Flow Assurance Validation of Wax Deposition Models Using Field Data- OTC-2011

7/26/2019 Flow Assurance Validation of Wax Deposition Models Using Field Data- OTC-2011

1/19

OTC 21641

SS: Flow Assurance: Validation of Wax Deposition Models Using Field Datafrom a Subsea Pipeline

A. Singh, SPE, the University of Tulsa; H. Lee, SPE, and P. Singh, SPE, ConocoPhillips Company: and C. Sarica,SPE, the University of Tulsa

Copyright 2011, Offshore Technology Conference

This paper was prepared for presentation at the Offshore Technology Conference held in Houston, Texas, USA, 25 May 2011.

This paper was selected for presentation by an OTC program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenreviewed by the Offshore Technology Conference and are subject to correction by the author(s). The material does not necessarily reflect any position of the Offshore Technology Conference, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of OTC copyright.

AbstractThis paper describes an offshore production system with a subsea pipeline (12-inch diameter 23-km long) where wax

deposition has been the primary flow assurance challenge. This subsea pipeline has been transporting nearly 55,000 BOPD

from a central processing platform (CPP) to a Floating Production Storage Offloading (FPSO). The crude oil has been

characterized for wax potential by performing several laboratory analyses which have been entered into available waxdeposition models to predict the wax deposition rate inside the pipeline under various operating conditions. The transient

flow rate and pressure drop data from the field have been analyzed to estimate the average wax deposit growth rate. This

paper will share the findings from the field validation and application of the available wax deposition models. The results will

help pipeline design engineers to accurately estimate the required insulation level for wax deposition control and will alsoassist in operating the pipelines with the optimum pigging frequency.

This study has confirmed that Film Mass Transfer (FMT) model gives higher wax deposition rate than Equilibrium

model (EM). The predicted deposition rates from both of the models have been much lower than the field data. Using

default viscosity, both EM and FMT models gave reasonable predictions of the wax deposition rate as compared to

deposition rate obtained from the field data. The field deposition rate is higher than the predictions by the EM model withoutany shear removal. By incorporating aging process to the FMT model, it was found that the value of Kaparameter (as

defined in the aging model by Singh et al., 2000) of 1 matches the deposit wax content observed in the field. Matzain shearremoval model (2001) over-predicts the impact of shear on the rate of wax deposition; however, Venkatesan's shear removal

model (2003) shows a very small impact of the shear.

The deepwater project development and engineering design needs an accurate prediction of the wax precipitation and

deposition in subsea pipelines. A number of wax deposition simulators have been developed to predict the wax depositionrate. Although there have been several attempts made to validate the wax deposition prediction models with laboratory data,

no reliable field verification study could be found prior to this study.

IntroductionParaffin deposition takes whenever paraffinic oil gets in contact with a cold pipe wall or ambience below the Wax

Appearance Temperature (WAT) of the oil, solid paraffin crystals can precipitate and deposit on the pipe surface. Deposition

of wax from oil leads to a gradual decrement in production due to plugging of wells and/or pipelines and, in an extreme case,can render a pipeline or production facility abandoned. Various measures including chemical and mechanical have routinelybeen applied. Mechanical methods include pigging and wireline cutting to remove the deposited wax. Chemical methods

include paraffin inhibitors/dispersants to inhibit the formation of deposit and hot solvents to remediate the deposit already

formed. These methods to prevent and remediate wax deposit add significant operating cost to the production operation. .

Successful development of the offshore deep-water projects requires engineering designs of the facilities that can handlethe flow assurance challenges such as wax deposit problems. A robust design can only be obtained with an accurate

prediction of the wax precipitation and deposition. Therefore, an accurate modeling of wax precipitation and deposition is

imperative to facilitate a successful engineering design, development, and operation of deep-water offshore projects.

Researchers have already made significant progress in understanding the thermodynamic equilibrium and depositionmechanisms of wax. Thermodynamic models have been developed based on precipitation of wax as multi-phase solids

solution to successfully describe the formation of solids wax as a function of temperature (Lira-Galeana et al., 1996,

Coutinho and Ruffier-Meray, 1997). The overall consensus is that the dominant mechanism of wax deposition is molecular


2/19

2 OTC 21641

diffusion in laminar boundary sub-layer driven by the radial Fickian diffusion of n-paraffins, the primary wax formers

(Brown et al., 1993 and Singh et al., 2000). Precipitation kinetics in laminar boundary sub-layer is included to describe the

turbulent flow wax deposition behavior (Lee, 2008). Several researchers also expressed that in turbulent flow, the shearstress acting on a wax gel layer may slough off wax from the deposition layer. This effect has been considered as one of the

major mechanisms to reduce the deposit thickness in cases with high turbulent flow (Venkatesan, 2003).

