (2012) that a high relative uncertainty may be a small absolute quantity in a multiphase flow metering station.

Figure 13 Relative expanded uncertainty (95 % confidence level) in uncorrected hydrocarbon mass compared to the uncertainty contributions. For the Statoil-operated field considered in example 3.

3 See Appendix A for definition of sensitivity coefficient.


16

Figure 14 Relative expanded uncertainty contribution (95 % confidence level) from WLR compared to sensitivity coefficient for WLR and relative expanded uncertainty for WLR calculated from the MPFM specifications (see equation (17)). For the Statoil-operated field considered in example 3.

Figure 15 Relative sensitivity coefficient for WLR compared to the inverse of the hydrocarbon mass. For the Statoil-operated field considered in example 3.


17

Figure 16 Expanded relative and absolute uncertainty in produced hydrocarbon mass during the lifetime of a field. For the Statoil-operated field considered in example 3. 5 CONCLUSIONS AND FUTURE CHALLENGES In this paper a measurement approach has been described in which the MPFM measurements are corrected by calibration towards test separator, and a methodology for analyzing the uncertainty in the calculated hydrocarbon mass has been outlined. Different issues affecting the uncertainty in hydrocarbon mass have been discussed, and in particular it was found that the following factors affected the uncertainty strongly:

Representativeness of reference measurements Uncertainty in fluid densities due to PVT calculation Production profiles

To ensure a high representativeness of the calibration measurements it is important to calibrate at actual flow rates and operating conditions. A new calibration should be performed if the process conditions changes significantly. In this paper we have suggested a method for estimating the representativeness (i.e. the correlation coefficient) through comparing the corrected hydrocarbon mass using adjacent correction factors. The added uncertainties of the fluid densities when converted to other temperature and pressure conditions are significant. The studied example illustrates that the use of a common correction factor for hydrocarbon mass is preferable. This conclusion is based on the assumption that the uncertainties in PVT conversions are 3 %. More knowledge on the uncertainty associated with the PVT conversion process is needed in order to give more accurate estimates of the uncertainty in hydrocarbon mass. Even though single phase and multiphase measurement instrumentation improves and becomes more reliable, failure and error measurement will still occur. It is therefore of great importance that calibrations of multiphase meters are traceable such that systematic errors can be corrected. Trending and analysis of measured and derived data (e.g. correction factors) gives a lot of information about the quality and performance of the measurement system, but it is also challenging to analyse all this information due to multiphase measurements at one process condition compared to single phase measurements at different


18

process conditions. It is therefore of importance to plan for and implement adequate methods and tools for this purpose. The specifications of a MPFM are typically given for primary output parameters such as liquid volume flow, gas volume flow and water-liquid ratio, whereas the primary measurements are done by techniques such as electrical impedance, microwave transmission, gamma densitometry and differential pressure (Thorn et al., 2013). In order to calculate the correct output parameters, it is important to know the physical properties that affect the primary measurements of the particular MPFM (e.g. permittivity, salinity, density and linear attenuation coefficients) as accurately as possible. These physical properties are dependent on the fluid composition, temperature and pressure, and an uncertainty in composition will therefore affect the uncertainty in the output parameters. The influence of a composition uncertainty will affect the various measurement technologies differently. In order to reduce the sensitivity for input parameters, some multiphase meter vendors perform in-situ measurements of fluid parameters such as water salinity and fluid densities . These new features integrated in the multiphase meters are important to reduce measurement uncertainties. More work should be performed to examine how these features can be integrated in the overall measurement system. REFERENCES ISO/IEC, «Uncertainty of measurement - part 3: Guide to the expression of uncertainty in measurement,» ISO/IEC, Geneva, 2008.

Thorn, R., G. A. Johansen, and B. T. Hjertaker, “Three-phase flow measurement in the petroleum industry,” Meas. Sci. Technol., vol. 24, no. 1, p. 012003, Jan. 2013.

Stockton, P and A. Wilson, “Allocation uncertainty: Tips, tricks and pitfalls”, 30th International North Sea Flow Measurement Workshop, October 2012, St. Andrews, UK

Åbro, E., K. Kleppe and L. J. Vikshåland “Recent field experiences using multiphase meters for fiscal allocation” 27th International North Sea Flow Measurement Workshop, October 2009, Tønsberg, Norway


19

APPENDIX A Calculation of uncertainties The uncertainty estimations are following the method outlined in “Guide to the expression of uncertainties in measurements” (ISO-GUM, 2008). The definition of some basic terms from the guide is given in Table 2. For a more detailed discussion of uncertainty calculations, the reader is referred to ISO-GUM (2008)

Table 2 Definition of terms related to uncertainty calculations (ISO-GUM, 2008).

Factor Comment Measurand Y Particular quantity subject to measurement Standard uncertainty Uncertainty of the result of a measurement expressed as a standard

deviation Combined standard uncertainty

Standard uncertainty of the result of a measurement when that result is obtained from the values of a number of other quantities. See equations (18) and(21).

Expanded uncertainty Quantity defining an interval about the result of a measurement that may be expected to encompass a large fraction of the distribution of values that could reasonably be attributed to the measurand. See equation (22).

Coverage factor Numerical factor used as a multiplier of the combined standard uncertainty in order to obtain an expanded uncertainty. See equation (22).

Sensitiviy coefficient Quantity describing how the output estimate varies with changes in the values of an input estimate . See equation (19).

The uncertainty in the estimated output quantity is calculated by the following 4 steps: 1. Modelling the measurement

Identify the input quantities that may influence the measurand. The uncertainty contribution for a given input quantity typically depends on the operating conditions, and for completeness even input quantities that may have insignificant contribution on the output quantity should also be included in the uncertainty budget. In this manner, the uncertainty budget is easily recalculated for other operating conditions where the input quantity may have a more significant contribution.

Determine the functional relationship relating the measurand and the input quantities, i.e. , , … , . The functional relationship should be interpreted in a broad context as a function that contains every quantity that contributes to the measurement uncertainty. This function may be very complex and, and may have to be determined experimentally or numerically.

2. Determine the standard uncertainty of input quantities The uncertainty is evaluated by statistical analysis (Type A) or by scientific judgment

based on available information on the possible variability of the input quantity (Type B) . Examples of information that can be used in Type B evaluation are manufacturer’s specifications, previous measurement data, calibration certificates and relevant experience.

The input quantities may themselves be considered as measurands of other input quantities, and estimated using the ISO-GUM method.

The uncertainties given in e.g. datasheets or found elsewhere are typically expanded uncertainties, and the standard uncertainty must therefore be calculated by dividing the expanded uncertainty by the coverage factor . The coverage factor depends on the probability distribution and level of confidence given in the datasheet (a typical 95 % normal distribution corresponds to a coverage factor 2).

3. Determine combined standard uncertainty For the general case of correlated input quantities, the combined standard uncertainty

is


20

2 , (18)

The sensitivity coefficient is defined as

(19)

The correlation coefficient is defined as

,,

(20)

where , is the estimated covariance associated with and .

For uncorrelated input quantities, the combined standard uncertainty is simplified to

(21)

4. Determine the expanded uncertainty.

The expanded uncertainty is obtained by multiplying the combined standard uncertainty, , by the coverage factor,

(22)

The coverage factor is chosen on the basis of the level of confidence required, and in

general k is in the range 2 to 3. When the probability distribution of y is approximately normal, a coverage factor =2 produces a level of confidence of approximately 95 %.

fltough

Stamp

1

Uncertainty Analysis Based on Historical DataUncertainty Analysis Based on Historical DataUncertainty Analysis Based on Historical DataUncertainty Analysis Based on Historical Data

Calum Hardie, NELCalum Hardie, NELCalum Hardie, NELCalum Hardie, NEL

1 INTRODUCTION Uncertainty analyses are essential to determine whether measurement systems are capable of meeting performance targets. They are also used to help develop maintenance and calibration schedules. When developing uncertainty budgets for new measurement systems however it is difficult to obtain reliable data to provide evidence. In many cases manufacturer’s estimates of uncertainty are used along with engineering judgement. These techniques are permitted in guidance documents such as the Guide to the expression of Uncertainty of measurement (GUM)1 and ISO 5168: 20033. To improve the analysis over time these uncertainty budgets should be updated with real data from calibrations and verifications of the measurement system. This data can also be used to improve maintenance and calibration schedules. This paper will discuss the importance of regarding uncertainty analysis as an iterative process and will show how historical data can be used to improve our understanding of meter performance through the use of uncertainty budgets as more data becomes available. An example of how historical data can improve uncertainty budgets will be shown using data from calibrations of secondary reference turbine meters at NEL. 2 UNCERTAINTY ANALYSIS BACKGROUND 2.1 Analytical Method The analytical method of calculating uncertainty is described in detail in the GUM1. The technique involves a series of steps outlined below.

1. Define the relationship between all the inputs to the measurement and the final result. 2. For each input, draw up a list of all the factors that contribute to the uncertainty in that

input. 3. For each of the uncertainty sources make an estimate of the magnitude of the

uncertainty. 4. Convert the uncertainties to standard uncertainties by assigning a probability

distribution to each uncertainty source. 5. From the relationship defined in step 1, estimate the effect that each input has on the

measured result. This is usually achieved by calculating sensitivity coefficients. 6. Combine all the input uncertainties using the root sum squared technique to obtain the

overall uncertainty in the final result. Note: If correlations exist then the inputs are combined in a different manner (see section 7).

7. Express the overall uncertainty as the interval about the measured value, within which the true value is expected to lie with the required level of confidence.

fltough

Stamp

2

The uncertainty budgets created using the analytical method are very useful tools for optimising measurement systems as the effect of changes in input uncertainties on the output uncertainty can be seen very quickly. The input uncertainty sources can be ranked to determine which sources have the most significant effect on the overall uncertainty. The process of developing uncertainty budgets can also be beneficial in that it helps to gain a full understanding of how the measurement system works.

2.2 Monte Carlo Method The Monte Carlo method is an alternative method of estimating measurement uncertainties. It is described in detail in supplement 1 to the GUM2. The method involves a series of steps outlined below.

1. Define the relationship between all the inputs to the measurement and the final result. 2. For each input, draw up a list of all the factors that contribute to the uncertainty in that

input. 3. For each of the uncertainty sources make an estimate of the magnitude of the

uncertainty. 4. Assign a probability distribution to each of the uncertainty sources. 5. Use a random number generator to assign a “measured value” for each input variable

based on its uncertainty value and probability distribution. 6. Calculate the final result using the “measured values” as inputs.

