Final Dissertation 1.pdf

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INTELLIGENT WELL APPLICATIONS IN PRODUCTION

WELLS

By

ADEKUNLE, OLAJIDE ADEREMI, B. Eng

A dissertation submitted in partial fulfilment of the requirements of the award of

Master of Science in Oil and Gas Engineering at the University of Aberdeen

(September, 2012)


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ABSTRACT

With the combined force of population outburst and industrialization, the demand for energy

sources is at an all time high, with hydrocarbon fuel sources topping the charts. The

conventional methods of producing oil and gas are no longer sufficient to meet up withdemand, and there has been an increased migration to unconventional methods for the last

few decades.

A practical solution to minimizing this problem is to increase oil production and recovery

factor from new and existing oil and gas fields. This project aims to model the application of

Intelligent Well Completion (IWC) technology using the Schlumberger Eclipse simulator. To

accomplish this target, the well design of a Chevron Field "X" is considered from the drilling

to the final completion phases, and optimised every step of the way. The Intelligent Well

Inflow Control Devices and downhole sensors are simulated for. The ICD offers the ability to

open and close sections of the borehole, while the downhole sensors monitor the borehole

and reservoir properties, providing better reservoir management and early anomaly

detection.

Based on the analysis of the simulation results, risk management, sensitivity analysis and

other procedures, it was noted that the IWC technology presented an increase in oil

production by approximately 50%, a higher return on investment, low attached risks within

the ALARP region, a high reliability, and a minimal water production (water cut < 90%)

amongst other added values, thus proving the multiple benefits of IWC adaptation.

Although inherent challenges still need to be overcome, with ample room for growth and

development, the Intelligent Well Completion technology has the potential to alleviate the

crippling challenge of the shortage of hydrocarbon energy sources.


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DEDICATION

I dedicate this work to God, who graciously seen me through the travails and challenges I

have encountered thus far. Glory be to His name.


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ACKNOWLEDGEMENT

I wish to thank my parents, Dr. and Mrs. Adekunle, for their support, material and otherwise.

I acknowledge Chevron upstream Europe, and my industrial supervisor, Mr Nick Waters, for

his tutelage and guidance through the quagmires I faced in executing this project.

I also want to thank Dr Thangavel Thevar, the course coordinator and Professor Gorry

Fairhurst, my academic supervisor, for solving the challenges I faced due to software

licensing.

I am grateful to Dr. Orodu. He was of extreme help in proof reading my thesis, and pointing

out errors along the way.

I thank all my friends that showed me love and support during the execution of this project.

Last and most of all, I thank God for seeing me through this stage of my academics, I couldn't

have done it without Him.


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TABLE OF CONTENTS

CONTENTS PAGE

TITLE PAGE I

ABSTRACT II

DEDICATION III

ACKNOWLEDGEMENT IV

TABLE OF CONTENTS V

LIST OF TABLES

LIST OF FIGURES

ABBREVIATION

1. INTRODUCTION

1.1 Introduction 1

1.2 Statement of the Problem 3

1.3 Objectives 3

1.4 Scope of Work and Limitations 4

1.5 Outline of Report 5

2. LITERATURE REVIEW

2.1 General Overview 6

2.2 Case Studies 6

2.3 Commingled Flow 9

2.4 Single and Multilateral Wells 10

2.5 Drilling Inclination 11

2.6 Inflow Control Devices 12

2.7 Downhole Sensors 13

2.8 Risk and Reliability 13

2.9 Economics 14

2.10 Reservoir Characterisation 15

2.11 Reservoir Heterogeneity 16


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2.12 Enhanced Oil Recovery 16