Various simulators like TUWAX, OLGAs wax deposition module; PVTsims Depowax, etc. include some of the

progress made in our understanding of the thermodynamic equilibrium and deposition mechanisms of wax. These software

packages cannot be completely relied on because the models used in the software has their own assumptions and limitations.For example, Singh et al.(2000) model works very well within laminar flow but for turbulent region it needs to be modified

(Venkatesan 2003). Since the complexity of the wax deposition phenomenon, these simulators do not capture all of the

physics and tend to ignore some of the critical aspects while simplifying the modeling parameters.Validation of wax deposition models have been the focus of several research projects published in the literature. Model

systems comprised of food grade waxes dissolved in model oils (a blend of mineral oil and Kerosene) were used by several

researchers (Singh et al., 2000, Venkatesan, 2003, and Lee 2008) to perform the laboratory deposition experiments utilizing a

flow loop system. These researchers were able to fit the modeling parameters to match their experimental data. However,

limited attempts were made to validate the wax deposition models for real crudes using laboratory data. Tulsa UniversityParaffin Deposition (TUPDP) consortium has extensively utilized South Pelto crude and Garden Bank condensate (Lund,

1998, Matzain, 1998, Apte et al., 2001, Hernandez, 2003, Couto et al., 2006, Espinoza, 2006, Bruno et al., 2008,) to obtain

experimental data to further study to predict wax deposition. Recently, a detailed experimental study was presented on aNorth Sea waxy condensate (43API) using a laboratory flow loop system by Hoffmann and Amundsen (2010). In another

study, a west African waxy crude (36API) was tested in a flow loop deposition setup by Alboudwarej et al.(2006). With

these studies, significant progress made on wax deposition prediction, but scaleup of the data to the real field cases still

remains as a problem.

Very few attempts have been made to validate the wax deposition models using field data (Hsu et al., 1998, Klienhans etal., 2000). Instead of true field scale systems, these studies utilized side streams and loops at the well site to generate data

using fresh produced fluids. Labes-Carrier et al.(2002) and Bagatin et al.(2008) utilized some of the operational experience

and qualitative information to validate wax deposition predictions.This paper studies an offshore production system with a subsea pipeline where wax deposition has been the primary flow

assurance challenge. The crude oil has been characterized for wax potential by performing several laboratory analyses.

These data have been entered into available wax deposition models to predict the wax deposition rate inside the pipeline

under various operating conditions. The transient flow rate and pressure drop data from the field have been analyzed to

estimate the average wax deposit growth rate. Pipeline inlet and exit temperatures have also been analyzed to estimate the

overall heat transfer coefficient.

Field DescriptionThe field layout of the offshore case studied is given in Fig. 1. A subsea pipeline transports waxy crude oil from a central

processing platform (CPP) to a Floating Production Storage Offloading (FPSO). The single phase crude oil pipeline has a 12

in. diameter and is 23-km long. A separate 16 in. gas pipeline transports the gas from the CPP to the FPSO. Flow rate of the

export oil is 55,000 BPD with a pipeline inlet temperature of 165F. The subsea water temperature is nearly 77F. Wax

appearance temperature of the crude is measured to be 136F by a cross-polarized microscope. The pipeline is un-insulated

and partially buried. Wax deposition inside the pipeline is managed by a routine pigging operation.

Figure1. Field Layout of the Case Study

SubseaPLEM

FPSO

CentralProcessingPlatform

Gas - 16" x 23 km

Oil - 12" x 23 km

Export Oil Rate = 55,000 BPD

Export Temp = 170oF

SubseaPLEM

FPSO

CentralProcessingPlatform

Gas - 16" x 23 km

Oil - 12" x 23 km

Export Oil Rate = 55,000 BPD

Export Temp = 170oF

A sample field data of the pipeline operation for a 32-day production period is summarized in Fig. 2. Temperature of the

export crude at the pipeline inlet is maintained at ~165F. Crude oil flow rate stays around 55,000 BOPD during this period.


3/19

OTC 21641 3

The pressure drop data clearly shows the effectiveness of the weekly pigging operation. Due to the wax deposition, pressure

drop of the pipeline gradually increases from ~200 psi to ~300 psi in a week of continuous production. Pigging operation

clearly decreases the pressure drop back to ~200 psi just after the pig run. The deposition continues even after a week asindicated by the gradual increase in the pressure drop between the two consecutive pig runs with no indication of a plateau

behavior in the pressure drop. A detailed set of crude properties has been discussed in the next section to accurately model

the wax deposition behavior of the crude oil using several models in TUWAX simulator.

Figure 2. A Sample of Pipeline Operation Conditions

Fluid PropertiesA summary of the results of fluids analyses of the stock tank crude sample is presented in Table 1. The crude sample hashigh normal paraffin content.

Table 1: Summary of Results of Crude Properties

Wax ContentHigh Temperature Gas Chromatography (HTGC) has been used to characterize the molecular weight distribution of both the

n-alkane and the all hydrocarbons as a function of the carbon number present in the stock tank sample. The results arepresented as the weight percent of all hydrocarbons containing a given carbon number and the weight percent of n-paraffin

(Fig. 3). Because the normal n-alkanes precipitate at higher temperatures than the iso-alkanes of the same carbon number,

they are responsible for higher cloud points and wax deposition issues.

Wax Appearance Temperature (WAT) and Pour Point Temperature (PPT)

WAT has been measured using cross polar microscopy. The sample has been preheated to 82C (180F) to remove anythermal history and introduced inside a microscope capillary that has been placed on the stage at 80 C (176F). PPT of thestock tank sample has been measured using ASTM D5853-95 procedure with the beneficiated method that requires

preheating the crude to 82C to remove the thermal history and then gradually cooling the sample until it no longer pours.The beneficiated method is usually the most applicable for crude production situations where the crude is flowing hot and

then allowed to cool when flow is stopped.