This process is repeated tens of thousands or hundreds of thousands of times until there is enough data to analyse the output distribution. The uncertainty in the final result can then be estimated by calculating the standard deviation of the output data. Monte Carlo has some advantages for example it shows the distribution in the output which can be used to view whether the distribution is skewed or rectangular in shape. The Monte Carlo technique is particularly useful when the uncertainties are large compared with the measured values3 which is not the case for the example in this paper. It has previously been shown that agreement between the Monte Carlo and Analytical methods can be good as long as they are carried out correctly4.

3

3 UNCERTAINTY OF NEL TURBINE REFERENCE METERS As a way of demonstrating the benefits of using historical data in uncertainty analyses the secondary reference turbine meters for the NEL water flow calibration facility have been analysed. The water reference meters consist of two 8” turbine meters known as M2 and M3. The meters are installed in parallel with a flowrange of 30-300 l/s. At NEL the meters are used up to a maximum flowrate of 200 l/s and when used in parallel can measure up to the maximum flow of the facility which is 400 l/s. There are also low flow reference meters but they have not been analysed for this paper. The meters are calibrated using the primary reference gravimetric weighbridges in the NEL water facilities. The meters are calibrated regularly and data is available back until 2004.

Figure 1: NEL water flow facility

Weightanks

Low

Flow

Lines High

Flow

Lines

Ref

Meters

in trench

4

Figure 2: NEL water flow facility reference turbine meters

The aim was to determine the uncertainty in the volume which passes the reference meters during a calibration. In order to do this the first task is to list all the sources of uncertainty which contribute to the overall uncertainty in volume. The uncertainty sources identified for consideration in this analysis are described below. Calibration: For each flowmeter the uncertainty in its calibration will contribute to the uncertainty in its use. The calibration uncertainty here is defined as the uncertainty in the reference measurements which in this case is the gravimetric weightanks used as the primary reference in the NEL water flow facility. Curve fit: The uncertainty in curve fit is defined here as the difference between the estimated k-factor and the actual k-factor. If the k-factor is assumed to be linear or a constant value then this uncertainty source could be described as linearity. In this case however a curve fit is applied to the turbine meter k-factors to try and minimise this uncertainty source. Drift: When flowmeters are calibrated periodically there will be an uncertainty source caused by k-factor drift between calibrations. Turbine meter k-factors can drift for many reasons including wear on the turbine blades or changes in bearing friction. This uncertainty source will reduce if calibrations are carried out more frequently. Resolution: All measurement instruments will have an uncertainty caused by resolution. In the case of these turbine meters they have a pulsed output where a pulse is generated each time a turbine blade passes the magnetic pickup. The resolution uncertainty is therefore simply the resolution of one pulse. Temperature/Viscosity effects: It has been shown previously that the performance of turbine meters are affected by changes in fluid viscosity6. This is due to an increase in the viscous shear force on the rotor which causes increased viscous drag within the bearing. Changes in viscosity can also lead to an increase in boundary layer thickness which causes non-linearity. Temperature can also affect the turbine meter performance due to changes in dimension of the meter and thermal expansion and contraction of the fluid within the meter. In this case the effects of temperature and viscosity have been combined into one uncertainty source. This uncertainty accounts for changes in temperature between calibration and use of the meter along with the stability of temperature during use.

5

These uncertainties will be in units of volume, k-factor or pulses. Using the analytical method they are all converted to units of volume using sensitivity coefficients before being combined using the root sum squared technique. Meters M2 and M3 are installed in parallel and when used together covariances or correlations will exist between some of the uncertainty sources since they are calibrated against the same reference and they have identical designs. Where correlation exists the sources are combined with straight addition rather than the root sum square technique.

6

4 UNCERTAINTY ANALYSIS WITH NO HISTORICAL DATA An uncertainty analysis was first completed assuming that no historical data was available. This is a common occurrence for example where the measurement system is newly installed or if the historical data has not been well documented or is missing. If no historical data is available then estimates of the magnitude of uncertainty sources has to be made using manufacturers specifications, engineering judgement or based on data from similar measurement systems. Figure 2 shows the uncertainty budget for meter M2 where no historical data is available. With no historical data available the uncertainty budget will be identical for meter M3.

Table 1: M2 uncertainty budget with no historical data Rank Uncertainty Source Units Value Expanded Relative Divisor Standard Sensitivity Output Uncertainty

Uncertainty Uncertainty Uncertainty Coefficient Uncertainty Squared

U U* (%) u c u.c (u.c)2

2 Calibration m39833 7.8663 0.080 2.00 3.9331 1 3.93E+00 1.55E+01

1 Curve fit/linearity P/m31.017 0.0015 0.150 2.00 0.0008 -9668 -7.37E+00 5.44E+01

3 Drift P/m31.017 0.0004 0.035 1.73 0.0002 -9668 -1.99E+00 3.96E+00

5 Resolution Pulses 10000 1.0000 0.010 1.73 0.5774 0.0001 5.77E-05 3.33E-094 Temp/viscosity effect P/m3 1.017 0.0001 0.005 2.00 0.0000 -9668 -2.46E-01 6.04E-02

Overall Uncertainty m39833 17.190 0.175 2.00 8.595 1 8.595 73.873

The estimated magnitudes of the uncertainty sources for meter M2 are described in sections 4.1 – 4.5.

4.1 Calibration The calibration uncertainty is taken here as the uncertainty of the NEL water flow facility primary reference which is 0.08%. This information should always be available even if the meter is new because there should be details of an initial calibration or a factory acceptance test from the manufacturer which will include an uncertainty figure. 4.2 Curve Fit If no historical data is available then it is difficult to obtain a figure of uncertainty for curve fit. The manufacturer’s data sheet in this case quotes a figure of 0.15% for accuracy. This is technically an incorrect statement because accuracy is a qualitative term and therefore should not be assigned a value. It is not clear how the manufacturer defines accuracy in this case but it is assumed to be defined as the difference between actual and estimated k-factor. Therefore the figure of 0.15% is assumed to be the curve fit uncertainty. 4.3 Drift It is not possible to obtain an uncertainty figure for drift if no historical data is available. It therefore has to be estimated from engineering judgement or from experience of similar instruments. In this case the value of 0.035% is taken from the uncertainty caused by drift in the NEL oil flow facility reference turbine meters.

7

4.4 Resolution The resolution uncertainty is simply the resolution of one pulse. If it is assumed that 10000 pulses are taken then the uncertainty is 0.01%. This source of uncertainty should be insignificant unless a smaller number of pulses are taken. It is generally recommended that at least 10000 pulses are collected unless pulse interpolation is being used.8 4.5 Temperature/Viscosity Effects It is difficult to obtain a value for uncertainty caused by temperature or viscosity effects unless calibrations have been carried out at different temperatures and viscosities. In this case no figures were available from the manufacturer on temperature/viscosity effects. Published data7 on a 6” turbine meter in water was available however and the meter was found to have a variation in k-factor of around 0.005% per °C. With a lack of additional information the uncertainty due to temperature/viscosity effects was assumed to be 0.005% multiplied by the difference in temperature between calibration and use of the meter plus the

temperature stability in use.

5 UNCERTAINTY ANALYSIS WITH HISTORICAL DATA An uncertainty analysis was then carried out for meters M2 and M3 using the historical data. Table 2 shows the uncertainty budget for meter M2 when historical data was available.

Table 2: M2 uncertainty budget with historical data Rank Uncertainty Source Units Value Expanded Relative Divisor Standard Sensitivity Output Uncertainty

Uncertainty Uncertainty Uncertainty Coefficient Uncertainty Squared

U U* (%) u c u.c (u.c)2

1 Calibration m39833 7.8663 0.080 2.00 3.9331 1 3.93E+00 1.55E+01

3 Curve Fit (linearity) P/m31.017 0.0003 0.030 2.00 0.0002 -9668 -1.47E+00 2.18E+00

2 Drift P/m31.017 0.0006 0.060 1.73 0.0004 -9668 -3.41E+00 1.16E+01

5 Resolution Pulses 10000 1.0000 0.010 1.73 0.5774 0.0001 5.77E-05 3.33E-094 Temperature/viscosity effect P/m3 1.017 0.0001 0.006 2.00 0.0000 -9668 -2.95E-01 8.70E-02

Overall Uncertainty m39833 10.837 0.1102 2.00 5.419 1 5.419 29.362

Sections 5.1 – 5.5 shows the analysis carried out to determine the magnitude of the uncertainty sources in meter M2. An identical exercise was also completed for meter M3 and the uncertainty budget can be seen in section 7. 5.1 Calibration The calibration uncertainty is the same whether historical data is available or not. In this case it is taken as the uncertainty of the NEL water flow facility primary reference which is 0.08%. This figure has been determined from an uncertainty analysis using data collected over a number of years.

8

5.2 Curve Fit An example polynomial curve fit for reference meter M2 is shown in figure 3. It can be seen that curve fit errors will be caused from the difference between the actual k-factor and the calculated k-factor based on the polynomial curve.

1.0150

1.0155

1.0160

1.0165

1.0170

1.0175

1.0180

1.0185

1.0190

0.0 50.0 100.0 150.0 200.0 250.0

K F

acto

r (P

/L)

Flowrate (l/s)

M2 Polynomial Example

Actual K-Factor

Calculated K-Factor

Figure 3: M2 example polynomial

Figure 4 shows all the curve fit errors for the calibrations from 2008 to present. It should be noted that data on the polynomial curves were not available from 2004 to 2008. The curve fit error data was analysed and it was found that 95% of the errors were within ±0.03%. Therefore the uncertainty caused by curve fit error was assumed to be ±0.03% at a confidence level of 95%.

9

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.0 50.0 100.0 150.0 200.0 250.0

Err

or (

%)

Flowrate (l/s)

M2 Curve Fit Error

27/11/2012 23/11/2012 18/11/201124/05/2011 19/11/2010 30/04/201027/11/2009 15/05/2009 13/11/2008Unc

Figure 4: M2 curve fit errors

5.3 Drift Figure 5 shows the calibration results for reference meter M2 from 2004 to present. As expected there is a spread of results and there appears to be a drift in k-factor. If there is drift in k-factor between calibrations then this will lead to uncertainty. It therefore has to be accounted for in the uncertainty budget.