2.13 Water Production 17

2.14 Reactive or Proactive 18

2.15 Benefits of Intelligent Well 19

2.16 Challenges Facing IWC 20

2.17 Focus of Present Work 20

3. METHODOLOGY

3.1 Introduction 21

3.2 Reservoir Simulation 22

3.2.1 Schlumberger Eclipse Simulator 23

3.3 Base Case Scenario Field Model 24

3.3.1 RUNSPEC 24

3.3.2 GRID 25

3.3.3 EDIT 25

3.3.5 REGIONS 26

3.3.6 SOLUTION 26

3.3.7 SUMMARY 26

3.3.8 SCHEDULE 27

3.4 Error Avoidance 28

3.5 Intelligent Well Modifications 28

3.5.1 Flow Rate 28

3.5.2 Well Location 29

3.5.3 Well Inclination 30

3.5.4 Well Contact Area 30

3.5.5 Multilateral Well 31

3.5.6 Inflow Control Devices 31

3.5.7 Effect of Reservoir Variability 31

3.5.7.1 Reservoir Heterogeneity 32


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3.5.7.2 Thief Zones 32

3.5.7.3 Minor Isolated Zones 32

3.5.8 Economic Evaluation 33

3.5.9 Sensitivity Analysis 35

3.5.10 Risk Identification and Reliability 36

3.5.11 Proactive Algorithm 37

4. RESULTS AND DISCUSSIONS

4.1 Overview 38

4.2 Base Case Scenario 38

4.3 Adopting Intelligent Well Completions 39

4.3.1 Flow Rate 39

4.3.2 Well Location 41

4.3.3 Well Inclination 44

4.3.4 Well Contact Area 46

4.3.5 Reservoir Variability 47

4.3.5.1 Effect of Thief Zone 47

4.3.5.2 Effect of Isolated Layers 49

4.3.6 Multilateral Well 51

4.3.7 Inflow Control Devices 53

4.3.8 Economics 55

4.3.9 Sensitivity Analysis 57

4.3.10 Risk Identification and Reliability 61

4.3.11 Proactive Algorithm 64

5. CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions 66

5.2 Recommendations 67

REFERENCES 68

APPENDICES 73


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LIST OF TABLES

Table Page

3.1 Initial Reservoir Properties 21

3.2 SUMMARY output keywords 27

3.3 Field Economics Value 34

3.4 Breakdown of Fields CAPEX 34

3.5 Sensitivity Variations 35

4.1 Field Production Outputs of Base Case Scenario 39

4.2 Field Production Outputs using Trial and Error method 40

4.3 Field Output Data of Well Located in the Various Layers 42

4.4 Field Output Data of the Various Completion Methods 45

4.5 Field Output Data of Conventional and ERD wells 46

4.6 FOPT and FWPT from horizontal and Deviated wells with thief zone effect 49

4.7 FOPT and FWPT from horizontal and Deviated wells with Isolated zone effect 51

4.8 Field Output Data of Multilateral and ERD wells 53

4.9 Field Output Data of Multilateral Well and ICD Multilateral Well (CECON) 55

4.10 Result of Economic Analysis 56

4.11 Field Data Output from Sensitivity Analysis (Well Diameter) 57

4.12 Field Data Output from Sensitivity Analysis (Oil Price) 58

4.13 Field Data Output from Sensitivity Analysis (Water Cut Limit) 59

4.14 Field Data Output from Sensitivity Analysis (Varying Skin) 60

4.15 Risk Management 61

I1 Economic Analysis of Base Case Scenario 86

I2 Economic Analysis of Multilateral Well 87

I3 Economic Analysis of ICD Well 88


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LIST OF FIGURES

Figure Page

1.1 An Intelligent Well Completion System 2

2.1 Multilateral Well 11

2.2 Natural Gas-Lift Intelligent Completion 17

4.1 Field Production Rate of Base Case Scenario 38

4.2 Field Production Total of Base Case Scenario 39

4.3 Comparison of Field Production Total with Varying Flowrates 40

4.4 Comparison of FOPT and FOPR of 10,000stb/day and BHP Constraints 41

4.5 Comparison of Field Oil Production Total of wells located in various layers 42

4.6 FloViz Image of Base Case Scenario Well located in Layer 9 43

4.7 FloViz Image of Optimal Well located in Layer 13 43

4.8 FOPR of the various Well Inclinations 44

4.9 FOPT of the various Well Inclinations 45

4.10 FOPR and FOPT of Conventional Horizontal Well and ERD 46

4.11 FloViz Image of ERD Wells 47

4.12 Plot Showing Effect of Thief Zone on FOPT from Horizontal and Deviated Wells 48

4.13 Plot Showing Effect of Thief Zone on FWPT from Horizontal and Deviated Wells 48

4.14 Plot Showing Effect of Isolated Zone on FOPT from Horizontal and Deviated Wells 50

4.15 Plot Showing Effect of Isolated Zone on FWPT from Horizontal and Deviated Wells 50

4.16 FloViz Image of the dual Injector Multilateral Field Plan 52

4.17 FOPR and FOPT of Multilateral and ERD Wells 52

4.18 FOPR and FOPT of Multilateral well and ICD Multilateral well (CECON) 54

4.19 FWPT of Multilateral Well and ICD Multilateral Well (CECON) 55

4.20 Plot of Payback Time of Base Case Scenario, Multilateral (No ICD) and ICD well 56

4.21 Plot of Sensitivity Analysis of Varying Well Diameter 58

4.22 Plot of Sensitivity Analysis of Varying Water Cut Limit 59

4.23 Plot of Sensitivity Analysis of Varying Skin 60

4.24 Proactive Algorithm 65


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A1 FOE of Base Case Scenario 73

D1 FOE of the Various Well Inclinations 79

D2 FWCT of the Various Well Inclinations 80

E1 FOE of Conventional Horizontal Well and ERD 81

E2 FWCT of Conventional Horizontal Well and ERD 81

G1 FOE of Non-ICD Multilateral Well and ICD Multilateral Well (CECON) 84

G2 FWCT of Non-ICD Multilateral Well and ICD Multilateral Well (CECON) 84


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CHAPTER 1

INTRODUCTION

1.1 Introduction

Numerous fields worldwide have been producing at rates considerably below potential

values. Marginal reserves have been overlooked and discarded because the technology

required for profitable exploitation has been elusive, expensive or unproven. These problems

can be remedied by the application of Intelligent Well Completion (IWC) technology.

An Intelligent well (also known as smart well) is a well that is fitted with special completion

equipments that monitor the wellbore properties, collect, transmit and analyse well and

reservoir production data, then enables actions to partition the well, seal portions of the

wellbore, and regulate flow. These actions are carried out in a bid to enhance well

production, and are triggered automatically or manually (operator activated).

An intelligent well may be a single (monitoring multiple zones within a single well),

multilateral (monitoring multiple wells) or a commingled (monitoring multiple reservoirs or

layers) well. It may be drilled vertically, horizontally or inclined. Basically, the initial well

design from conception to drilling to the final completion stage is of importance in

determining how smartly the well would be completed, thus affecting the future production

and operability of the well.

Intelligent well completion (IWC) involves a combination of inflatable packers and sealing

elements, Inflow control devices/valves (ICD), and downhole sensors. It is an advanced non-

conventional well. Non-conventional wells may include horizontal wells, side tracked wells,

multi-laterals, highly deviated wells, extended reach drilling etc. Generally, any well that is

drilled or completed using techniques out of the norm, is considered non-conventional.

The concept of Intelligent well has been in existence for over a decade (1). It was first

installed at the Saga platform in the North Sea (2). The idea was initially borne out of a

search for a replacement to expensive and complicated well intervention procedures(3). At

first, the adoption of the technology was slow paced, but it has experienced sporadic maturity

and growth in recent time.


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The early versions of IWC used the concept employed by conventional wire line operated

sliding sleeves, which applied down hole electrical control systems and electronic sensors.

However, recurrent issues with longevity and reliability, and high attached costs, led to an

upgrade. This upgrade evolved to the application of down hole fiber optical sensors and

hydraulic controlled systems (2). Necessary electronic devices and software systems are

installed on the surface, allowing easy access for maintenance and repair operations (4).

Figure 1.1: An Intelligent well completion system. (Taken from (5))

Inflow Control Devices (ICD) or Inflow Control Valves (ICV) are considered keyequipments in Intelligent Completion Wells. They provide variability in the

control/regulation of fluid flow through the single or multilateral wells, by utilizing multiple

open and close flow ports. ICD are usually of 2 types; binary (open and close control) or

variable (intermittent, stage-wise control).

Intelligent Well Completion technology is applicable to both conventional and

unconventional hydrocarbon wells. The technology is fast gaining acceptance in the

completion of heavy oil fields (6), and is highly beneficial in Enhanced Oil Recovery (EOR)

operations (7). The goal of IWC is the automation of as much of the production process as is

achievable, so as to improve the Net Present Value (NPV) of an asset, which is achieved by

maximizing production and minimizing costs.


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1.2 Statement of the Problem

There is an ever increasing demand for fuel, which is caused by population growth and

globalization. Hydrocarbon fuel sources are unarguably the major source of energy, thus

putting a sizeable strain on the available resources.

To meet up with this demand, new hydrocarbon prospects need to be explored, and

recoverable production from existing and future proved reserves must be maximized. Proved

reserves are reserves with a high degree of certainty of being developed based on current

market prices (8). Conventional recovery rates hover around 35%, but operators aim to raise

this value to 60% (2).

This is achievable by 2 major methods, which are:

Enhancing the reservoir to increase recoverable reserves (i.e.: recovery factor) (9),

Maximizing the production of proved reserves by Intelligent Well Completions.

Various prospects present different challenges, which are tackled with diverse technological

combinations. The solution to maximizing production by IWC application is subjective, and

it is unfeasible to design a Universal combination.

Consequently, the challenge at hand is the determination of an optimal IWC combination for

maximum hydrocarbon recovery and minimal water production; with a high reliability and

relatively low attached risks.

1.3 Objectives

The objectives of this project include:

1.

To carry out extensive literature review on Intelligent Well Completions and their

applications.

2. Apply a reactive trial and error method of IWC, by modifying drilling, completion,

production and reservoir parameters to obtain maximum hydrocarbon recovery and

minimal water production (< 90% water cut).


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3. Analyze and compare results by simulating the modifications in Objective 2 above

with a numerical simulation package (Eclipse) to predict production.