PressureDrop(psig)

50

450

150

250

350

FlowRate(MBOPD)

0

80

20

40

60

70

10

30

50

InletCrudeTemperature(F)

0

200

50

100

150

Hydraulic Change Due to Pigging

Time (days)0 4 8 12 16 20 24 28 32

PressureDrop(psig)

50

450

150

250

350

FlowRate(MBOPD)

0

80

20

40

60

70

10

30

50

InletCrudeTemperature(F)

0

200

50

100

150

Hydraulic Change Due to Pigging

Time (days)0 4 8 12 16 20 24 28 32

Crude Property Measurement Technique Values

API @ 60 F Anton Paar DMA 5000 45

Density (g/cc) Anton Paar DMA 5000 0.8

Cloud Point (WAT) Cross-Polarized Microscopy 58C (136F)

Anton Paar DMA 5000 57C (135F)

TU MSI Model 55.5C (132F)

WAT in Pipeline Live Oil TU MSI Model 55C (131F)

STO Pour Point (PPT)ASTM D5853-95 Beneficiated

Method29C (85F)

Wax Content (% wt) HTGC n-C19+ 17

Asphaltene Content (%) IP 143 0.03


4/19

4 OTC 21641

0.01

0.1

1

10

0 10 20 30 40 50 6

Carbon #

Wt%

0

All Hydrocarbons

n-Alkanes

Figure 3. Weight Percent Distribution of n-Alkanes and All Hydrocarbons

Viscosity of the Stock Tank Oil

Haake RS 150 Rheometer with a rotational pressure cell has been used to measure the viscosity of the crude saturated with

separator gas at 150 psig to simulate the pipeline operating conditions. Figure 4 shows the measured viscosity data for thelive crude with some gas dissolved at different shear rates ranging from 30 and 1,000 1/sec. It can be noted that the viscosity

of the crude begins to rapidly increase when the crude cools below 60C and even more rapidly below 35C. The fluid

behaves almost Newtonian at temperatures above 60C. However, the fluid turns non-Newtonian below WAT due to the

presence of the wax crystals. The default viscosity predicted by the TUWAX program matches very well with the single

phase viscosity of the crude above 60C. Below 60C, the presence of wax crystals changes the rheology of the slurry by

making it highly viscous non-Newtonian fluid. The viscosity of the continuous medium of the slurry is expected to follow

the default viscosity predictions below 60C.

Figure 4. Viscosity vs. Temperature of Live Crude

Figure 4 shows the viscosity predictions by TUWAX using default viscosity option as a function of temperature along with

the measured viscosity. The predicted viscosity values match well with the measured data above 60C. The program is nottaking into consideration the effect of the wax crystals on the bulk viscosity of the slurry.

Trapped Oil Content of Deposit

Several samples of the wax deposit collected from the pig trap were analyzed using HTGC for the wax content and the

trapped oil content of the deposit. The deposit seems to have the consistency of a shoe polish and melts at temperature well

above the WAT of the crude. Melting points of the deposit samples range from 95-100C. Figure 5 shows the percent waxcontent of the deposit.


5/19

OTC 21641 5

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7

# of Samples

WaxContentinPigSamples

Figure 5. Percent Wax Content of the Deposits

TUWAX Thermodynamic ModelingTUWAX uses HTGC analysis results of the sample as input to develop a thermodynamic model of the waxy crude oil and

identify which carbon number n-alkanes will precipitate at a given temperature and pressure if gas is dissolved in solution.

The WAT is defined as the temperature and pressure at which 0.02 mole% of the crude precipitate out as the solid state. TheWAT results between the thermodynamic and cross-polarized microscopy technique agree within 2 C (Table 1).

Live Oil Composition

In order to study the behavior of the crude in the subsea pipeline, the composition of the live oil has been estimated.

Depending on the pipeline operating conditions, the liquid phase may contain some light ends that are not present in the stocktank sample. It is important to estimate the additional light ends that will be present in the pipeline fluid relative to the stock

tank sample since they may have some effect on the wax deposition rate. The general procedure used was to develop a good

thermodynamic model of the reservoir fluid and then flash it to the separator pressure and temperature conditions to estimate

the composition of the pipeline fluid. Figure 6 shows the analysis of the separator oil composition obtained at 150 psig and

60F from TUWAX program. This live oil is expected to contain 2.3% of light C1-C4 components that are missing in thestock tank analysis.

0

1

10

C1 C3 C5 C7 C9 C11 C13 C15 C17 C19 C21 C23 C25 C27 C29Carbon Number

WtFraction(%)

Live Oil (PVT EOS Prediction)Dead Oil (HTGC Analysis)

Figure 6. Live and Dead Oil Compositions

Wax Precipitation vs. Temperature

Figure 7 shows the TUWAX predictions of solid weight fractions as a function of temperature. Note that the precipitated

solid fraction of 0.02 mol% was predicted at the WAT of 55C. The curve increases rapidly below 35C. The fraction of

solids at the pipeline exit temperature of 29C is predicted to be ~6 wt%. The presence of 6 wt% solid crystals results in anincrease in the bulk viscosity of the slurry by one to two orders of magnitude.