1.011

1.012

1.013

1.014

1.015

1.016

1.017

1.018

1.019

0 50 100 150 200 250

K-F

acto

r (

P/L

)

Flow (l/s)

Reference Meter M2 K-Factor 27/11/201223/11/201218/11/201110/11/201124/05/201119/11/201030/04/201027/11/200915/05/200913/11/200803/07/200805/05/200810/04/200831/01/200726/01/200626/05/200515/05/200514/05/200512/05/200506/01/200514/12/200409/12/200416/11/2004

Figure 5: M2 K-factor 2004 to present

10

An average k-factor was calculated for each calibration from 2004 to present and this is plotted in figure 6. It can be seen that there is a general trend in that the k-factor appears to be increasing over time. However from one calibration to the next the k-factor both increases and decreases. An uncertainty can therefore be assigned to drift but there is equal probability as to where the k-factor will drift within this uncertainty band. Therefore a rectangular probability distribution has been assigned to drift.

1.0158

1.0160

1.0162

1.0164

1.0166

1.0168

1.0170

1.0172

1.0174

1.0176

10/12/2002 17/02/2005 28/04/2007 06/07/2009 14/09/2011 22/11/2013

K fa

ctor

Date

M2 Average K Factor

Average K Factor

Figure 6: M2 average k-factor

Figure 7 shows the drift of the polynomial curves from the previous calibration of meter M2. It can be seen that 95% of the data points are within ±0.06%. The drift uncertainty was therefore assumed to be ±0.06% at a confidence level of 95%.

11

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0 50 100 150 200 250

Drif

t (%

of R

eadi

ng)

Flowrate (l/s)

M2 Drift from Previous Calibration

27/11/2012 23/11/2012 18/11/201124/05/2011 19/11/2010 30/04/201027/11/2009 15/05/2009 13/11/200803/07/2008 05/05/2008 Uncertainty

Figure 7: M2 Drift from previous calibration

5.4 Resolution The resolution is simply the resolution of one pulse and as a minimum of 10000 pulses are taken at NEL this means the resolution will be a maximum of 0.01%. Resolution uncertainties have a rectangular probability distribution because within the uncertainty limits no value is more likely than another. 5.5 Temperature/Viscosity Effects Turbine meters are known to be affected by temperature and viscosity changes. Figure 8 shows the k-factor of meter M2 at 20°C, 34°C and 40°C. Although ideally more data would be available there appears to be a trend where the k-factor decreases as the temperature increases.

12

1.010

1.011

1.012

1.013

1.014

1.015

1.016

1.017

1.018

1.019

0 50 100 150 200 250

K F

acto

r (P

/L)

Flowrate (l/s)

M2 Temp/Viscosity effects

20C 18/11/11 20C 09/11/11 20C 27/11/12

34C 17/11/11 40C 15/05/13 40C 16/05/13

Figure 8: M2 k-factor at various temperatures

The k-factors were then averaged for each temperature and plotted as shown in figure 9. A linear relationship between k-factor and temperature was assumed and it could then be calculated that the k-factor reduces by 0.006% for every 1°C rise in temperature. The uncertainty is calculated by firstly calculating the temperature change which is the difference in temperature between calibration and use of the meter plus the temperature stability in use. This is then multiplied by 0.006 to determine the uncertainty in k-factor caused by the combined temperature and viscosity effect on the turbine meter.

13

1.0158

1.0160

1.0162

1.0164

1.0166

1.0168

1.0170

1.0172

1.0174

15 20 25 30 35 40 45

Ave

rage

k fa

ctor

(p/

l)

Temperature (°C)

K factor vs Temperature

K factor

Figure 9: Average k-factor vs temperature

5.6 Repeatability It can be argued that an uncertainty source due to non-repeatability should also be included in uncertainty budgets for flowmeters. However ISO 51683 states that: Meter performance characteristics such as non-repeatability are included in the curve fit uncertainty because the curve is necessaririly based on multiple readings. To test this theory a series of twenty repeat points were taken at 60 l/s during a calibration. It can be seen in figure 9 that all of these repeat points were within the curve fit uncertainty of 0.03%. Therefore it is assumed that uncertainty due to non-repeatability is included in the curve fit uncertainty.

14

1.0138

1.0148

1.0158

1.0168

1.0178

1.0188

1.0198

44 64 84 104 124 144 164 184

K-f

acto

r (P

/L)

Flowrate (l/s)

M2 Repeatability

Actual K-factor

Repeatability Points

Poly. (Calculated K-Factor)

Figure 10: M2 Repeatability test

6 MONTE CARLO METHOD It has been shown previously that the combined use of the analytical and Monte Carlo methods of uncertainty analysis can be useful4. The advantages of carrying out both methods on the same system are as follows:

• The Monte Carlo and analytical methods can be used to cross check against each other.

• The Monte Carlo method can be used to show if the output distribution is skewed or rectangular.

• The Monte Carlo method can be used to ensure that covariances are being accounted for in the analytical method.

• The analytical method can then be used to carry out what if analyses which will show the effects of changes in the input parameters on the overall uncertainty of the system.

Comparing the two methods is particularly useful when uncertainties are large compared to the measured values when the mathematical theory in the analytical method can break down.8 This is not the case for the example of the NEL reference meters but the two methods have been compared here for illustration purposes. The Monte Carlo method of uncertainty analysis has been used in this case to calculate the uncertainty of the NEL reference meters. The analysis was performed in Excel using 10,000 iterations. Figure 11 shows the output distribution for meter M2. It can be seen that the distribution is close to a normal distribution and does not have significant skewness. This can be proven by using skewness and kurtosis tests to determine the relative degree of asymmetry and flatness

15

in the output distribution compared to the normal distribution. Therefore in this case the assumption of a normal output distribution made in the analytical method is considered acceptable.

0

20

40

60

80

100

120

140

160

180

200

9,810 9,820 9,830 9,840 9,850 9,860

Num

ber

of R

eadi

ngs

Volume (m 3)

Output Probability Distribution

Figure 11: M2 output probability distribution

Table 3 shows the comparison between the analytical and Monte Carlo methods to calculate the uncertainty of meter M2. It can be seen that the agreement is within 0.015%. This agreement helps to increase confidence in the uncertainty calculation.

Table 3: Comparison between analytical and Monte Carlo methods

Method Used Expanded Uncertainty

(m3) Expanded Uncertainty

(%)

Analytical Method 10.837 0.110

Monte Carlo Method 9.311 0.095

Difference 1.52 0.015

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7 USE OF METERS IN PARALLEL If the flowrate is higher than 200 l/s in the NEL water flow facility then reference meters M2 and M3 are used in parallel. If meters are used in parallel then correlation or covariance have to be accounted for. Correlated uncertainties are combined arithmetically rather than using the root sum square technique which is generally used in the analytical method. In this case the calibration uncertainties are fully correlated because both meters are calibrated against the same reference. The curve fit, drift and temp/viscosity effect uncertainties are assumed to be partially correlated. This is because the meters are of the same manufacturer, model and size and are therefore expected to be affected in similar ways but turbine meters do have some degree of individuality due to manufacturing tolerances. The uncertainty budget for meters M2 and M3 when used in parallel is shown in table 4.

Table 4: Uncertainty budget for M2 and M3 used in parallel UNCERTAINTY IN M2Uncertainty Source Units Value Expanded Relative Uncertainty Correl.

Uncertainty Uncertainty Squared

U U* (%) (u.c)2 % u.c (u.c)^2 u.c (u.c)^2

Calibration m39833 7.8663 0.080 1.55E+01 100 3.93 15.47 0 0

Curve Fit (linearity) P/m31.017 0.0003 0.030 2.18E+00 50 -0.74 0.54 -0.74 0.54

Drift P/m31.017 0.0006 0.060 1.16E+01 75 -2.56 6.54 -0.85 0.73

Resolution Pulses 10000 1.0000 0.010 3.33E-09 0 0 0 5.8E-05 3.3E-09Temp/viscosity effect P/m3 1.017 0.0001 0.006 8.70E-02 20 -0.06 0.00 -0.24 0.06

Overall Uncertainty m39833 10.837 0.1102 29.362 N/A

UNCERTAINTY IN M3

Uncertainty Source Units Value Expanded Relative Uncertainty Correl.Uncertainty Uncertainty Squared

U U* (%) (u.c)2 % u.c (u.c)^2 u.c (u.c)^2

Calibration m310040 8.0321 0.080 16.129 100 4.02 16.13 0 0

Curve Fit (linearity) P/m30.996 0.0003 0.030 2.086 50 -0.72 0.52 -0.72 0.52

Drift P/m30.996 0.0007 0.068 14.327 75 -2.84 8.06 -0.95 0.90

Resolution Pulses 10000 1.0000 0.010 3.33E-09 0 0 0 5.8E-05 3.3E-09Temp/viscosity effect P/m3 0.996 0.0000 0.004 0.037 20 -0.04 0.00 -0.15 0.02

Overall Uncertainty m310040 11.416 0.1137 32.580 N/A

UNCERTAINTY IN REFERENCE METERS M2 & M3 IN PARALLEL

Uncertainty Source Units Value Expanded Percentage CorrelatedUncertainty U Uncertainty U* u.c (u.c)^2 u.c (u.c)^2

(%)

Calibration m3 7.95 63.19 0 0 6.32E+01

Curve Fit (linearity) P/m3 -1.46 2.13 1.03 1.07 3.20E+00

Drift P/m3 -5.40 29.12 1.27 1.62 3.07E+01

Resolution Pulses 0 0 8.2E-05 6.7E-09 6.67E-09Temp/viscosity effect P/m3 -9.8E-02 9.5E-03 0.28 0.08 8.89E-02

Overall Uncertainty m3 19873 19.720 0.099 9.72 94.45 1.66 2.77

Uncorrelated

Correlated Uncorrelated

Correlated Uncorrelated

Taken from appropriate metersArithmetic sum of u.c Rt sum squares

17

8 BENEFITS OF USING HISTORICAL DATA It is clear that using historical data to carry out uncertainty analyses leads to many advantages. It will allow the operator to determine a more accurate estimation of uncertainty on measurement systems. Other methods of estimating uncertainty sources such as using manufacturer’s specifications, the engineering judgement of experts or data from similar systems are all acceptable in the GUM. However the assumptions made using these methods will lead to a less accurate estimation of uncertainty. Having an accurate, evidence based estimation of uncertainty for a measurement system will allow cost effective improvements to be made to the system in the future. The uncertainty sources can be ranked to determine which sources contribute most to the overall uncertainty in the system. The uncertainty of these sources can then be improved first before time and money is wasted on improving the uncertainty of insignificant sources. If historical data is not used then the uncertainty budget will not be as accurate and it will be more difficult to determine the most significant sources. This is shown by the example in this paper where the most significant source is different depending on whether historical data is used or not. The process of analysing historical data will also bring benefits to the maintenance teams. Typically, maintenance (including verifications and calibrations) is carried out very frequently especially when a new system is installed. The frequency can be reduced over time if the instrument passes verification checks. By analysing the stability of instruments over a period of time however calibration schedules can be determined based on evidence rather than choosing arbitrary time periods. Less stable instruments can be calibrated more frequently and more stable instruments can be calibrated less frequently. If new systems are installed with identical equipment then evidence will be available for determining initial calibration schedules. This will help to save money and improve accuracy of measurement systems over long periods of time. If the stability of instruments is likely to reduce over time then it will also help determine when these instruments need to be replaced. If the frequency of calibration on more stable instruments can be reduced then it will also lead to improved safety procedures as it will avoid the need to break into the line which involves isolation and depressurisation. Using historical data to carry out uncertainty analyses will not only benefit the company operating the measurement system but will also benefit the industry as a whole. Increased knowledge of the uncertainty of measurement systems will lead to more effective allocation principles in shared pipelines. It will also help regulators to set regulations which are suitable and achievable based on current industry best practice.