4. Employ Inflow Control Devices for IWC applications.

5. Carry out reliability and risk surveys for optimal IWC.

6.

Carry out Economic analysis of optimal IWC.

7. Create an algorithm for Proactive IWC.

1.4 Scope of Work and Limitations

Data and simulation models from a Chevron oil field named "Field X" (For Confidentiality

purposes) are used for this case study. Schlumberger Eclipse simulation package would be

used to modify the drilling, completion and reservoir parameters, and simulate the changes in

production outcome.

The envisioned limitations include:

No access to specialized Intelligent well modeling tools and software e.g.: the ABB

Offshore systems' Advanced Downhole Monitoring and Reservoir Control

(ADMARC) Intelligent well systems.

A lack of on-hands direct field application to test viability of results under real-time

conditions. Results from simulations would have to be sufficient.


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1.5 Outline of Report

This project is presented in Five (5) chapters, outlined as follows:

Chapter One is a basic introduction to IWC, summarising its history and

applicability. The task at hand and expected targets are specified, the scope of work

and limitations are duly defined.

Chapter Two consists of an in-depth review into past experience in the field of

Intelligent well completion, and the various processes required for its attainment. The

benefits and challenges facing IWC are also investigated.

Chapter Three gives a detailed description of the steps taken to achieve optimal

production from the field using the Eclipse simulation package. Methodology for

economic evaluation, risk identification and sensitivity analysis are also presented.

Chapter Four presents results obtained from simulations and other methodology,

with detailed discussions.

Chapter Five presents the conclusions from this research, and makes

recommendations for future adaptation.


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CHAPTER 2

LITERATURE REVIEW

2.1 General Overview

Advancements in hydrocarbon exploration and production technologies have led to upgrades

in the complexity of well architecture (10), and intelligent wells are considered the most

advanced method of well completion available. Though the concept of Intelligent wells has

been in existence for over a decade, it continues to receive a lot of attention and research

grants, due to the enormous amount of accruable benefits, if effectively applied. Foremost on

the list of benefits are the accelerated oil production rate and upsurge in reservoir recovery

factor (11).

The functions of IWC are considered two-pronged i.e.: monitoring and control. The

downhole sensors act to measure various properties of the wellbore, while the ICD (Inflow

Control Devices) control and regulate the fluid flow at diverse points based on the results

from the sensor. Both the monitoring and control sectors must work in synergy for a well to

be truly considered intelligent.

The concept of IWC has been investigated from different perspectives, ranging from its

applicability under different conditions and multiple scenarios, to the benefits and challenges

encountered in operation. Extensive knowledge sharing on application of IWC technology

has bolstered its growth. A review has been carried out on previous IWC research and

applications, and these are summarized below.

2.2 Case studies

Over the years, the IWC technology has been applied to multiple fields under varying

environmental and reservoir conditions, and in different continents. Each field is considered

unique, as the method of IWC application varies based on individual field properties and

requirements. The desired benefits also play a major role in the selection of optimal IWC

combination to be employed.

Lien et al(12) evaluated the applicability of IWC on the Saramacca Oil fields, located in the

Republic of Suriname, South America. For the purpose of real-time monitoring of well


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performance and down hole pressure, a fully automated intelligent well system was installed

on the field. This aided in early detection of wellbore complications, troubleshooting as soon

as detected. There was a recorded increase of approximately 12 barrels of oil per day

(BOPD) from the Saramacca oil fields due to IWC adoption, thus compensating for the

higher installation cost.

Collins et al(13) investigated the effect of IWC on Agbami deepwater field, located offshore

Nigeria. IWC technology was installed on both production and intelligent wells on the field,

so as to alleviate the issues created by the complex stratigraphic architecture and subsurface

uncertainty of the reservoir. The Agbami IWC project provided real time

monitoring/surveillance and the control necessary for field performance and recovery

optimization. It ensured judicious reservoir management by the integration of surveillance

plans and production management practices in producing from multiple zones. Between

August and November 2010, approximately 10 million BOPD was incrementally added, due

to IWC application.

Al-zahrani et al(14, 15) of Saudi Aramco oil company, examined the installation of an IWC

SCADA (Supervisory Control And Data Acquisition) system on a Haradh Increment-III

(HRDH-III) field in Saudi Arabia.

The SCADA system was applied for real time monitoring and remote control of surface and

downhole tools from the control station. This eliminated the need for engineers to

control/regulate the Intelligent Well from the well site, while optimizing hydrocarbons

production. It was noted that IWC application (multilateral drilling, ICVs and sensors)

maintained peak production values, reduced water cut to the barest minimum (less than 3%),

and increased field performance.

Anderson et al (16) studied the first Maximum Reservoir Contact(MRC) multilateral well,

also located in Saudi Arabia. An MRC well is defined as a well that has a combined reservoir

contact area of more than 5km (16,000 ft), through the single or multilateral configuration

(17). Intelligent well completions and fiber optic monitoring technology were applied to

maximize production volume, resulting in a configuration that produced hydrocarbons at a

very high rate with low drawdown values for a prolonged timeframe.

In addition, the installation of IWC helped to extend the life of the wells. Ideally, when

premature water production occurs through one branch of a multilateral MRC well, it affects


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the other laterals, rendering the whole well network useless. However, IWC application

ensures that the erring lateral branch can be shut-in at will, thus stopping the standard

destructive chain reaction.

IWC applications in the Nakika fields, located in the deepwater Gulf of Mexico, were

studied and reported by Chacon(18). The application of Intelligent Well technology enabled

successful development of the Nakika field with a minimum number of wells while

providing high quality reservoir surveillance data.

Most of the reservoirs in the field are highly faulted, compartmentalized, and difficult to

image due to high salt presence. However, the installation of downhole pressure and

temperature sensors, availability of real time data, and installation of ICVs have been critical

to increasing reservoir characterization and eliminating the need for costly subsea well

intervention; thus reducing uncertainties and ensuring production optimization. These

combined benefits ensured a drastic reduction in the fields development costs.

Ajayi et al (11) evaluated the number of wells required for optimal development of a

segment of an offshore field. Both conventional and non-conventional IWC wells were

compared, so as to identify the best application of Intelligent well technology and quantify

the accrued benefits. This survey was carried out using a reservoir simulation model.

Downhole ICVs were installed at multiple positions in the wellbores to further increase

reliability and efficiency.

The results obtained showed an estimated incremental oil gain of between 2.5% and 26%

during the first 2 years of production, after water breakthrough had occurred. Controlling

produced water after water breakthrough occurred led to an increase in oil production.

In a study of a North sea oilfield, Yu et al (19) applied a reservoir simulation tool to quantify

the benefits of IWC. Multiple variations were considered in completing the well to achieve

optimal production. These include water injection, and altering the well inclination to that of

a slanted well. The selected combination proposed an addition of ICVs, downhole sensors

and downhole water-gas separator to a 2-branched inclined multilateral well.

Results indicate that the IWC combination increased the field oil recovery by 4%, continuing

production to the end of the fields life. This is a major improvement over the conventional

completion method, where hydrocarbons production was halted early due to free gas coning.