6/19

6 OTC 21641

SOLID WEIGHT FRACTION IN TOTAL FLUID

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 10 20 30 40 50 60 70 8

Temperature (deg.C)

Solidswtfra

ction

WAT = 57oC (by DMA)

WAT = 58-59oC (by CPM)

0

WAT = 55oC

Pour Point

Wax Content

SOLID WEIGHT FRACTION IN TOTAL FLUID

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 10 20 30 40 50 60 70 8

Temperature (deg.C)

Solidswtfra

ction

WAT = 57oC (by DMA)

WAT = 58-59oC (by CPM)

0

WAT = 55oC

Pour Point

Wax Content

Figure 7. Solid Weight Fraction vs. Temperature

Model Sensitivity StudyTUWAX simulator has both EM and FMT models available for predicting wax deposition. In this section, a sensitivity study

is performed to understand the impact of various variables on the wax deposition results in the two models. Figure 6 shows

various options available in the TUWAX for user to set up a wax deposition model. A user can select either EM or FMT

model. The simulator allows user to enter the experimental viscosity data as well as let the user use default viscosity fromthe lookup table generated by the thermodynamic model. Oil fraction in the deposit can be entered by the user or can be

calculated using Singh et al.Aging Model (2000). Singhs model requires user to enter a fitting parameter Ka,which depends

on the aspect ratio of the wax crystal. For shear stripping, two separate options are provided based on Venkatesan (2003) and

Matzain (1998) models.

Figure 8. Input Options Available in TUWAX Simulator

Equilibrium Model (EM)

A base case wax deposition model within TUWAX has been run for viscosities of 1, 10, 100 cP. Figure 9 shows the waxdeposit thickness results after 10 days for the three viscosity cases. There is a significant reduction in the deposit thickness as

the viscosity of the crude is increased. In order to further quantify the effect of the viscosity, peak deposit thickness after 10

days is plotted as a function of the viscosity in Fig. 10.

A trend line with a power law describes the predicted results. Both the temperature gradientinterface

drdT and solubility

slopeiT

eqm

wdTdC at the 10km location have been found to be a weak function of the viscosity (Fig. 11). Thus, the impact of

viscosity is primarily coming from the diffusivity.


7/19

OTC 21641 7

Equilibrium Model Prediction after 10 days

0

2

4

6

8

10

12

0 5 10 15 20 25

Pipeline Location (km)

Depositthick

ness(mm)

Viscosity = 1cP

Viscosity = 10cP

Viscosity = 100cP

Figure 9. Wax Thickness vs. Distance as Function of Viscosity using EM

y = 10.134x-0.7773

R2= 0.9997

0

2

4

6

8

10

12

1 10 100

Viscosity (cP)

DepositThickness(mm)

Figure 10. Peak Deposit Thickness vs. Viscosity after 10 days of Deposition Using EM

Variables computed at 10km location

y = 0.0022x-0.1219

R2= 0.8186

y = 1837.8x0.0425

R2= 0.9864

0

500

1000

1500

2000

2500

0 20 40 60 80 100 120

Viscosity (cP)

RadialTempGra

dient(F/m)

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

0.005

dC/dT(1

/F)

Radial temp gradient at the interface

dC/dT at the interface

Power (dC/dT at the interface)

Power (Radial temp gradient at theinterface)

Figure 11. Radial Temperature and Concentration Gradients vs. Viscosity


8/19

8 OTC 21641

Film Mass Transfer Model (FMT)

TUWAX has been run for viscosities of 1, 10, 100 cP. Figure 12 shows the wax deposit thickness results after 2 days for the

three viscosity cases. There is a significant reduction in the deposit thickness as the viscosity of the crude is increased.

FMT Model Prediction after 2 days

0

2

4

6

8

10

12

0 5000 10000 15000 20000 25000


Depositthickness(mm)

Viscosity = 1cP

Viscosity = 10cP

Viscosity = 100cP

Figure 12. Wax Thickness vs. Distance as Function of Viscosity using FMT.

In order to further quantify the effect of the viscosity, peak deposit thickness after 2 days is plotted as a function of the

viscosity in Fig. 13. A trend line with a power law perfectly describes the predicted results. The concentration driving force

varies significantly as a function of the viscosity (Fig. 14). Thus, in FMT model also, the impact of the viscosity is primarily

coming from the diffusivity.

Diffusivity Independent of Bulk Viscosity

As seen in the measured viscosity data, bulk viscosity of the waxy oil slurry below the WAT can be orders of magnitude

higher than the viscosity of oil in the continuous medium. The diffusion of wax occurs through the continuous media and is

slightly hindered by the presence of wax crystals because only 4-6% of the crystal volume fraction is occupied by the waxcrystals. Therefore, diffusivity of the wax molecules should depend on the viscosity of the continuous medium and should

not decrease orders of magnitude when the bulk viscosity of the waxy oil slurry increases by orders of magnitude.

y = 11.586x-0.5082

R2= 0.9962

0

2

4

6

8

10

12

14

1 10 100

Viscosity (cP)


Figure13. Peak Deposit Thickness vs. Viscosity after 2 days of Deposition Using FMT


9/19

OTC 21641 9

Concentration Driving Force Calculated at a Bulk Temp of 92oF

y = 0.0011x0.4702

R2= 0.9976

0

0.002

0.004

0.006

0.008

0.01

0.012

0 20 40 60 80 100

Viscosity (cP)

CondentrationDifferenc

e(bulk-wall)

120

Figure 14. Concentration difference vs. Viscosity using FMT.