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9 CONCLUSIONS AND RECOMMENDATIONS The analysis carried out in this paper has shown that the estimation of uncertainty in measurement systems can be greatly improved when historical data is available. The estimated uncertainty for the NEL reference meters when no historical data was available was 0.07% higher than when historical data was available. If this difference was applied to a large North Sea oil field producing 50,000 bbl/day then the overestimation of uncertainty would equate to a monetary value of $1.5m per year assuming an oil price of $120/bbl. This paper makes the following recommendations:

• Historical data should be used whenever possible to estimate uncertainty sources.

• Historical records of calibrations should be kept in good order so that they can be analysed at regular intervals. This should already be the case if the system is audited and therefore should not be difficult to achieve.

• Uncertainty analysis should be seen as an iterative process and uncertainty budgets

should be updated whenever new calibration data is available or changes are made to measurement system. It is recommended that as a minimum uncertainty budgets should be reviewed annually to ensure they are still relevant and accurate.

• If a system is new or calibration data is not available then uncertainty sources can be

estimated by other methods. However the uncertainty values should be updated over time as more historical data becomes available.

• It is recommended that manufacturers make available more data on the performance

and stability of instruments over time. Performance data is generally available on the manufacturer’s data sheet but different manufacturers present the data in different forms, a coverage factor is not always given and the stability of the instrument over time is not always given. It is understandable that these data sheets are used for marketing and need to be concise but as a minimum the data should be readily available on request. This will allow more accurate estimates of uncertainty when historical data is not available.

• It is also recommended that more sharing of data is carried out throughout industry.

This will lead to better understanding of measurement systems which will be beneficial for buyers, sellers and pipeline users. This paper recommends the development of a database of calibration data as described in section 10.

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10 DEVELOPMENT OF CALIBRATION DATABASE A paper by B Peebles at the North Sea Flow Measurement Workshop in 20125 recommended the development of a national database “to capture all UKAS calibration data for instruments and flow meters to provide the information required to monitor and enhance our knowledge and understanding of failure rates and subsequent availability analyses”. NEL agree that such a database would be very beneficial to the industry as a whole especially when specifying and selecting new equipment. This paper suggests that this could be carried out in three stages: Stage 1: In addition to the calibration of flowmeters, a single company’s calibration data could be analysed over a period of time similar to the analysis carried out in this paper. This could also be carried out on past data if the data is available and in a suitable format. Stage 2: If companies agree to share data then the data from more than one operating company could be analysed and shared. This will increase the amount of knowledge about the performance of commonly used measurement devices in the oil and gas industry. Stage 3: If enough companies become involved then a database will be developed to show the analysis of calibration and verification data for a number of companies with calibration data from various calibration laboratories. If the database is developed to stage 3 then it could be used in two ways. Firstly a company could use it to view information on their own individual instruments. The data on individual instruments could be protected to maintain confidentiality. The second way it could be used is to view all the analysed data for a particular type of or model of instrument. This would allow companies to make evidence based decisions about the uncertainty, maintenance and calibration schedules when installing new equipment or when historical data is not available. Figures 12 and 13 shows how the calibration database front page could look when used in these two ways. It should be noted that the information shown is for illustration purposes only to show the type of information that could be available in the database.

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TOP LEVEL SHEET

InputsAnalysis Type Individual Instrument

Company NEL

Serial Number 123456789 Uncertainty Information Units

Most Recent Calibration Lab NEL

Instrument Information Calibration Uncertainty 0.080 %Instrument Turbine Flowmeter Curve fit uncertainty 0.030 %Manufacturer Manufacturer A Curve fit (last calibration) 0.025 %Model Model 1 Drift Uncertainty 0.060 %Size 8" Drift (last calibration) 0.040 %Date of Installation 16/11/2004 Temperature/Viscosity effect 0.006 %Number of Calibrations 23

Last Calibration 27/11/2012

Current K Factor 1.017 p/l Calculated Uncertainty 0.1102 %

CALIBRATION AND UNCERTAINTY DATABASE: TOP LEVEL

Figure 12: Calibration database front page example (individual instrument)

InputsAnalysis Type Instrument TypeInstrument Type Coriolis MeterManufacturer Manufacturer AModel Model 1

Size 3"

Units

Average curve fit error 0.10 %

Number of calibrations

in database105

Average drift between

calibrations0.080 %

First Calibration 10/10/2005Uncertainty due to

temperature effect (per °C)0.001 %

Last Calibration 20/09/2013Uncertyainty due to pressure

effect (per bar)0.020 %

Uncertainty Information

Instrument Information

CALIBRATION AND UNCERTAINTY DATABASE: TOP LEVEL

Figure 13: Calibration database front page example (All data for model) If enough calibration data is collated and analysed then good quality evidence based estimates of uncertainty will be made possible on all commonly used measurement equipment in the oil and gas industry. Clearly there would be technical and contractual obstacles to overcome to create such a database but it would ultimately be mutually beneficial to all those involved and could lead to improved understanding of the uncertainty of measurement systems used in the North Sea.

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9 REFERENCES [1] Evaluation of measurement data — Guide to the expression of uncertainty in

measurement, OIML JCGM 100:2008

[2] Evaluation of measurement data − Supplement 1 to the “Guide to the expression of

uncertainty in measurement − Propagation of distributions using a Monte Carlo method

,OIML JCGM 101:2008

[3] Measurement of fluid flow — Procedures for the evaluation of uncertainties, BS ISO 5168:2005

[4] Allocation uncertainty – Tips, Tricks and Pitfalls, P Stockton, A Wilson, North Sea Flow Measurement Workshop 2012

[5] Discussion on Uncertainty Analyses, B Peebles, North Sea Flow Measurement Workshop 2012

[6] Measurement of flow in viscous fluids using a helical blade turbine, C Mills, R Belshaw, North Sea Flow Measurement Workshop 2011

[7] Determination of liquid flowmeter characteristics for precision measurement purposes by utilizing special capabilities of ptb’s ‘hydrodynamic test field’, R Engel, H Baade, International Symposium on Fluid Flow Measurement 2006

[8] Manual of Petroleum Measurement Standards, Chapter 4: Proving Systems, Section 2: Displacement Provers, API: 2003

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Subsea Sampling on the Critical Path of Flow Assurance

Eivind Vethe, Michael Smith and Bruno Pinguet, OneSubsea Bernard Theron, Malcolm Atkinson and Onur Ozen, Schlumberger

1. ABSTRACT The subsea oilfield development business is growing rapidly around the world, and it is going into ever deeper waters and involving increasingly long tiebacks. In such situations it is no longer viable to produce each well through one pipeline or riser. It is common for tiebacks to result in comingling of fluids from different reservoirs, often with different properties, in order to minimize the cost of production up to the FPSO or host platform before processing. In addition, in areas including the North Sea, some operators are simultaneously producing fluids as diverse as gas condensate and heavy oil to the same platform from different layers of the same reservoir. Flow assurance is becoming an increasing concern, as stopping flow could lead to situations posing safety risks and financial loss. It is, for example, extremely difficult to reduce production for a train of liquefied natural gas (LNG). Challenges include how to guarantee the flow; what to do to mitigate deposition; how to plan a shutdown of production and address collateral effects inside the riser. Other challenges include ensuring that the reservoir model is correct, identifying possibilities for optimizing production, and considering whether the fluids from tiebacks and/or multi-level reservoir production are compatible for efficient flow when comingled. These and more are all challenges that subsea operators face from the field development design phase to their daily operations. Subsea sampling of representative samples is a key source of relevant information to help mitigate flow assurance problems. For multiphase flow, a representative sample is defined as a sample where each individual phase has the same composition as the flow. This supposes that a thermodynamic equilibrium exists. Over the last 16 years, subsea sampling technology has been available for the subsea oil and gas market. In an increasing number of examples, the cost of gaining the knowledge provided by representative samples has been recovered many times over through early intervention to resolve issues such as localized water influx. The first operator to adopt the new technology was BP, which has applied it in the North Sea and West Africa. Experiences from these installations have been already documented in technical papers. This paper looks into the evolution of subsea sampling technology and how the hardware has evolved into today’s high-technology solution for multiphase subsea sampling. The process has seen development of the capabilities from the simple collection of a sample liquid to an understanding of the flow regime that has allowed multiphase sampling at representative subsea conditions. This paper investigates the main drivers experienced from the point-of-view of an oilfield services and solutions provider over the last decade, and how these drivers have influenced the evolution of its subsea sampling technology. The paper explains the benefits of representative subsea multiphase sampling and the beneficial outcomes that are enabled when this is combined with reservoir modeling and flow

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assurance modeling. In addition, the paper discusses what is possible to achieve today and what might be needed in the future to meet the demands of new business drivers that are coming in. An explanation is given of the relationship between subsea sampling and a multiphase flowmeter (MPFM) in order to demystify this relationship and create a common understanding between the two unique and independent technologies. It is becoming increasingly apparent that accurate knowledge of fluid properties – especially density – is essential for calibrating multiphase flow measurements. Finally the paper considers the current status of subsea sampling compared to expected future technical and regulatory needs for an increasingly accurate picture of reservoir and field production. 2. INTRODUCTION Oil and gas exploration and production operators are increasingly moving into deep and ultra-deep waters to explore and develop new fields. This has resulted in considerable success in areas such as Brazil, West Africa and the Gulf of Mexico despite the increasing need for more reliance on subsea production infrastructure. Increased water depths, longer tiebacks, complex reservoirs and the continuous effort to reduce the cost of the subsea infrastructure, while still ensuring the highest possible recovery rates, raise new challenges in terms of reservoir and production management, flow assurance and enhanced oil recovery. In this scenario, subsea sampling is rapidly becoming a simple but effective way to gather representative samples of the produced fluids through the life of the field, enabling reliable measurements of their properties, and of the changes that occur in these properties through the years. Subsea sampling, combined with the expanding use of subsea multiphase meters, provides new levels of understanding of subsea reservoirs and of their behavior, improving the decision making process thanks to increased accuracy, and leading to extended recovery rates, while also enabling simpler and more cost effective architectures of the subsea infrastructures. Subsea sampling is a commonly used expression for the functionality where equipment permanently installed on the seabed allows a deployed unit to capture samples of liquid or gas from subsea oil and gas installations. Common terminologies do not distinguish between the different methods that have been used for this process, and this paper focuses on explanation of the terminology and technology used by OneSubsea™. 3. THE NEED FOR SUBSEA SAMPLES Production management processes and workflows can be grouped into three main sets of activities, as depicted in Figure 1. These are: • Monitoring and Reporting • Surveillance and Analysis


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• Diagnosis and Optimization All of these activities require reliable measurement of the quantities of each phase (oil, water, gas) being produced by each well in the field, and on the properties of the produced fluids. The need for subsea flow measurements is already addressed by the use of subsea multiphase meters, which are already widely accepted in the industry. Subsea sampling, particularly in deep and ultra-deep waters, is gaining increasing acceptance as the preferred method for achieving high-quality representative measurements of produced fluid properties that enable effective production management. In addition, knowledge derived from subsea sampling improves the accuracy of subsea multiphase meter measurements, which not only supports better reservoir development decisions, but also enables more reliable reporting of production data. This is becoming more important with increasingly stringent production allocation guidelines and regulations.