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Akram et al (20) investigated IWC installations on 9 mature, Shell operated prospects

located in the Brent and Tern fields, North Sea, UK. The fields produce a combined output of

400 thousand barrels of oil equivalent per day. ICVs were installed to provide on-line remote

control of the communication path from the reservoir zones to the tubing. The downhole

flow control enabled perforation of oil, gas and water production zones in a single operation,

thus saving costs.

The results of IWC installation included optimized oil production, zonal water management,

reduced sand production, and flexible gas capacity management; with the major challenge

encountered being the cost and technical difficulty of integrating IWC with the existing field

facilities.

Lie et al (21) reported on the first ever application of IWC technology in recompleting a

perforated live well in the Gullfaks field, offshore Norway. The result was considered

favourable from a technical and economic standpoint. The data from the downhole sensors

increased reservoir characterization, commingled production which was formerly considered

impossible was made feasible, and the need for drilling new wells was eliminated.

2.3 Commingled flow

Commingled wells are used to describe a concurrent production of hydrocarbon fluids from

two or more separate zones or reservoirs, via a single production conduit. This is of high

economic benefit, especially in the profitable production of marginal fields.

In most real life scenarios, wells often cross more than one hydrocarbon bearing layer. The

field operators are then faced with the challenge of deciding if to produce each layer

separately, or to apply commingling techniques. The latter option is only applicable when

Government regulations allow(22); also, the hydrocarbon bearing zones must be of

compatible pressure and fluid composition. Conventional sequential production usually

yields a lower recovery factor and poor production profiles. Application of IWC technology

solves these problems and eliminates intervention requirements, by opening and shutting

each zone remotely from the surface control unit (3, 23).

Chukwueke et al(24) investigated the deployment of IWC technology in exploiting reserves

offshore Nigeria, using commingled production. Three (3) marginal reserves which are not

considered economical by production from single wellbores, are commingled and produced


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through a single IWC wellbore. Marginal reserves can be defined as petroleum prospects that

are considered too minute or complicated to recover economically. Production from each

zone is managed and controlled using ICVs, without a need for intervention, further saving

costs.

2.4 Single and Multilateral Wells

Multilateral wells are wells that have more than one wellbore branching out from the main

borehole. Typically, a Multilateral well consists of 3 or 4 single open hole laterals drilled

from a sole main bore, with each single lateral acting as a single well with variances in

permeability and productivity from lateral to lateral. They are considered advantageous

because they increase overall cost of hydrocarbon production by accessing multiple

reservoirs/layers from a single surface location and improving reservoir drainage (25).

Multilateral technology is utilized to increase reservoir contact (reservoir-to-well exposure),

while remaining within the drilling and production limits. IWC can be installed to mitigate

against issues associated with variances in reservoir parameters that can lead to early water

breakthrough and low recovery.

Reed et al(3) indicate that application of IWC technology to multilateral wells bestow the

operators with the ability to isolate, test, monitor, and control each lateral of the wellbore. In

turn, this provides the engineers with the ability to maintain peak oil production, avoid or

reduce water coning, and extend the wells yield life.


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Figure 2.1: Multilateral well (Taken from (26))

2.5 Drilling inclination

Directional drilling is the process of directing the wellbore along a deviated trajectory to a

predetermined target in a 3-dimensional process on an X-Y-Z plane (27). The X plane

signifies direction, the Y plane signifies inclination, and the Z plane signifies depth.

Economic and environmental pressures have increased the adoption of directional drilling.

In a deviated well, produced fluid influx along the wellbore tends to be disproportionate due

to the reservoir heterogeneity and frictional pressure drop. This uneven influx can lead to

production problems e.g.: early water breakthrough. Though horizontal wells have a higher

production yield than vertical wells, they don't tend to access all the recovery layers. These

problems can be solved by applying IWC to isolate sections of the wellbore, and adjusting

fluid influx at each isolated section to obtain an even production(26, 28).

IWC application is even more advantageous in Extended Reach Drilling (ERD). ERD is an

advanced technique that applies the concepts of directional and horizontal drilling to achieve

horizontal well departures and reservoir contacts that exceed conventional directional drilling


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(29). Though, ERD technology increases hydrocarbon yield, a few challenges are associated

with it. These include:

Higher Initial Cost.

Complexities with reservoirs at great depths.

Issues with controlling pressure and weight of drilling mud.

Wellbore Instability and difficult wellbore management etc

2.6 Inflow Control Devices (ICDs)

The Inflow Control Devices (ICDs) or Inflow Control Valves (ICVs) are surface controlled

chokes that are used to restrict and regulate production. These devices regulate flow at each

predetermined zone by creating a pressure difference between the annulus and the production

string. They are applicable for shutting off zones of gas or water production, and can also be

used to shut in a well or layer for Pressure Build Up (PBU) operations (30). However, their

overall effectiveness is dependent on the reservoir properties (its pressure profile,

permeability, porosity and saturations), and the properties of the wellbore (well Productivity

Index (PI), Inflow Performance Relation (IPR) and Vertical lift performance (VLP)) (31).

Although sliding sleeves are applicable in choking flow, they cannot be used as effectively

nor accurately as ICVs. Installation of ICVs to choke a layer can only be effective when

production from the other completion layers can produce adequately to meet or exceedproduction targets. Nodal analysis and reservoir simulations are applied in determining

position and type of ICV for optimal effect (32). Historically, ICDs exhibit high success rates

when installed in high permeability areas, but variations may occur based on the particular

scenario under investigation.

ICDs are built in multiple variations. They may be operator controlled or automated; they

may be operated hydraulically or electrically, they may be variable from open to close or

may operate in multiple incremental steps (15, 33). Notwithstanding their variations, ICVs

are purpose built, operating within pre-set objectives e.g.: an ICV installed to choke water

production might not function optimally under excess gas production (34). Although, control

valves may also be designed for multipurpose applications.


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manufacture and installation of hardware. These 4 steps should be considered iteratively with

formal managements systems linking each stage for reliability improvement to occur.

Naldrett further believes that a stage-gate Product Lifecycle Management Process (PLMP)

should be adhered to, ensuring that the hardware is assessed from conception to

obsolescence. HAZID (Hazard Identification) and HAZOP studies should be performed for

risk quantification based on past experiences (21). Application of this process has led to

widespread improvements in specialists training techniques, well-site installation tools,

deployment efficiency and ultimate reliability.

Another method for increasing the reliability of the IWC system is by grouping the

completion components into sub-systems, with easy to handle pre-made hydraulic and fiber

optic links (16). Ferguson et al(4) prescribed that for increased longevity and reliability of

downhole data, the sensor transmission should be limited to raw data only and transmitted

through fiber optic cables. All stages of data processing and interpretation should be

performed on the surface where upgrades and maintenance procedures are easily applicable.

Al-Zharani et al (14) reiterate that the IWC hardware should be tested in a testing facility

i.e.: Factory Acceptance Test (FAT) before conveyance to the well site for actual installation.