Oil Content of Deposit:Wax deposits contain a certain amount of trapped oil inside the network of wax crystals. The fraction of trapped oil dependson the flow regime and it can range from 20 to 90% (Lund, 1998). Turbulent flow with high shear stress at the wall tends to

produce the deposit with lower trapped oil fraction. The fraction of trapped oil also decreases with time (also known as aging

of the deposit).

Equilibrium Model (EM).

TUWAX was run with varying the trapped oil fraction of the deposit. Figure 15 shows the wax deposit thickness results after

10 days for the three trapped oil fraction cases. There is a significant reduction in the deposit thickness as the trapped oil

fraction of the crude is reduced. In order to further quantify the effect of the trapped oil fraction, peak deposit thickness after

10 days is plotted as a function of the trapped oil fraction in Fig. 16.

Film Mass Transfer Model (FMT)

TUWAX was run with varying the trapped oil fractions. Figure 17 shows the wax deposit thickness results for the threetrapped oil fraction cases. There is a significant reduction in the deposit thickness as the trapped oil fraction is decreased. Inorder to further quantify the effect of the trapped oil fraction, peak deposit thickness after 2 days is plotted as a function of

the oil fraction in Fig. 18.


0

2

4

6

8

10

12

0 5 10 15 20 25



Trapped Oil Fraction = 0.8



Figure 15. Wax Deposit Thicknesses after 10 Days for Three Trapped Oil Fractions Using EM


10/19

10 OTC 21641

0

2

4

6

8

10

12

14

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

DepositThic

kness(mm)

y = 2.1038x(1-)-1

Figure 16. Peak Wax Deposit Thicknesses after 10 Days vs. Oil Fractions Using EM


0

2

4

6

8

10

12

14

0 5 10 15 20 25






Figure 17. Wax Deposit Thicknesses after 2 Days for Three Trapped Oil Fractions Using FMT

0

2

4

6

8

10

12

14

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

DepositThick

ness(mm)

y = 2.2848x(1-)-1

Figure 18. Peak Wax Deposit Thicknesses after 2 Days vs. Oil Fractions Using FMT


11/19

OTC 21641 11

Thermal Conductivity of Deposit

Since wax deposits contain trapped oil/water and other solids including reservoir fines, scale and corrosion products, thermal

conductivity of the deposits are usually higher than the calculated value. Previously, it was assumed the deposit thermalconductivity to be equal to the oil thermal conductivity (Svendsen (1993), Niesen et al. (1993)). Lund (1998) showed that

the thermal conductivity of the deposit can range from the oil thermal conductivity to 6 times the oil thermal conductivity.

Apte (1999) showed that the assumption made by the previous researchers (Svendsen (1993), Niesen et al.(1993)) may be

valid for initial soft deposits, however, the thermal conductivity of aged deposits could be as high as 2 times that of the oil. A

sensitivity analysis has been performed using both the EM and FMT models by varying the thermal conductivity from thedefault value to user defined values of 1, 2, and 3. Figures 19 and 20 show the results obtained from the EM and FMT

models, respectively. The deposit thickness predictions seem to be not a strong function of the thermal conductivity.

0

2

4

6

8

10

12

0 5 10 15 20 25Distance (km)

W

axThickness(mm)

Default (Maxwell Corr.)

User defined = 1

User defined = 2User defined = 3

Figure 19. EM Predictions After 10 Days

0

2

4

6

8

10

12

14

0 5 10 15 20 25Distance (km)

WaxThickness

(mm)

Default (Maxwell Corr.)

User defined = 1

User defined = 2

User defined = 3

Figure 20. FMT Predictions after 2 days

Aging Model by Singh et al., 2000TUWAX was run with the Singhs aging model by varying Ka for wax aspect ratio. Figures 21-22 show the results of wax

thickness and wax content after 10 days using EM model. As Ka is increased, the wax content of the deposit decreases

resulting in lower deposit thickness. The wax content from the field deposit sample collected from the pig trap ranges from

25-30 wt%. This means that the tuned value of Ka should be close to 25 for the EM model.Figures 23 and 24 show the results of thickness and wax content after 10 days using the FMT model. It is clear as the Ka

is increased, the wax content of the deposit decreases resulting in lower deposit thickness. The wax content from the field

deposit sample collected from the pig trap ranges from 25-30 wt% indicating that the tuned value of Ka should be close to 1

for the FMT model.

Shear Removal.

TUWAX has been run with the shear removal options by selecting Matzain and Venkatesan models. Figure 25 summarizes

the results for the three cases: no shear removal, Matzains model, and Venkatesans model (a=2e-10 and b=1.9). Matzains


12/19

12 OTC 21641

model has very high shear removal rate resulting in a significant lower deposit thickness. The value of parameter a needed

to be increased to 2.0e-10 (from the default value of 0.8e-12) to bring the shear removal rate to about 50%.