Figure 1. The need for fluid properties as input to production management processes. Monitoring and reporting well, reservoir, and field performance is usually more complicated in deepwater subsea developments due to commingling of fluids from multiple wells and increasingly long tiebacks to other fields. Reservoirs and individual wells that are commingled at the seabed are often operated by different company partnership groups, and reliable flow measurement is key to accurate allocation of production and royalties. Gaining representative seabed samples prior to commingling enables geochemical fingerprinting and reduced uncertainty of flow measurements for calculating these allocations. Reservoir surveillance and analysis activities are at the core of any production management workflow. It is important to understand and predict reservoir behavior while ensuring uninterrupted production through the subsea infrastructure. The main activities in this group are:


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• Reservoir modeling and management • Water/gas/chemical injection management • Flow assurance In addition to flow rates, knowledge of the physical properties of the produced fluids is essential. Changes in these properties during the life of the field, if not detected and not properly accounted for, can lead to significant inaccuracies in the models used for the decision-making process, with significant financial impact for the operator. Geochemical fingerprinting is another important activity enabled by subsea sampling, which combined with flow measurements, allows the operator to allocate the production to each zone of a well. This is important because it enables multi-zone wells in situations that might otherwise require separate wells for each zone. Drilling one well per zone to meet allocation requirements could result in a decision that the discovery is not commercially viable. Diagnosis of production problems and corresponding production optimization activities requires accurate reservoir models. Reliable knowledge of fluid properties at line conditions reduces uncertainty and inaccuracies in these models and subsequent fluid dynamics simulations. Combined with multiphase flow measurements, subsea sampling provides essential information to support: • EOR / IOR • Production optimization • Production troubleshooting • Intervention planning Knowledge of the exact composition of the fluids produced from each well at line conditions enables reliable evaluation of pressure, volume, temperature (PVT) relationships and determination of a tuned equation of state (EoS). These are essential in the management of potential challenges such as scale, waxing, and hydrate deposition. 4. SUBSEA SAMPLING ADDING VALUE TO THE OILFIELD Subsea sampling plays an important role in the whole production management process for subsea field developments. An increasing number of operators are realizing the additional value provided by subsea sampling and are including the requirements of the sampling system in their subsea architecture designs. Figure 2 summarizes the most significant trends experienced in the subsea production world and how these trends represent important technical drivers for the subsea sampling market, particularly in deep and ultra-deep waters.


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Figure 1. Subsea production trends and drivers for subsea sampling. In addition to addressing technical challenges in deepwater developments, subsea sampling also supports more accurate production allocation through more accurate inputs to flow measurement systems. This is important not only for individual operating and partner companies, but also to meet increasingly stringent industry standards and national regulations. API published its Manual of Petroleum Measurement Standards (MPMS) Chapter 20.3 in January 2013, superseding API Recommended Practice 86-2005, which is withdrawn. The new standard addresses multiphase flow measurement in the production environment, upstream of the custody transfer (single-phase) measurement point, where allocation is applied. The document addresses operational requirements or constraints in multiphase measurement systems, including expectations for flow meter acceptance, calibration criteria, flow-loop and in-situ verifications. It specifically describes representative sampling as the ultimate way of setting up any MPFM. Furthermore, API MPMS Chapter 20.3 points to the importance of representative sampling as essential to reduce uncertainty in the overall measurement. The API MPMS 20.3 also points to the challenges of capturing representative subsea samples, and includes the following suggestions: • The sampling point should be in a vertical leg of the flow line; the best position is

immediately downstream of a flow line component providing a mixing effect.


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• Multiple subsequent samples should be taken, allowing each sample to completely separate before the WLR is measured. For some crude oils, this will require the use of a de-emulsifier.

• The sampling point should be close to the MPFM. An acceptable sample should

contain all of the fluid constituents and the time frame for the samples shall be selected such that the samples are representative for the liquid constituents passing through the MPFM during the same time frame.

The API MPMS 20.3 includes the phrase “NOTE 1 Verification techniques are used by some meters to determine fluid property changes, hence reducing or potentially eliminating the need for physical sampling”. It is the understanding of this paper’s authors that while this is the industry’s ultimate goal for the future, vendors are not there yet. Automatic updates based on an assumed salinity and non-representative in-situ measurements do not provide accurate and reliable information that can eliminate the need for physical sampling. It is also important to note that API 20.3 is aware of the challenge of obtaining phase representative samples. Phase representative samples measure water-liquid-ratio (WLR) and gas volume fraction (GVF) from the content of sampling bottles. It is our understanding that these parameters can be challenging to obtain from subsea samples considering the complex nature of multiphase flow and difficulties such as slug flow and emulsions. The document includes “NOTE 2 Due to the issues with multiphase sampling, samples may not fully represent the volume fractions.” The Norwegian Petroleum Directorate (NPD) recently issued new requirements in its “Måleforskriften”, which addresses metering for production allocation for fiscal regulations. This also states that subsea inline MPFMs are typically set-up using samples obtained during drilling. The composition of produced hydrocarbons changes over time and these changes are likely to be significant over the life of a deep sea development. Obtaining representative samples from the production system at line conditions enables maintenance of the inputs of multiphase and wet gas flow meters, leading to more accurate allocation. Of all the main drivers for subsea sampling mentioned previously, the need for more accurate conversion of flow rate data from line to standard condition is one of the most valuable (Figure 3). In allocation regimes where actual flow is being allocated according to ownership structures, a small failure in the conversion can lead to a significant value loss for one or some of the partners. These conversions are factors added more or less directly to the bottom line for projects.


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Figure 2. Calculation of flow rates at standard conditions based on measurement at line conditions.

Figure 3 Mathematical recombination of a representative sample


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The term PVT is well described in literature. The most common definitions are simplified and understandable for inexperienced readers who are exploring the topic. For the method of PVT correction described in this paper, a more detailed definition and understanding has been used; We define PVT (pressure, volume, temperature) as the knowledge about the volumetric changes caused by the shrinkage, expansion and density variations for all three phases present at different stages in a multiphase stream of oil water and gas. This knowledge can be summarized as the fluid behavior path. It is important to note that the density of a single fluid at line conditions does not supply adequate data to generate the PVT relationship between the produced fluids. Hence a suggestion that a multiphase meter can, through the use of a densitometer, compensate for changing PVT relationships is misleading and a gross misstatement. Another fact that is important to highlight is that the hydrocarbon mass remains the same through the journey from line to standard conditions, so this can be used for production allocation for true three-phase meters that measure oil, water, and gas without the input of GOR. Obviously, the volumetric proportion of oil and gas will change with the different stages in the fluid behavior path as pressure and temperature drop. The mathematical recombination based on the compositional analysis and assumed GOR gives a new phase envelope of the fluid as described earlier and then leads to a new definition of the following six parameters: • bo Oil shrinkage factor • bw Water shrinkage factor • bg Gas expansion factor • Rst Stock tank gas oil ratio • Rwst Stock tank gas water ratio • rgmp Gas phase condensate ratio Figure 4 shows the phase envelope for the gas and oil sample, separately analyzed. Combining both and having a given GOR defines the fluid phase envelope. It should be noted that the interception point of the gas and liquid envelope defines the operating pressure and temperature conditions, and this should represent the exact conditions where both samples (oil and gas) have been taken. This is an important point, because if they do not cross at this location it can indicate that the sample has been contaminated or suffered some other damage, such as failure in maintaining the thermo equilibrium conditions during the sampling process. This new EoS can then be used to post-process the raw data of the MPFM collected at the time of the sampling. This in turn will generate an updated and improved GOR and associated new flow rates. This GOR can be used to recombine (still mathematically) the initial sample of gas and liquid to obtain a new EoS. As depicted in Figure 5, this is an iterative process. In practice, the convergence is very quick, and typically less than 3 iterations are necessary.