This is done by connecting the hardware units to ICVs on the shop floor, and function testing

the valves to various positions i.e.: simulating the downhole control lines in the wellbore, so

as to optimize operating procedures and scrutinize equipment quality, hence giving an idea

of the length of each operation. This ensures a reduction in non-productive time (NPT),

while enhancing reliability.

Before and after installation, the IWC hardware is re-validated on location i.e.: Site

Integration Test (SIT). The sub-components are function tested and pressure tested to ensure

operation at full capacity, and this process should be routinely carried out every few months.

Presently, ICV deployment success rate is close to 100% (38)

2.9 Economics

Assets development teams are usually required to effectively rationalize the additional

capital cost of IWC installation. The rationale for cost effective IWC application is

subjective, and can only be justified on a case by case basis. A cost effective operation does


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not necessarily mean a lower cost. Economically, it is better defined as an investment that

produces a higher Net Present Value (NPV) than the alternatives.

Though, IWC is sometimes more expensive at the onset, this is compensated for by higher

hydrocarbons production, reduced water cut and a diminished necessity for future well

intervention.

In a study of a typical North Sea oilfield, Yu et al (19) indicated that the major challenge

with proper economic justification of IWC applications is the unavailability of ideal software

and modeling tools. These tools are required to show to the management that there would be

an increased and accelerated cash benefit by IWC adoption.

The North Sea oilfield case study describes an operation where an optimal IWC combination

is applied. The value of the oil produced from the IWC adoption was similar to the

conventional completion when an annual discount rate of 10% is employed; this is a major

financial advantage.

2.10 Reservoir Characterization

A prompt and judicious acquisition, evaluation and processing of reservoir data is paramount

for optimal reservoir characterization (35). Without these, the reservoir is considered a blind

zone with a high degree of uncertainty, making it difficult for engineers to manage the

reservoir soundly. Data from Intelligent Well downhole sensors are interpreted to enhance

and update production models and simulations. This increases clarity of reservoir

connections, reduces volumetric and deliverability uncertainties.

Naldrett et al(35) present a case where IWC technology is applied for reservoir management

and characterization. A production well is fitted with downhole temperature sensors which

record a temperature profile during production activities. An analysis and comparison of the

measured temperature profile with the geothermal gradient makes it possible to determine

the production interval and mass flow input from each reservoir layer.

Issues of poor or insufficient reservoir data have embarrassed petroleum engineers in the

past, forcing them to reduce estimates of recoverable reserves from oilfields. This situation

can be counteracted by a combination of MRC and Intelligent Well Completion, resulting in

a higher and more accurate reservoir description.


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Yu et al (19) summarized the positive effect of IWC on reservoir characterization as

providing real time downhole information from the production layers, thus increasing oil

recovery through better management.

Installation of IWC enhances reservoir characterization and modeling, enabling a better

reservoir model to be developed thus optimizing future production operations.

2.11 Reservoir Heterogeneity

Reservoirs are non-uniform in their properties such as permeability, porosity, pore size

distribution, wettability, connate water saturation and fluid properties. These variations can

be areal and vertical, and are caused by the depositional environments and subsequent events

e.g.: Catagenesis (39).

According to Reed et al (3) , experience has indicated that the profitability of IWC

application is greatly dependent on the inherent, pre-existing properties of the reservoir. The

extent to which production is enhanced depends on the field properties. The reservoir

heterogeneity causes a variation in the deliverability of each layer/zone. The reservoir must

be assessed and verified suitable for IWC technology before it is adopted.

Ebadi et al(31) carried out a research on the application of IWC to heterogeneous reservoirs.

The permeability and porosity values of the reservoir were varied using the geological

properties; Coefficient of Variation (Cv) and Correlation Length (CL). By increasing CL,

similar permeability values are grouped together; by increasing Cv, the disparity and range

of permeability values in the reservoir model are increased.

Based on the reservoir models, Ebadi et al deduced that there is great potential for added

value by applying IWC to a heterogeneous reservoir,. However, this is not conclusive, as a

full economic analysis was not carried out.

2.12 Enhanced Oil Recovery

Enhanced Oil Recovery or Tertiary oil recovery is the process of reducing oil saturation

below the residual oil saturation, after the primary and secondary recovery processes have

been exhausted (40).


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Naldrett et al (35) highlight an IWC application called Natural Gas-Lift Intelligent

Completion, whereby Intelligent Well technology is deployed for injection of gas for

improved reservoir and production management. Non-associated gas from a gas cap is

injected into the borehole through a wireline retrievable ICV, to provide a pressure boost and

aid oil production (41). This eliminates the need for intervention procedures, surface gas

injection and the accompanying equipments, saving costs in the process.

The concept of downhole gas separation reduces production of formation water and free gas

to the surface. The separated fluids (water and gas) will be re-injected into the formation to

enhance reservoir energy; the separated water will be re-injected into the aquifer, while the

separated gas will be re-injected into the gas cap or another formation.

Clark et al (7) expatiated on application of IWC to Steam Assisted Gravity Drainage

(SAGD) EOR process so as to achieve steam conformance along a horizontal wellbore, and

obtain a 5 to 10% increase in recovery factor.

Figure 2.2: Natural Gas-Lift Intelligent Completion (Taken from (35))

2.13 Water production

In cases where there exists an aquifer influx, or water injection (water flooding) is adopted as

a secondary recovery mechanism, the field would be susceptible to water breakthrough and a

high water cut. The produced water reduces the fractional flow of oil and increases the


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wellbore hydrostatic head, deteriorating oil flow. This forces the operators to make a

decision whether to abandon the well, or to continue production at a reduced rate.

Application of IWC technology reduces this problem by regulating water production through

the use of Inflow Control Valves.

Water flooding is the process of injecting water into the formation to displace the inherent oil

(42). Water flood projects are usually abandoned at the point where water breakthrough

becomes excessive, and the water produced dwarves the hydrocarbon swept along.

Application of IWC technology would gradually render this challenge extinct. Regulating the

injection flow rates from the injection well using ICVs would accomplish this goal

efficiently, maintaining the required pressure at each injection point.

Reed et al (3) analysed the application of IWC for water injection operations on Statoils

wells in the Norwegian sector of the North sea. The adoption of this technology generated

increased sweep efficiency, better reservoir drainage and decreased intervention costs. As a

result of these, the recoverable reserves were increased twofold from 2.4 million m3 to 5.4

million m3.

Armstrong et al(43) investigated the application of IWC to a single well producing from two

isolated pay zones of dissimilar permeabilities. ICVs are applied to choke back water

production from the high permeability zones, resulting in synchronized water breakthrough

along the wellbore. This reduced water cut, and simultaneously increased hydrocarbon

production volume.

2.14 Reactive or Proactive

There are two methods of controlling flow valves, these are the reactive and proactive

methods. Both methods have attached advantages and disadvantages. For example, though

the reactive method is simpler and faster to implement, the proactive method is less time

consuming, and can be more profitable in the long run (44).