0

1

2

3

4

5

6

7

8

0 5 10 15 20 25Distance (km)


Ka = 1Ka = 5Ka = 10Ka = 25

Figure 21. EM Predictions after 10 Days

Figure 22. EM Predictions After 10 Days

Ka

= 1

Ka

= 5

Ka

= 10

Ka

= 25

Ka

= 1

Ka

= 5

Ka

= 10

Ka

= 25

0

5

10

15

20

25

30

0 5 10 15 20 25Distance (km)

DepositThick

ness(mm)

Ka = 1Ka = 5Ka = 10Ka = 25

Figure 23. FMT Predictions after 10 Days


13/19

OTC 21641 13

Figure 24. FMT Predictions After 10 Days

Ka

= 1

Ka

= 5

Ka

= 10

Ka

= 25

Ka

= 1

Ka

= 5

Ka

= 10

Ka

= 25

0

2

4

6

8

10

12

14

16

0.00 5.00 10.00 15.00 20.00 25.00

Location (km)


No Shear Removal

Matzain Model

Venkatesan Model (a=2.0e-10, b =1.9)

Figure 25. Shear Effect Predictions from EM model

Figures 26 and 27 show the results of a parametric study to analyze how the values of parameter a and b affect the

shear removal rate in the field pipeline.

0

20

40

60

80

100

120

1.0E-13 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08

Value of Parameter 'a'

ShearRemoval(%)

Venkatesan's Model b = 3.0Venkatesan's Model b = 1.9Venkatesan's Model b = 1.0

Figure 26. Venkatesan Model Parameter a Sensitivity.


14/19

14 OTC 21641

0

20

40

60

80

100

120

140

0.0 1.0 2.0 3.0 4.0 5.0

Value of Parameter 'b'

ShearRe

moval(%)

Venkatesan's Model a = 1.0e-9Venkatesan's Model a = 2.0e-10Venkatesan's Model a = 2.0e-11

Figure 27. Venkatesan Model Parameter b Sensitivity.

To get some reasonable rate of the shear removal, the value of a should be at least two orders of magnitude greater thanthe default value of 0.8e-12.

Analysis of the Field DataExport pressure and temperatures and crude oil flow rates were monitored at CPP while the arrival pressure was measured at

the FPSO. The pipeline operating data obtained from the field has been summarized in Fig. 2. The export temperature at theCPP was maintained near 165F. The arrival pressure at the CPP was maintained around 350 psig. The temperature of the

pipeline fluid arriving at the FPSO ranged between 80 and 85F. Table 3 provides some of the pipeline design parameters.

Average seabed temperature is 77F. Temperature of crude oil entering the pipeline has been around 165F. Thermalconductivity of the steel pipeline is 44 W/m/K with a clean pipe roughness of 50 m.

Table 3. External Heat Transfer Coefficient (EHTC) Used in Simulations for Non Insulated Half BurriedPipeline

Pipeline Segment

(CPP Seafloor to base

of Flexible Riser)

Segment Length (m) Segment ID (mm) Segment OD (mm) EHTC* (W/m2/K)

23,000 305 324 (12.75 in.)22 (50 mm concrete over

steel)

* Computed for Heat Flux at Steel Pipe OD

Pressure Drop Study

To quantify the effect of wax deposition on the pressure drop of the pipeline, Blasius (1913) friction factor correlation is

selected based on the range of the Reynolds number (104


15/19

OTC 21641 15

Figure 28. and Liquid Flow Rate History Plot

Figure 29. Deposit Roughness Effect

Average Deposit Thickness Calculation

The term is plotted in Fig. 30 for seven pig runs and there is a reasonable reproducibility from run to run. The run 7 shows a

slight decrease in the average deposit thickness after day 14th and 17th. This could be due to some changes in the productionrate and/or pipeline operating conditions.

The average thickness of the wax deposit can be calculated from Eq. 23 as follows;

( )

( )

( )

( )

=

==

75.4/1

75.4

75.4

75.4

75.4

122

2

2

2

ooio

i

o

i

i

o

i

o

RtR

R

R

R (4)

Using the above analysis, average thickness of the wax deposit has been calculated and shown in Fig. 31. The average

thickness increases rapidly initially at 3-4 mm/day and then the deposit growth rate significantly slows down.

0

10000

20000

30000

40000

50000

60000

70000

1-Jul-08 31-Jul-08 30-Aug-08 29-Sep-08 29-Oct-08 28-Nov-08 28-Dec-08

Date

LiquidRate(BPD

)

0

50

100

150

200

250

300

350

400

450

500

DP/vo^1.75

0

10000

20000

30000

40000

50000

60000

70000

1-Oct-08 16-Oct-08 31-Oct-08 15-Nov-08 30-Nov-08

Date

LiquidRate(BPD)

0

50

100

150

200

250

300

350

400

450

500

DP/vo^1.7

5


16/19

16 OTC 21641

100

120

140

160

180

200

220

240

260

280

300

0 5 10 15 20 25

Time (days)

Kappa(psi/(m/s

)^1.75)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7

Figure 30. Reproducibility between Pig Runs.

Figure 31. Average Deposit Thickness vs. Time Based on Pressure Drop Measurements

Result and Discussion Comparison of Model Predictions with the Field Data

0

5

10

15

20

25

30

0 5 10 15 20 25

Time (days)

Aver

ageThickness(mm)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7

Predictions were made using several options available in the TUWAX simulator (Fig. 32). It can be seen that the predictionsfrom both the EM and FMT models with the experimental viscosities of the crude are significantly lower as compared to the

deposit thickness obtained from the field data. The average trapped oil fraction of 0.72 is used as found in the field deposits

collected from the pig. Using default viscosities, the predictions from both of the models increased significantly with the

FMT model predictions closer to the field data. Singh aging model was run with the FMT model and the parameter Ka wasvaried to match the wax content data measured from the pig trap solids. Figure 33 shows the wax content data as a function

of time. It was found that Ka=1 gave a realistic predictions of the wax content of the deposit during the aging process.