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Figure 4. Mathematical recombination of an oil and water sample to redefine the reservoir fluid composition. Defining the new EoS provides clear benefits for the reservoir, allocation and flow assurance, enabling the possibility to identify potential pitfalls and, if necessary, adjust the development plans of the field to improve the recovery factor, which is the ultimate goal from an operator’s point of view. It is always the optimum situation to have good downhole sample from the reservoir during the exploration phase. A “good sample” here means an uncontaminated sample obtained as a single phase fluid at, or near to, known reservoir conditions and with a sufficient quantity to allow comprehensive fluid and flow assurance analyses. Integration of reservoir properties and robust understanding of the fluid characteristics and behavior allows a thorough assessment of the field development concept, plan and operating philosophy that will be the most economically and technically viable. Nevertheless, obtaining a sufficiently good downhole sample is often a challenge during field development, especially when sampling is done without proper planning for analysis requirements. It is also not uncommon for a field development plan to commence many years after the exploration phase, which was the time when the first samples were taken, either downhole or at the well test separator. Although details of the sample may have been recorded precisely, fluid compositions, properties and characteristics might have changed. As a result, assumptions are often made based


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on limited knowledge of the history of the fluid sample, limited fluid data (e.g. single fluid data but not the commingled fluid), operational experience, and reference to nearby reservoirs to provide some safety margins in the design of production system and operating philosophy. There are also uncertainties in reservoir properties, geological systems, and simulation tools. All of these uncertainties will result in operational challenges that must be resolved reactively or proactively. Apart from the uncertainty in the fluid data available from the early phase of a field development, there is a potential for changes in reservoir fluid compositions over the life of a field due to issues such as injection gas breakthrough; injection water breakthrough; unexpected communication with other reservoirs that have different fluid composition; commingling of production from different wells, zones, reservoirs or tie-ins; and microbiological activities that induce souring. Reservoir production optimization and maintenance teams often face fire-fighting and production management issues, in which case they need to quickly identify the causes of problems and the current status of the system in order to manage the response appropriately and effectively. There are three main varying factors in the system during the life of a field: reservoir condition; fluid composition, condition, properties and characteristics; and operating condition. For a well-planned and maintained production system, continuous measurements of downhole and subsea pressure/temperature/flow rate at points along the flow line is used with the PVT of the surface sample recombined from the gas and liquid phases obtained at the test separator to identify potential causes of production issues and the current status of the system. Unfortunately, production fluid arriving at the surface has often left behind some of its components along its flow journey. The recombined surface sample is not able to represent the reservoir fluid and can lead to misinterpretation or faulty analysis. The availability of the well stream compositions from each well (subsea sampling at the wellheads) and in the main flow line (subsea sampling at the manifolds), will reduce uncertainties and increase the efficiency and confidence of investigation to achieve more effective operational responses. A subsea sample can still suffer loss of some elements (e.g. wax/scale precipitation/deposition in the wellbore) or change of properties (e.g. increased viscosity due to cooling or emulsion) when travelling up the well or from the wellhead to the manifold. While the ideal situation is to investigate the full fluid journey from reservoir to the top side separator, subsea sampling at wellheads and manifolds, when integrated with the pressure, temperature and flow rate measurements, helps to find out where events take place along the fluid journey, which makes trouble-shooting faster and more efficient. It is helpful to know the well stream composition at the manifold, as this helps in the understanding of the characteristics and behaviors of commingled well streams, which are usually assumed, or are based on limited information during the design phase. Production monitoring models can then be updated to better predict system behaviors that allow good production optimization and planning. Production allocation is an important activity for an operator. Production allocation in deepwater fields with complex commingled streams and royalty allocations can be challenging, and inefficient measurement can lead to costly resolution. Production


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flow meters are often set-up based on fluids obtained from exploration samples, which can change over the field life. To have reliable flow metering, it is important to periodically update the meters with fluid properties from each well and commingled fluid, if applicable, over the field life. This fluid composition also enables fluid fingerprint identification when checking the change in fluid composition or tracing specific streams or fluid components. Subsea sampling, in addition to topside sampling, extends the ability to understand a subsea production system with increased confidence. Continuously updated information about the fluids over the field life, when integrated with reservoir information and flow conditions (pressure, temperature and flow rates), form a powerful surveillance database to support proactive operational actions, production optimization, and flow maintenance. Early identification of discrepancies between the crude compositions of samples obtained during the exploration phase (subject to correction for contamination and changes over time between sampling and testing) and compositions from periodic subsea sampling enables timely investigation of possible causes and, if required, appropriate adjustment of operating strategies and production requirements. Subsea sampling at the wellhead and manifold helps to identify the in-situ fluid composition and hence a cross-check of the current status of the fluid characteristics and behaviors in the reservoir and along the production systems. This information allows fine-tuning of the reservoir, well and production models over the production life, and consequently enables good prediction of system response for planned or unplanned activities with increased confidence. Up-to-date fluid composition and PVT data increases the confidence and reduces the time required for trouble-shooting production issues. Earlier and effective responses can be taken to assure optimum production uptime with minimum risks. Confident fluid data that is available continuously without interruption to production helps to optimize operations and the run life of equipment in the production system. It improves system integrity and minimizes risk of production downtime and supply interruption. Representative samples of produced fluids can be used to update and fine-tune reservoir, well, production and facilities models, and are an important factor in production surveillance, monitoring, planning and system modifications or upgrading. The data also support efficient and reliable production allocation by periodically providing updated information for EoS modeling. 5. SUBSEA SAMPLING TECHNOLOGY OneSubsea offers a subsea sampling system for the industry in its ability to provide high-quality multiphase fluid samples for full recombination and EoS modeling. The sampling module is manipulated by a remotely operated underwater vehicle (ROV) and connects hydraulically with the subsea infrastructure at a “blind-tee”. Subsea architectures of the OneSubsea PhaseWatcher Subsea Multiphase Flow Meter with Vx technology include a sampling interface as a standard option. Interface systems are also available for integration into the design of other subsea metering systems. It should be noted that the incremental CAPEX cost of including the interface in subsea architecture design is negligible, especially considering the long-term potential benefits.


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The sampling module, which is the mobile unit that connects with the permanent sampling interface, is initially configured as a loop to flush and pre-heat the sampling lines. When the flushing is complete, the sample is captured in pressure compensated bottles. The ROV-enabled operation is usually performed in a campaign that sequentially samples multiple wells in a field. The subsea sampling system comprises several key components in order to achieve the target of capturing truly representative subsea samples, these key components are briefly described in the following section.

Figure 5 Sampling cycles of displacement of fluid. A Schlumberger custom-designed displacement pump displaces the sample fluid with a minimal differential pressure (Figure 6). The design of this pump is an adaptation of a technology used in wireline sampling tools to the subsea environment. This pump is able to move fluids at a controlled rate from a high pressure point to another high pressure point. Incidentally, it also generates low mixing and pre-separation of the phases. All pumped volumes are measured by volumetric meters. In order to fully control which phases are being collected, optical phase detector (OPD) probes are used in the flow lines (Figure 7). Using these probes allows the sampling engineer to select which phase will be collected in the sample bottles. This is also an adaptation of an existing Schlumberger technology used in production logging and surface sampling tools to the subsea environment.

The system (Figure 8) makes use of a small separator to enable sampling of the phase of interest. This is particularly required to collect the “minority” phase. For instance if there is only 1% water in the production flow and the requirement is to collect a water sample, the separator enables the enrichment of the water content in the sample. The full system is maintained in a “heated bath” at line temperature until the sample is captured (isothermal sampling). This prevents composition change in the sample and particularly the precipitation of asphaltene and other heavy components. (Note that asphaltene deposition is more dependent on pressure than temperature). The sample bottles are US Department of Transportation (D.O.T.) approved and can be shipped directly from the sampling module. An internal piston ensures controlled filling of the sample bottle during operations and a nitrogen precharge in the bottle ensures the sample is maintained above line pressure during recovery to surface and


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transportation to the laboratory. The sampling lines are short and maintained at temperature.

Figure 6 Simplified subsea sampling system diagram

On the permanently installed subsea sampling Interface, the sampling ports are located in a “blind tee” ensuring a partial separation of the liquid and gas phases. The blind tee sampling interface can be deployed as a standalone device or together with MPFMs. When at the laboratory it is only necessary to heat the fluid back to the line conditions. The capability to monitor and control the pressure and temperature during the subsea sampling operation is also essential to prevent hydrates, waxes, scale, or asphaltene deposition. Figure 7. Parts of the subsea sampling system.


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5. SUBSEA SAMPLING AND MULTIPHASE METERING One of the main advantages of capturing a representative subsea sample is the ability to take samples in close conjunction with a subsea MPFM – for example, a PhaseWatcher subsea multiphase flow meter with Vx technology. Since the subsea sampling interface is based on a blind tee that is also a required inlet conditioner for the MPFM it forms a powerful combination with three key benefits. Firstly, the combination allows for recombination of the gas and oil samples, as described earlier. This enables analysis to generate a tuned EoS for the sampled well. This is not possible with only one of the devices by itself. It will reduce the uncertainty of PVT effects in an allocation regime, if volumes are utilized. Secondly, the combination of flow rates of oil, water and gas with their known compositions enables more in-depth flow assurance analysis. This can, for example, identify risks of commingling production subsea from different reservoirs. The third benefit of combining subsea sampling with the PhaseWatcher subsea multiphase flow meter is that it provides two unique measurements. The multiphase flow meter provides a unique measurement by use of a gamma system. This is based on a Barium 133 source which emits gamma rays at various energy levels. The traditional set up for the PhaseWatcher subsea multiphase flow meter is by use of two energy levels, also known as dual energy gamma ray detection. These are the measurements that provide and reliable WLR and GVF. These measurements are based on the hold up of gamma rays i.e. theoretical known properties. Figure 9 shows a schematic of an MPFM with a blind tee sampling interface. The green encircled area is the gamma system measurement, the orange encircled area the Venturi measurement, and the dotted black oval encompasses the blind tee at the inlet as the area from where the samples will be withdrawn. The blind tee sampling point provides the ability to withdraw representative samples, the Venturi measurement provides total mass flow rate, and the dual gamma system provides the rate of oil, water and gas flow at actual conditions. These measurements are logged as raw data files from which dedicated processing software is used to derive gamma hold up information with the analyzed composition from a truly representative sample. Operators can establish a correct EoS state in the MPFM computer and hence generate a more correct conversion from line to standard conditions. The focus in this case is set on the conversion from line to standard conditions because this is the biggest uncertainty contributor. MPFMs such as the PhaseWatcher subsea multiphase flow meter need input data as a reference, including attenuation for the energy levels in use for oil, water and gas as well as the densities. However this information is not as critical in the overall uncertainty budget as the conversion from line to standard.


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Figure 8 Schematic of MPFM with blind tee sampling interface(within black dotted oval line).

To summarize the main points of the combination of subsea sampling and multiphase metering: • Subsea sampling and multiphase metering are not dependent on each other

• Subsea sampling and multiphase metering can form a powerful combination in

the following cases: - EoS determination for allocation - fluid compositional analysis combined with flow rates for flow assurance

workflows and modeling.

• All multiphase meter measurements can gain from having a full compositional analysis from a truly representative subsea sample to provide up-to-date input parameters such as mass attenuation coefficients, densities, and permittivity of oil water and gas.