Proactive method prevents an undesired future result, while the reactive method activates the

ICV when the undesired event occurs. The Proactive method is automated, following a

predetermined set of rules, while the reactive method is operated activated based on certain

decisions.


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The efficiency of a production system is optimized by an interactive integration of the

monitoring and control systems. Control actions can be taken based on readings from the

monitoring sensors, in turn these actions can be verified by the sensors. This ensures that

swift measures are taken, when anomalies are detected by the monitoring units.

Conditions for selecting the method of valve control to adopt are subjective, and different

scenarios may call for a different choice (45).

2.15 Benefits of Intelligent wells

As is evident thus far, the benefits of optimal application of IWC technology are numerous.

These include:

Reduction or elimination of extra wells, surface facilities, and intervention

procedures (24, 26, 32).

Reduction in the water cut (43,46)

Reduced Operational Expenses (OPEX) (26).

Maintaining oil and gas production peak (47).

Extends the life of wells and reserves(47).

Control, crossflow elimination and Back Allocation of commingled production for

economic exploitation of marginal reserves (32, 48).

Automated regulation of flow by downhole ICVs, controlled from remote locations

(21).

Augmentation or Replacement of Wireline services, particularly in inaccessible ERD

wells(26).

Maximise injection sweep efficiency by regulating injection rates.

Reduces geological uncertainty by higher reservoir characterization (2, 49).

Reduction in Non-Productive Time and Rig down time (32).

Real time measurement and transmission of reservoir properties for better reservoirmanagement(24, 47).

Reduced risk of personnel accidents, since there is reduced requirement for their

presence on the well site(24, 47)


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These benefits are amplified many times over in deepwater and subsea operations, due to the

expensive and technically demanding challenges of these locations.

2.16 Challenges facing IWC

Challenges in IWC adoption can be alleviated by selecting optimal system combinations, andensuring excellence in the different stages of design, planning, installation and

implementation. Though this does not guarantee a successful operation, it would greatly

increase its chances.

Based on literature review of past experiences, common challenges encountered include:

Substantial amount of rig time and expertise is required for the installation and testing

of IWC (16).

Oil and gas price uncertainties(50)

Lack of reservoir evaluation tools for effective modeling of the Intelligent well

components and expected operation (11).

Expensive adoption of IWC to mature fields (20).

Reliability of downhole valves and sensors (33).

Identification of potential and suitable candidates for IWC(34)

The challenge of finding the right "People", employing the right "Process" and using

the right "Products" (46).

2.17 Focus of Present Work

The aim of this project is to identify the optimal Intelligent Well Completion combination for

drilling and completion of Chevron Field X, and quantify the accrued gains by Numerical

reservoir simulation predictions. The reservoir simulation package of choice is the

Schlumberger Eclipse 100 simulator.

Based on prediction results from the simulator, economic analysis would be carried out to

determine the feasibility of the IWC of choice, a proactive algorithm is generated for future

applications, and Risk analysis is performed to reduce inherent risks and increase reliability.


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Chapter 3

METHODOLOGY

3.1 Introduction

As discussed in previous chapters, there is no "one size fits all" ideal IWC configuration. All

optimal Intelligent well applications are conceived as fit-for-purpose.

The 1st step in adopting IWC technology is to ascertain that the reservoir under study is

lithologically suitable. This starts from a simple analysis of the reservoir properties, and

advances to complex numerical reservoir simulations. From the reservoir standpoint, factors

such as number of layers, pressure variations, heterogeneity (porosity, permeability),

reservoir size, fluid contact depths, natural and artificial recovery mechanisms in place, total

recoverable reserves, fluid type etc are taken into consideration.

The next step is to consider the operating environment (onshore or offshore), well

completion and production methods. Based on well completion considerations, the well

geometry and trajectory, well type (injection or production), sand production potential,

expected drawdown rates, casing and tubing sizes, artificial lift etc are deliberated upon.

Afterwards, the reliability and associated risks of the IWC components will be outlined and

investigated, with steps proposed for mitigating against the occurrence and impacts of

exposure to these risks.

Finally, the economics of IWC adaptation i.e.: Increased investment costs (CAPEX, OPEX

etc) are compared to the calculated increase in revenue (due to higher hydrocarbon recovery,

lower water production, saved rig time from elimination of intervention procedures etc)

based on simulation predictions. The objective is to determine the economic viability of IWC

utilization.

These steps are not sequential, but are interwoven and considered in combination.


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3.2 Reservoir Data

The reservoir properties have been collated from well logs, core samples, laboratory tests and

measurements and petro-physical interpretation. Some of these properties are highlighted

below.

3.2 Reservoir Simulation

The best way for appraising the production benefits of an IWC application is by modeling

both a conventional base case scenario and an alternative IWC scenario, and then comparing

the output ultimate recovery (11, 37). This is achieved using Reservoir simulation packages,

and in this case, the Schlumberger Eclipse 100 simulator.

Table 3.1: Initial Reservoir Properties

Reservoir Parameters Value

Grid cell Dimensions

Length (DX) (ft)

Width (DY) (ft)

Height (DZ) (ft)

328

328

6.06 to119.10

Volume of Grid cells (X * Y * Z) (ft3) 138 * 82 * 24 = 271,584

Average Porosity * 0.12 to0.17

Average Net To Gross NTG 0.63

Reservoir Permeability (mD)* 1 to 100

Initial Water Saturation 0.45

Initial Reservoir Pressure Pinitial (psi) 2,776

Oil Gravity (oAPI) 23

Oil Viscosity (cP) 7

Reservoir Depth (ft) 5,971

Water Oil Contact (ft) 6955

Gas Oil Contact (ft) 5085.30

* =Heterogeneous and Anisotropic


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Reservoir simulation is a process of deducing the performance of a real reservoir from the

predictions made by a physical or mathematical model of that reservoir (51). It allows the

operator to gain a deeper insight into the mechanisms of hydrocarbon recovery. This is

achieved by dividing the reservoir into a number of blocks and applying basic equations

(governing, boundary and initial equations) for flow in porous medium to these individual

blocks.

The ultimate target of a reservoir simulator is to predict the future performance of a

reservoir, providing information on methods of increasing overall recovery (51). A

Numerical method is composed of a complex mathematical model of a physical system,

which is governed by partial differential equations and iterations, with preset boundary

conditions that describe and predict physical processes taking place in the reservoir. A

Numerical model is usually carried out with the aid of a computer program, applying

numerical methods to compute solutions to the mathematical model. The processes that take

place within a reservoir are predominantly fluid flow (water, oil and gas) and mass transfer.

However, it must be noted these models are prone to geological uncertainties, and disparities

might occur between predicted and real behaviour. Though, the reservoir simulator is a

valuable tool, it does not replace good and experienced engineering judgment. Operators

should continually adapt the model to changes.

3.2.1 Schlumberger Eclipse Simulator

Schlumberger/Geoquest Eclipse is a fully implicit, multiphase, multi-dimensional computer

program for numerical simulation of a reservoir model; consisting of several thousand

keywords that are used to initiate various actions.