The deposit thickness trend calculated by the FMT model with Singhs aging model was shown in Fig. 34. There is no

significant difference between the wax deposition results obtained from either the constant assumption or the Singh aging

model assumption. Thus, the value of Ka=1 gave a realistic predictions of both the deposition rate and wax content of the

deposit during the aging process.


17/19

OTC 21641 17

Figure 32. Comparison of Model Predictions with Field Data

0

5

10

15

20

25

30

0 5 10 15 20 25

Time (days)

AverageThick

ness(mm)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7FMT Model, =0.72

default

, No aging

Figure 33. Aging Predictions Using Singh et al.(2000) Model

Figure 34. Aging vs. No Aging Predictions Using Singh et al. (2000) Model

EM Model, =0.72

default, No aging

EM Model, =0.72

exp, No aging

FMT Model, =0.72

exp, No aging

0

5

10

15

20

25

30

0 5 10 15 20 25

Time (days)

AverageThick

ness(mm)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7FMT Model, =0.72

default

, No aging

EM Model, =0.72

exp, No aging

EM Model, =0.72

default, No aging

FMT Model, =0.72

exp, No aging

0

5

10

15

20

25

30

0 5 10 15 20 25

Time (days)

AverageThic

kness(mm)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7

FMT Model, =0.72

default, No aging

FMT Model, default,

Singh aging Ka=1

0

5

10

15

20

25

30

0 5 10 15 20 25

Time (days)

AverageThic

kness(mm)

Run 1

Run 2

Run 3

Run 4

Run 5

Run 6

Run 7

FMT Model, =0.72

default, No aging

FMT Model, default,

Singh aging Ka=1


18/19

18 OTC 21641

Summary and ConclusionsIn this study, field data from an Indonesian offshore production and crude oil pipeline system have been utilized to

investigate the wax deposition buildup. The performance of the existing models available in the TUWAX has been assessedand recommended practices and improvements have been proposed. Table 4 provides a summary of the results.

The specific conclusions are provided below:

Using measured data of the bulk crude oil viscosity below its WAT, the predicted deposition rates from both theEquilibrium EM model and Film Heat Transfer FMT model with no shear removal have been found much lower than

deposition rates obtained from the field data. Using default viscosity, both EM and FMT models gave reasonable predictions of the wax deposition rate as compared

to deposition rate obtained from the field data.

The field deposition rate is higher than the predictions by the EM model without any shear removal. By incorporating aging process to the FMT model, it was found that the value of Kaparameter of 1 matches the deposit

wax content observed in the field.

Predictions by the FMT model using the default viscosity with Singh aging model (Ka=1) and no shear removal showeda reasonable match to the field data including both deposit thickness and wax content of the deposit.

Matzain shear removal model significantly over-predicts the impact of shear on the rate of wax deposition; however,Venkatesans shear removal model (with the published values of parameters a and b) shows an insignificant impact of

the shear on the deposition rate.

Table 4: Validation of Wax Deposition Models / Options in TUWAX

DepositionModel Viscosity Aging

ShearRemoval

Comparison to fielddata

EM Measured No no significantly lower

FMT Measured No no significantly lower

EM Default No no somewhat lower

FMT Default No no reasonable Match

FMT Default Singh no reasonable match

Learning from this StudyBased on the results obtained from this study, several learning items have been captured for a possible future study.

1. Diffusivity of Wax MoleculesThe experimental measured viscosity of the crude is orders of magnitude higher as the temperature decreases below the wax

appearance temperature as a result of the formation of oil-wax crystals slurry. TUWAX assumes that this bulk viscosityshould be entered into the molecular diffusivity equation. However, since the wax molecules diffuse through the continuous

medium of de-waxed oil, the viscosity of the continuous medium should be considered for the diffusivity calculations

(Hayduk and Minhas, 1982). Thus, the dependence of diffusivity of wax molecules on crude viscosity below its waxappearance temperature needs to be revised. For a crude oil with low pour point, the difference between the viscosity of bulk

fluid and viscosity of the continuous liquid medium of the slurry may not significantly impact the deposition rate. A

recommendation is made to consider diffusion inside wax slurry as diffusion through the continuous (de-waxed) liquid phase

with a slight hindrance from the precipitated crystals.

2. Shear Stripping/Removal ModelsThe prediction of wax deposition rate has been reasonable without considering any shear stripping removal. Matzains shear

removal model showed a much significant reduction in the deposit thickness. Additionally, the parametric studies show that

a and b parameters of Venkatesans shear removal model needs to be verified for the field scale production pipelines. It isnot appropriate to use the default values of a and b parameters developed based on the lab results (Venkatesan, 2003).

3. Field Validation of More Oil FieldsThere is a need for more field data to generalize wax prediction characteristics. An effort is being made to analyze other fieldsystems with wax deposition challenges and compare the field data with the model predictions.