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6. CONCLUSIONS Subsea sampling is already acknowledged as a valuable requirement for some applications throughout the world and is being increasingly recognized as such by government regulations. Flow assurance is a major driver for subsea sampling and some recent projects have utilized the technology specifically for that purpose. Representative subsea sampling forms a basis of knowledge about the produced fluid and its behavior that can be used in many flow assurance and measurement related situations, and forms an important source of securing investment in subsea fields. There has been a tendency in the subsea measurement market to not take into account the value of obtaining up-to-date representative samples as well as suggest inaccurate solutions to overcome challenges, such as use of densitometers to gather fluid properties in a producing well. Associated required assumptions such as that the pipe is clean and that the fluids are pure are likely to be inaccurate in upstream oil and gas production. The subsea sampling technology discussed in this paper has undergone an extensive qualification program even though it is, to a large extent, based on existing proven technology from topside and downhole solutions. Experience using the technology provides confidence that it can address safety issues when opening a subsea pipeline and is able to take representative samples that, with proper handling and analysis, provide operators with valuable knowledge. 7. REFERENCES [1] SBORDONE A, SMITH G, SMITH M.T, VETHE E:” IBP1836_12” Analysis of subsea sampling applications and drivers for subsea field developments in deep and ultra deep water” [2] PINGUET B, VETHE E, SMITH M.T, SMITH G, SBORDONE A, NIGHSWANDER J:” OTC OTV-23340-PP Reducing uncertainty from PVT by representing subsea sampling [3] HOLLAENDER F, ZHANG J.J, PINGUET B, BASTOS V, DELVAUX E: “An Innovative Multiphase Sampling Solution at the Well Site to Improve Multiphase Flow Measurements and Phase Behavior Characterization” IPTC 115763, Dubai, December 2007 [4] HJELMELAND M.,PINGUET B., VETHE E., SMITH MT. “Subsea Sampling for Production Optimization and Increased Oil and Gas Recovery” March 2011 South East Asia Workshop [5] BOOTH D., SEBASTIAO P., “Greater Plutonio – Realtime Reservoir Management in a High Cost, Deepwater Environment” - SPE 128542 – March 2010 [6] VAGEN N.” Enhanced production and effective well testing by utilizing subsea multiphase boosting and flowmeters for BP’s Etap development (Subsea 97 International Conference, December 1997) [7] API MPMS Chapter 20.3: Measurement of Multiphase Flow. January 2013

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Condition Based Monitoring – A Fully Automated Station Solution

John Lansing CEESI, Nunn, Colorado

Abstract During the past several years the use of ultrasonic meters (USMs) has gained worldwide acceptance for fiscal applications. The many benefits of USMs have been documented in papers at virtually every major conference. The significance of knowing the ultrasonic meter is operating accurately has never been more important. The use of diagnostics to help identify metering issues has been discussed in several papers at many conferences [Ref 1, 2 & 3]. USM technology has played a key role in reducing Lost and Un-Accounted For (LUAF) numbers. However, like any technology, the client must understand the meter’s diagnostics in order to validate it is working correctly. Due to mergers, acquisitions, changes in technology and purchasing preferences within an organization, this can be extremely difficult as technicians often encounter multiple manufacturers’ equipment. Thus, what is needed is a system that can monitor the USM’s health, regardless of manufacturer, and provide a single report to the client when problems occur. This paper is about a system that not only monitors all the USM diagnostics from most common brands, but also monitors data from the flow computer, and by utilizing separate P&T transmitters, computes flow for comparison to the customer’s fiscal flow computer. In addition, the system monitors the gas chromatograph (GC) diagnostics to insure proper operation and validation of the gas quality data used in the flow calculations. Field information is transmitted via a secure cellular data modem to a cloud server. The client then automatically receives periodic reports via email providing detailed information about the meter stations operation. In the event a problem occurs with the USM, pressure or temperature transmitter, GC, or even if flow computer volumes are in question, the system will send an email or text message (SMS) immediately to the appropriate personnel. Introduction The traditional method of verifying whether the USM is operating accurately essentially requires using the USM manufacturer’s diagnostic information to help understand the meters health. This is usually accomplished by having a technician visit the site periodically (typically monthly) to collect a maintenance report. This report is analyzed by the technician while onsite, and often analyzed a second time by office measurement specialists at a later date. However, if a problem has occurred during the month, and isn’t present at the time of the site visit, added measurement uncertainty may be the result. More recently some customers have implemented a semi-continuous monitoring system that collects diagnostics from the meter on a more real-time basis. This has often been referred to as Conditioned Based Maintenance, or CBM for short. Essentially when any of the meter’s diagnostics are outside prescribed limits, the SCADA system will alert the client of a potential problem. Implementing a system to not only collect the USM data, but also transmit and provide further analysis, is not easy and is often very costly.

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Another problem clients face within this model of CBM is that different USM meter designs require different analysis techniques, especially for the velocity profile analysis. For the field technician, it is often difficult to understand all the diagnostic features of each USM meter design. Diagnostic limits vary from one brand of USM to another. They also may not have sufficient data communication bandwidth to bring the diagnostics back thru their SCADA system. Additionally companies may lack the internal technical expertise to fully understand what the meter is telling them. Thus developing a company-based system can be challenging. Through the years manufacturer’s software has continuously evolved to significantly simplify determining if the meter is operating correctly. While this is an improvement for CBM, in most cases the client still has to travel to the site to collect the data. In addition, since companies are generally required to validate the accuracy of the P&T transmitters, remote data collection of the USM diagnostics is not widely used. A system that continuously monitors the health of the USM, rather than relying on a periodic analysis of a USM maintenance report, greatly improves the potential of timely data review. The CBM concept is not new. For many years some clients and manufacturers have programmed their onsite flow computer to do some basic monitoring of the gas USM diagnostics. From an industry perspective, these programs were generally proprietary to a given brand of flow computer and USM, thus implementation for other customers was generally not possible. Another issue with traditional CBM implementations is additional data has to be transmitted via the customers telecommunications network (usually via the SCADA network). This creates a significant amount of work to get the programming in place, and uses up valuable bandwidth. The programming is generally specific for a brand or two of USM, and thus it becomes costly to add additional suppliers to the system. Traditional CBM implementations generally focus on only monitoring the USMs health, and does little, if anything, to validate the integrity of the entire measurement facility. It is certainly important to insure proper USM operation, but it is equally important to verify the flow computer is also working correctly, that the P&T transmitters are still accurate, and the gas chromatograph (GC) is performing properly. What is needed is a separate, independent system that can be easily retrofitted to the existing sites without requiring modification to the existing SCADA network, and that can work with equipment currently in use by the customer. It needs to validate all aspects of USM facility operation in order to minimize site visits and immediately report when problems occur. This system needs to work with a variety of USM brands as well as GCs. It should also validate that the existing fiscal flow computer is collecting all the USM pulses (no more, no less), that it is correctly computing AGA 7, 8, 9 & 10, as well as correct energy rates. With today’s technology, this is now possible. System Overview This paper discusses an entirely new concept that provides many unique benefits over traditional CBM systems. Continuous station health monitoring includes not only verifying the USM diagnostics are OK, but also validating P&T values are still accurate, the flow computer is performing properly, and the GC is also providing accurate information. This is accomplished by utilizing a variety of advanced diagnostic techniques that quickly and accurately identify measurement problems. Benefits include extending periodic site inspection and calibration intervals, providing immediate alarming via email and/or text messaging when problems occur,

reducing travel time by technicians and lowering overall measurement station O&M costs. Continuous “health” monitoring translates into overall reduced facility uncertainty and ultimately lower company-wide LUAF. Perhaps a better acronym for this would be “Continuous Based Monitoring System,” or CBMS. The CBMS system is comprised of both site-installed hardware and cloud-based software server components. Field installed hardware collects, computes and transmits a variety of measurement data to an off-site server. A secure cloud-based software system receives and analyzes the data, then provides periodic reports to the client via the Internet at prescribed intervals. Both hardware and software work together to remotely monitor and verify all aspects of the USM measurement station performance continuously (24/7/365). The Hardware Component The field hardware consists of a flow computer based device that monitors the measurement station equipment’s performance in “real-time.” USM diagnostic “health” information, along with volumetric flow data, is obtained serially to ensure all values are within the normal operating range. Fiscal pressure and temperature readings, as well as actual and corrected volumes, are acquired directly from the client’s flow computer. The hardware component then computes AGA 7, 8, 9 & 10 to confirm the fiscal flow computer is obtaining the correct number of pulses from the USM, thus ensuring accurate volume and energy values. In addition, the meter’s reported SOS is directly compared to the AGA 10 computed SOS within the CBMS unit. No other system offers this complete level of computational redundancy and verification. This field hardware device continuously performs short-term analysis of USM diagnostics. This consists of transducer Performance, SNRs, path-to-path SOS deviations, Gains, Turbulences, Profile Factor, Symmetry and Crossflow. It also monitors for deviations in the fiscal P&T transmitters, GC performance problems (response factors and alarms), and flow computer calculation discrepancies (actual volume, compressibility, corrected volume and energy). This is accomplished by incorporating a redundant P&T transmitter for each meter run. By comparing the CBMS pressure and temperature readings with the fiscal flow computer’s values, deviations can be quickly and easily identified. When an issue occurs with any of the facility's equipment, the field unit immediately transmits the problem to the AT&T cloud-based system. The client can choose to receive these alerts in real-time and/or daily via email and/or text messages. Hourly average flow data and diagnostics information is automatically transmitted once per hour for further detailed analysis, trending, archiving and reporting by custom-developed software. All communication is via a secure digital cellular network that is 100% independent of the client’s SCADA system. In the event cellular data is not available, satellite communication can be used. Today many clients use multiple USMs at their facility in order to provide added rangeability or, in some cases, redundancy. In many Transmission applications, the USM is used for bi-directional applications. In order to be cost effective, is important that any device attempting to provide this information be able to handle more than one meter. A meter run is defined as one USM electronics head with a single flow direction. Thus, a bi-directional single meter run station would be considered as two meter runs. A single CBMS field unit is designed to handle up to four (4) meter runs; thus it is capable of handling a two-run bi-directional meter station. Additional field units can be added for larger stations.

Figure 1 is an example of a two parallel meter runs with two meters in series. This configuration is often used in high volume application to help validate the fiscal USM. Two USMs in series provide redundancy and reduced uncertainty because generally two different path configurations are used.

Figure 1 - Dual Meter Runs in a Series/Parallel Configuration

Figure 1 shows the client’s transmitters in blue and the CBMS system components that are added (hardware and transmitters) in Red. Some key system features are as follows:

• It incorporates a separate, fully independent internal flow computer for obtaining P&T readings, computing uncorrected and corrected flow (from serial USM data), AGA 10 SOS, and communicates with the gas USM and GC.

• P&T readings for each meter run are continuously validated by comparing the fiscal transmitter readings to the unit’s independent multi-variable transmitter (MVT).

• Daniel, Instromet and SICK ultrasonic meters, Daniel and ABB gas chromatographs, and most common flow computers are polled via Modbus using a local RS-232 or RS-485 port.