To begin simulation with Eclipse, a DATA input file is required. The name of this file must

have a ".DATA" filename suffix. (e.g.: Reservoir2.data). This file contains the reservoir data

or links to other files in which the data is saved (using the INCLUDE keyword). The DATA

file can be created and modified using any standard word editor

An Eclipse data file is split into 8 major sections, each of which is introduced by a keyword

(52). Some keywords can be used in both Eclipse 100 and 300, while others are recognised


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in only one of the two. Most of these keywords can only be used in 1 specific sections of the

data file. Out of these 8 major sections, 5 are required, while 3 are merely optional. These

section keywords must be specified in the displayed order:

RUNSPEC (required)

GRID (required)

EDIT (optional)

PROPS (required)

REGIONS (optional)

SOLUTION (required)

SUMMARY (optional)

SCHEDULE (required)

For both the conventional base case scenario and all alternative IWC combinations,

keywords 1-7 listed in the table above remain the same, except in the case of Minor isolated

layers and Thief zones. The modifications for Intelligent Well completion are made in the

SCHEDULE section (keyword number 8).

3.3 Base Case Scenario Field Model

The Base case field model refers to the field at initial conditions, before Unconventionaltechnologies and Intelligent Well Completion are applied. The details and data input

keywords can be viewed in Appendix A

3.3.1 RUNSPEC (Field Run Descriptions)

This is the first section of the simulation data input. It is a required keyword as the

information contained in Eclipse are a prerequisite for the simulations to execute without

errors. RUNSPEC further contains other keywords that execute the modeling options or

contain data and information.

Based on the RUNSPEC section in the base case scenario data file, the field contains oil,

water, gas and dissolved gas phases. The model grid block is subdivided into 138 cells on the

X axis, 82 cells on the Y axis, and a total of 24 layers on the Z axis for simulation purposes.

The X and Y axis are the length and breadth of the square model grid blocks, while the Z


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axis depicts the reservoir model thickness. All measurements are carried out in "field" units

for consistency. The reservoir has a weak aquifer, which is considered to be insignificant.

The field production and injection simulations are set to begin on the 1st of September, 2010.

3.3.2 GRID (Grid and Rock Geometry and Properties)

The GRID section contains information on the basic geometry of the simulation grid blocks

and rock properties in each grid block.

Based on the GRID section in the base case scenario data file, the model is of a Cartesian

block centered geometry, consisting of horizontal blocks with right angle corners.

Anisotropic values for Porosity, Net-To-Gross (NTG) and Permeability values for each grid

block are specified in the INCLUDE files. Net to Gross refers to the fraction of the reservoir

within the total sand sequence. The minimum pore volume that a cell must exceed or be

rendered inactive (cells with zero pore volume) is set by the MINPV keyword at a value of

30 rb (reservoir barrels). To curb the barrier effects of these inactive cells, the PINCH

keyword is used.

Based on the cell dimensions and permeability, the program then calculates the

transmissibility of fluid over the field model.

3.3.3 EDIT (User Defined Modifications)

No modifications are made to the simulation model, thus the EDIT keyword is omitted.

3.3.4 PROPS (Reservoir and Fluid Properties)

This section contains pressure and saturation reliant properties of the reservoir fluids (as

specified in the RUNSPEC section) and rocks. The PVT (Pressure-Volume-Temperature)

keywords used here are determined by the oil, water, gas and dissolved gas phases.

Based on the results from laboratory and field tests, the PVT model is inputted as an

INCLUDE file. This file contains the PVT properties of oil, dry gas and water. The

properties stipulated for oil includes its Gas Oil Ratio (GOR), bubble point/saturation

pressure, the oil formation volume factor, and oil viscosity respectively. The properties of


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dry gas include the gas phase pressure, the gas formation volume factor, and corresponding

gas viscosity. The properties of water include the reference pressure, the water formation

volume factor, the compressibility of water, and the water viscosity.

In addition, the surface densities of the reservoir fluids, rock compressibility and pressure,initial water saturations, water/oil and gas/oil relative permeability and capillary pressures

etc are also included using appropriate keywords. These values are used by the simulator to

solve the mass balance equations.

3.3.5 REGIONS (Splits the Computational Grid into Regions for Calculation)

The INCLUDE section keyword is used to import files containing the FIPNUM and

EQLNUM keywords. The FIPNUM keyword signifies the region numbers for fluids in

place. Every grid block is defined by an integer following the keyword, specifying the fluid

in place region to which it belongs; while the EQLNUM keyword specifies the equilibration

region to which each grid block belongs.

3.3.6 SOLUTION (Initial Reservoir Conditions)

This section contains data on the initial state of each grid block that makes up the simulation

model i.e.: the pressure, saturations and composition.

Based on base case conditions, the datum depth is set by the EQUIL keyword at 5971ft. This

is followed by the pressure at this specified depth, which is set at 2776psi, the Water Oil

Contact (WOC) is at a depth of 6955ft, and the Gas Oil Contact (GOC) is at 5085.3ft. The

PBVD keyword is used to specify bubble point pressures at varying depths.

3.3.7 SUMMARY (Result Output Specification)

This section contains specifications of data that should be written to the summary file at the

end of each simulation time step. Though this section is optional, it is very helpful when

certain types of graphical and tabular outputs (e.g.: Completion oil production rate as a

function of time) are required. If there is no SUMMARY section, no summary files are

created at the end of the simulations.


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In the SUMMARY section of the base case data file, the following keywords are used and

are considered noteworthy:

Table 3.2: SUMMARY Output Keywords

KEYWORD DESCRIPTIONEXCEL This ensures that the generated predictions from the simulation model are

written to an excel file, and saved with a .RSM suffix. These files can be

opened and viewed with Microsoft Excel, making viewer readability easy.

COPR Completion Oil Production Rate

COPT Completion Oil Production Total

CWPR Completion Water Production Rate

CWPT Completion Water Production Total

FOPR Field Oil Production Rate

FOPT Field Oil Production Total

FWPR Field Water Production Rate

FWPT Field Water Production Total

FOE Field Oil Efficiency; signifies the field recovery factor (the percentage of oil

that has been recovered)

3.3.8 SCHEDULE (Simulation Operations and Timing)

This section specifies the well definition, various operations to be simulated, production and

injection well completion and constraints, and simulation schedule (timesteps/dates). In the

SCHEDULE section of the base case data file, the WELSPECS keyword is used to define

the names of the single injector and producer wells, their cell locations on the simulation

gridblocks, and the preferred fluid phase produced from the well. The injection well location

is selected based on areal sweep efficiency, reservoir depth and geometry, and fluid

saturation(53). The reference well depth for the bottom hole pressure is defaulted, thus it is

automatically set to the grid block where the first connection is located in COMPDAT (52).

The COMPDAT keyword is used to define the position and properties of the well

completions, with the corresponding input values stipulating the location and inclination of


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the wellbore on the gridblocks. Based on values inputted in the COMPDAT keyword of the

base case file, the Base Case well is a horizontal well. OPEN flag is set to indicate that each

well connection is open to flow. The wellbore diameter is specified as 0.708ft, and the skin

factor is set as "2" for both injector and producer wells.