Nomenclaturea exponent in shear removal correlation with default value of 0.8 e-12

b exponent in shear removal correlation with default value of 1.9

concentration of wax in the liquid phase at a temperature under thermodynamic equilibriumeqm

wC

interface

drdCw

radial concentration gradient

interface

Eqm

wC thermodynamic equilibrium concentration of wax in the liquid phase at deposit interface temperature

D molecular diffusivity of wax in the oil


19/19

OTC 21641 19

f friction factor

k thermal conductivity of the oil

L length of the pipeline

r radial location near the deposit interface

Re Reynolds number

Ri effective radius of the pipeline with the deposition

clean pipeline radiusoi

R

T temperature of the fluid at a radial location rTi temperature at the deposit interface

v fluid velocity in the pipeline

Ka fitting parameter in aging model

Greek symbol

deposit thickness

o oil content of the deposit viscosity of the crude oil

density of the oil parameter defined in Eq. 3

References

1. Alboudwarej, H., Hou, Z., and Kempton, E.: Flow-Assurance Aspects of Subsea Systems Design for Production of Waxy CrudeOils, SPE 103242 (2006)

2. Apte, M.: Investigation of Paraffin Deposition during Multiphase Flow in Pipelines and Wellbores,, MS Thesis, University ofTulsa, (1999)

3. Bagatin, R., Busto, C., Correra, S., Margarone, M., and Carniani, C.: "Wax Modeling: There is Need for Alternatives," SPE115184 (2008)

4. Blasius, P. R. H.: Das Aehnlichkeitsgesetz bei Reibungsvorgangen in Flssigkeiten, Forschungsheft 131, (1913), 1-415. Brown, T. S., Niesen, V. G. and Erickson, D. D.: Measurement and Prediction of the Kinetics of Paraffin Deposition, Proc.

SPE Technical Conf. and Exhib., Houston, (1993) p. 353.6. Bruno, A., Sarica, C., Chen, M., and Volk, M.: Paraffin Deposition during the Flow of Water-in-Oil and Oil-in-Water

Dispersions in Pipes, SPE ATCE held in Denver, Colorado, USA, SPE -114747-PP (2008)

7. Coutinho, J. A. P. and Ruffier-Meray, V.: Experimental Measurements and Thermodynamic Modeling of Paraffinic WaxFormation in Undercooled Solutions, Ind. Eng. Chem. Res., 36, (1997) 49774983

8. Couto, G. H., Chen, H., Delle-Case, E., Sarica, C., and Volk, M.: An Investigation of Two-Phase Oil-Water ParaffinDeposition, OTC-17963-PP (2006)

9. Espinoza, G. M. S.: Investigation of Single-phase Paraffin Deposition, MS Thesis, The University of Tulsa (2006)10. Hayduk, W., and Minhas, B. S.: Correlations for Prediction of Molecular Diffusivities in Liquids, Can. J. Chem. Eng., 60

(1982), 29511. Hernandez-Perez, O. C.: Investigation of Single Phase Paraffin Deposition Characteristics, MS Thesis, The University of Tulsa

(2002)12. Hoffmann, R. and Amundsen, L.: Single-Phase Wax Deposition Experiments, Energy and Fuels, 24 (2010), 1069108013. Hsu, J. J. C., Lian, S. J., Liu, M., Bi, H. X. and Guo, C. Z.: "Validation of Wax Deposition Model by a Field Test", SPE 48867

(1998)

14. Labes-Carrier, C., Rnningsen, H. P., and Kolnes, J.: Wax Deposition in North Sea Gas Condensate and Oil Systems:Comparison Between Operational Experience and Model Prediction, SPE 77573 (2002)

15. Lee, H. S.: Computational and Rheological Study of Wax Deposition and Gelation in Subsea Pipelines, Ph.D. Thesis,University of Michigan, Ann Arbor, Michigan (2008)

16. Lira-Galeana, C., Firoozabadi, A., and Prausnitz, J. M.: Thermodynamics of Wax Precipitation in Petroleum Mixtures, AIChEJ., 42 (1996), 239248

17. Lund, H. J.: Investigation of Paraffin Deposition during Single-Phase Liquid Flow in Pipelines, MS Thesis, University ofTulsa, (1998)18. Matzain, A., Apte, M. S., Zhang, H. Q., Volk, M., Redus, C. L., Brill, J. P., and Creek, J. L.: Multiphase Flow Wax Deposition

Modeling, Proceedings of ETCE2001, Houston, Texas, February 5-7 (2001) 92719. Matzain, A., Apte, M., Delle-Case, E., Brill, J.P., M. Volk, M. and Wilson, J. Creek, J. and Chen, X. T.: Design and Operation

of a High Pressure Paraffin Deposition Flow Loop, NACMT, Banff, Alberta, Canada, 10-11 June (1998)20. Niesen, V.G., Brown, T. S., and Erickson, D. D.: Thermodynamic Measurement and Prediction of Paraffin Precipitation in

Crude Oil, SPE 26604 (1993)21. Singh, P., Venkatesan, R., Fogler, H.S. and Nagarajan, N.: Formation and Aging of Incipient Thin Film Wax-oil Gels, AIChE

J., 46(5), (2000), 1059-107422. Svendsen, J. A.: Mathematical Modeling of Wax Deposition in Oil Pipeline Systems, AIChE Journal, 39, Issue 8 (1993) 1377-

138823. Venkatesan, R.: The Deposition and Rheology of Organic Gels, Ph.D. Thesis, University of Michigan, Ann Arbor, Michigan

(2003)

Flow Assurance Validation of Wax Deposition Models Using Field Data- OTC-2011

Documents

Transcript of Flow Assurance Validation of Wax Deposition Models Using Field Data- OTC-2011