• When physical wiring isn’t practical between the CBMS base unit and the new MVT transmitters, existing fiscal flow computer, USM and/or the GC, the system can be equipped with an optional 900 MHz radio system data communication.

• All data is transmitted via secure digital cellular radios by either an AT&T Asavie VPN client, or by AT&T ANIRA software. Other cellular data providers can be used where AT&T is not available. This fully isolates the client’s site from the public Internet. In other words, the IP address of the field unit is invisible to the public Internet.

• All radio data transmission is secured by SSL, 256-bit AES, and other encryption technologies.

• One field-installed system can support up to four parallel ultrasonic meter runs (four uni-directional or 2 bi-directional), or two parallel meter runs utilizing two series USMs in each run. Additional systems can be added to accommodate larger stations.

The “Cloud-based” CBMS Software and Hardware Components The off-site “cloud-based” system host is a proprietary software application developed in conjunction with AT&T. It is specifically designed to collect, analyze and monitor measurement station performance data. The unique algorithms incorporated provide storage, trending and further data analysis, and automatically generates warnings, alarms, and a variety of reports. This provides clients with fast, easy and secure proactive access to their site information without the limitations of corporate SCADA networks. Some key features of the “cloud-based” CBMS system are:

• Performs AGA meter run flow calculations to provide back up (redundant) flow data. • The AT&T Data Center system provides multiple levels of security and encryption,

including SSL and 256-bit AES encryption, VPN connections, and managed firewalls. • All reports are accessible via the Internet from any computer using Microsoft Silverlight &

Internet Explorer (or other web browser). No special software is required to obtain reports. • Customized reports can be user-developed with Excel 2010. • All meter station reports, regardless of gas USM brand, look the same. Consistent report

structure greatly reduces training and simplifies understanding of meter station performance.

• Standardized reports are delivered in a PDF format for ease of viewing and sharing. • Series and parallel meter run flow rate ratios are analyzed and alerts are generated when

deviations exceed customer specified limits. • Provides long-term trending of all vital USM diagnostic data to help identify “slow-to-

develop” issues like contamination, gasket extrusion, and minor flow conditioner blockage. • This AT&T solution provides all the necessary infrastructure to support the statistical

processing, control chart development, and data filtering for analysis and generation of custom reports.

Figure 2 is a drawing that shows an example of the communication topology.

Figure 2 - Overall System Communication Topology

This Figure 2 graphic includes the field-installed hardware in the upper left of the drawing, the client’s communication with the AT&T Data Center, and how the technical support team gains access to set up each client and meter run. Periodically the client receives reports via email that summarizes the performance of the USM metering station. This not only includes the USM diagnostics, but also P&T verification, actual and corrected volume validation, GC performance and flow rate ratios for series or parallel meter station designs. These reports are sent either daily, weekly or monthly, depending upon the client’s need. They are in a PDF format for ease of viewing and distribution. In the event an alarm or event occurs in the field, the client can choose to receive these immediately, or daily via email or text message. The client can also gain access to data by logging into the system. Clients can easily assign different levels of access to each user. Using Internet Explorer with Microsoft Silverlight, the user simply needs to connect to the system’s URL, enter their user name and password, and all sites to which they have authorized access become available. Once connected to a specific meter station, additional reports and quasi-live data (last two minute average flow data) is available. There are many reports (more than 50) that the client can choose from. Historical trend reports for all USM diagnostics, meter run pressures and temperatures, actual and corrected flow rates, as well as gas chromatograph information are readily available. Customized reports can be generated using Excel 2010. Report Examples The client will automatically receive reports summarizing the health of the facility. Depending upon client preference, these can be daily, weekly or monthly. The client can choose from a vast menu of available reports to customize data presentation for their particular needs. The “Alarm

Report” is one report option that summarizes the facilities performance, and includes an overview of not only the USM, but all other aspects of station operation. This report provides alarm condition summaries for the following categories:

• USM Detailed Analysis • USM Path Status • USM Summary Analysis • USM Meter Head Analysis • Pressure and Temperature • Flow Calculations • Series and Parallel Meter Analysis • Events like Testing and Calibration

Figure 3 - Alarm Report Example

Figure 3 above illustrates an Alarm Report. The report quickly illustrates the percentage of time any alarm condition monitored by the CBMS field unit that was in alarm during the report test period. Green boxes with zeros indicate that no alarm condition was detected during the test period, while a red bar with a number other than zero indicates the amount of time a particular alarm condition was active. For example, the AGA 10 SoS %Diff alarm was active for 33% of the test period (see bottom left of Figure 3). The graph in the bottom right of Figure 3 illustrates when the AGA 10 SoS %Diff alarm was active. The user can use the graph to select and interrogate any of the active alarms. A second report, called the Run Verification Report, provides detailed values for each of the diagnostic tests. The Run Verification Report is illustrated in Figure 4.

Figure 4 - Run Verification Report Example

The Run Verification Report provides a concise summary of how close the various diagnostic tests are to their expected values. This report summarizes each of the test conditions in more detail. For example, transducer performance is shown as 100% for all 4 paths. The individual per-path SOS differences are graphed with some reading slightly low and some reading slightly high. The “Exp” in the middle is the “expected” value. The bar graph for each variable may be to the right or left depending upon if the variable is above or below the expected value. For transducer performance, it can only show a bar to the left of “Exp” since performance can never be greater then 100%, which

is the expected value. Once a variable exceeds the LL (Low Limit) or HL (High Limit) limit, it will turn red indicating a potential problem. This summary includes all diagnostic tests on the USM, and includes others like the P&T Verification, Flow Computer Calculation Verification, Series and Parallel Meter Analysis and Gas Chromatograph Verification. The combination of the Status Report and the Run Verification Report provides a comprehensive overview of all aspects of the facilities’ operation. Another example this report provides is to show the USM diagnostics are all near the expected values, and the percent difference between the meter’s measured speed of sound and the AGA 10 calculated speed of sound was 0.081% for the test period. The percent difference between the pressure and temperature measurements during the test period were -0.031% and -0.100% respectively. For this test period there was no difference in the volume calculations (0.000%) when comparing the customer’s flow computer and the CBMS field unit values. The percent change in the response factors are also illustrated in the bottom right of the report and are all minimal (typical values). Figure 4 illustrates a report from a series meter installation. This can be seen by examining the Series Meter Analysis Section (lower left of the graphic). It incorporates data from both meters. In this example the user can easily see the average flow difference between the two meters (-0.233%), plus the speed of sound difference between the two meters (-0.073%). This allows users of Daniel Senior Sonic and Junior Sonic series installations, or users or SICK 4+1 or 4+4 installations, or for that matter in combination of ultrasonic series metering, to easily take advantage of the series diagnostics. Should the client want to review any particular variable over time, a simple “mouse click” will then permit reviewing a report that trends the variable over time. For instance, if the client would like to see how the Profile Factor has been behaving over the past week, clicking on the cell will bring up a report as shown in Figure 5. The same function applies to all other diagnostic parameters.

Figure 5 – USM Profile Factor Example

Figure 5 shows a trend for the Profile Factor over 1 week. The data to the right of the red dashed, vertical line represents the test period, while the data to the left of the line provides a quick, visual reference. The Profile Factor illustrated in Figure 5 is very stable during the entire period, which is expected because the velocity, graphed in the lower right, was also very stable. Figure 6 illustrates a Profile Factor Test for a meter flowing from 1 fps to 15 fps. The data is from a Q5 Instromet meter. The Profile Factor is expressed as the ratio of the swirl to axial velocities, and therefore is less than one under normal conditions. This figure shows that the alarm limits are a function of velocity, and thus the tolerance changes as the velocity changes. This method allows the user to establish tight alarm limits through the entire operating range. It also allows the user to characterize the USM diagnostics down to very low velocities.

Figure 6 – USM Profile Factor Example

Figures 7 and 8 that follow illustrate how the same test report format is used to other diagnostics. Figure 7 is an example of a report for the AGA 10 Speed of Sound Percent Difference test. This figure shows that during the weeklong period the percent difference between the meter’s measured speed of sound and the calculated speed of sound was within a tenth of a percent. For the test period the average was -0.06%.

Figure 7 – AGA 10 SoS % Difference

Figure 8 – Static Pressure % Difference

Figure 8 illustrates the percent difference in static pressure between the fiscal measurement transmitter and the monitoring systems transmitter. This graphic shows that for the test period the difference was approximately -0.06%. Conclusions As technology continues to advance, more automation is the result. Residential Automatic Meter Reading (AMR) is commonplace today. The cost and reliability of these systems has significantly improved during the past decade.

Providing a system that does more than just monitor a USM metering station performance is now more practical then ever. Through the use of web-based systems like the AT&T cloud, more automation is being achieved today in order to lower meter station uncertainty. As the entire facility can now be monitored on a real-time basis, problems like liquid contamination from hydrates, blocked flow conditioners, contamination buildup within the metering facility, and much more can be quickly identified and immediately communicated to the client via an email or text message. All of this is now possible without the need to visit the facility periodically. By incorporating a CBMS system, not only is the USM facility station uncertainty lowered, the need for monthly site visits to verify the accuracy of pressure and temperature transmitters is also reduced by incorporating separate redundant measurements for each meter run. With the addition of AGA 10 SOS being computed by the CBMS unit, any problem with the gas chromatograph can also be quickly identified. This even includes components in the flowing stream that are not being detected like H2 and He. With the emphasis by many clients to “do more with less,” every aspect of field maintenance is being reviewed in order to reduce costs. Fewer site visits can increase vehicle longevity, reduce exposure time for potential vehicular accidents, and reduce vehicle emissions that translate into a better environment for everyone. Finally, calling this system a Condition Based Monitoring System isn’t fully descriptive. Perhaps it should be called a Complete Measurement Verification System (CMVS) since it not only reports in when a “condition” exists, it verifies the operation of all ancillary equipment including the flow computer, P&T transmitters and GC. Maybe a better term than this CMVS “reduces your measurement uncertainty” would be “Increases Your Measurement Certainty” by insuring all equipment is operating correctly 24/7/365. References 1. John Lansing, How Today’s USM Diagnostics Solve Metering Problems, North Sea Flow

Measurement Conference, October 2005, Tonsberg, Norway 2. Klaus Zanker, Diagnostic Ability of the Daniel Four-Path Ultrasonic Flow Meter, Southeast

Asia Flow Measurement Workshop, 2003, Kuala Lumpur, Malaysia 3. John Lansing, Advanced Ultrasonic Meter Diagnostics, Western Gas Measurement Short

Course, May 2007, Seattle, Washington, USA

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31st North Sea Flow Measurement Workshop 2013

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