The keywords WCONPROD and WCONINJE are used to specify the control data and

constraints for the injection and production wells (production and injection rates). In the base

case model, there are 1 production and 1 injection wells. The production well is set OPEN to

flow, and constrained by a maximum oil rate (set by the ORAT keyword) of 5, 000 stb/day.

The injection well is also OPEN to flow, with the injection fluid phase set to WATER, and

constrained by a maximum water injection rate of 5,000 stb/day.

The TSTEP keyword is used to specify the schedule timestep for this project, which is set at

a total of 10 years, starting from 2ndSeptember, 2010.

The proper arrangement of keywords is essential, as certain keywords must appear before

others for the simulation to run without errors (54). e.g.: WELSPECS must be specified

before COMPDAT, as this is analogous to the sequence with which events occur in well

drilling and exploitation. This can be viewed further in Appendix A.

3.4 Error Avoidance

The "NOSIM" keyword is used for data checking, to test the model without actual

simulation, thus saving time in error prone situations. Double dashes (--) are inserted before

comments to ensure that the Eclipse model treats it as a comment, and avoid unnecessary

errors. This also ensures that the various keywords and steps are clearly defined for ease of

readability.

3.5 Intelligent Well Modifications

3.5.1 Flow rate

The flow rate is the volume of a given fluid that passes through a surface as a function of

time. Fluid motion through the reservoir into the wellbore is governed by Darcy's law; which


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production points of view; and then results from numerous tests and investigations are

compiled to determine the most ideal location, economically and lithologically.

Paramount amongst these factors are the permeability and saturation of the reservoir around

the wellbore. Using the Schlumberger Eclipse Floviz tool, it can be seen that the Base casescenario well is a horizontal well completed in layer 9. To test for ideal well location,

modifications were made to the COMPDAT keyword of the SCHEDULE section. These

syntax can be viewed in Appendix C.

3.5.3 Well Inclination

After the best location for well placement has been determined, the next step is to determine

the inclination with which the well will be completed. The angle of wellbore completion has

a sizeable effect on the well production rates and total. As can be seen in the COMPDAT

keyword of the SCHEDULE section of the Base Case Scenario (Appendix A), the well is

completed horizontally. To test if optimal inclination was adopted, modifications were made

to the SCHEDULE section ,COMPDAT keyword to complete the well with vertical and

deviated connections.

In testing for vertical and deviated flow, the concept of "Commingled Production"was also

put to the test. Based on the assumption that the various production layers of the reservoir are

compatible, and the hurdles of Government legislations have been cleared, the principle of

concurrent production of hydrocarbon fluids from multiple zones via a single production

conduit is applied to the oil field. These syntax can be viewed in Appendix D1 and D2.

3.5.4 Well Contact area

In a bid to contact a larger reservoir area from a single wellbore, the borehole is kept in the

reservoir over a longer distance, thus optimizing well productivity and drainage i.e.:

Extended Reach Drilling (ERD) and Maximum Reservoir Contact (MRC) are applied. ERD

and MRC increase the field production total and rates.


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The Base Case Scenario producer well had a horizontal distance of 2,296 ft i.e.: X gridblocks

30 to 37 (COMPDAT keyword in Appendix A). In applying Intelligent Well technology, the

wellbore horizontal distance was amplified by increasing the horizontal completion in the

COMPDAT keyword, as seen in Appendix E.

3.5.5 Multilateral Well

The propelling force behind multilateral wells is the improved exposure to productive

reservoir formation by multiple lateral wells, resulting in higher production rates.

The Base Case Scenario has a single production and injection wells (SCHEDULE section,

WELSPECS keyword of Appendix A). IWC is applied by increasing the number of lateral

wells to produce from multiple optimal layers. In addition, a 2nd water injection well is

added to the model based on considerations for increased flood efficiency of the added layers

(53). These syntax are detailed in WELSPECS and COMPDAT keywords of Appendix F.

3.5.6 Inflow Control Devices

Inflow control devices are applied across wells and intervals to regulate flow, optimizing

hydrocarbon production and minimizing water production.

Modeling for Open/Shut ICDs with the Schlumberger Eclipse 100 simulator was achieved by

applying the CECON keyword in the SCHEDULE section to set a water cut limit of 90%, at

which the errant connections are automatically SHUT (Appendix G).

3.5.7 Effects of Reservoir Variability

Over the decades, there have been astounding improvements in technology, spanning

exploration, drilling, completions and production techniques. However, uncertainties remain

inevitable and are to be expected and factored into the planning process of every oil field.


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In a bid to satisfy unavoidable variations in reservoir properties, 3 factors are considered.

These are: a) Homogeneous reservoir b) Thief zones c) Minor Isolated zones. The impact on

recovery of Thief zones and Minor Isolated layers are modeled and tested.

3.5.7.1 Reservoir Homogeneity

A homogeneous reservoir refers to a reservoir with isotropic properties, that do not vary with

change in direction (i.e.: horizontally and vertically) (56).

3.5.7.2 Thief Zones

Thief zones are defined as portions of the reservoir formation, which are of limited thickness

but relatively high permeability (57). This results in rapid channeling, fluid loss, and

bypassing. However, these can be remedied by IWC, which aids in detecting the errant zones

and activates the ICDs to regulate or shut down production from these zones.

Thief zones are included in the reservoir models for horizontal and deviated wells by using

the MULTIPLY keyword in the GRID section (APPENDIX H1). Their effects on field

production total are duly investigated.

3.5.7.3 Minor Isolated Zones

Isolated zones are regions of relatively low permeability (as low as zero) which act as seals

(permeability barriers), preventing free flow of petroleum fluid through the reservoir

formation and into the wellbore (56). These isolated zones are usually formed during the

secondary stages of porosity due to excessive cementation. The effect of Isolated zones can

also be remedied by IWC application, detecting the errant zones and activating the ICDs to

regulate or shut down production from these zones.

Isolated zones are included in the reservoir models for horizontal and deviated wells using

the MULTIPLY keyword in the GRID section, by multiplying specified zones with "0" to

simulate their occurrence (APPENDIX H2). Their effects on field production total are duly

investigated.


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3.5.8 Economic Evaluation

Optimal hydrocarbon exploitation is 3 pronged. It entails "maximum recovery", which must

be carried out "safely" and "profitably". An innovative, ingenious technology that ensures 1

or 2 of the above mentioned targets are satisfied, but tends to neglect the others, would not beconsidered adoptable. Hydrocarbon exploitation is a business for virtually every party

involved, and like all businesses, its main aim is to make profit.

The economics of adopting IWC technology on the field are thoroughly investigated, to

determine the viability of implementing the technology. Basic economic analysis tools and

formula are applied (58) to generate comprehensive results and plots consisting of CAPEX,

OPEX, Revenue, Payback time, and NPV (Net Present Value).

The NPV budgets for the deterministic cash surplus that is generated by anticipated future

events (59). CAPEX refers to the front end cost that arise at project inception (60).i.e.:

drilling costs, completion costs etc. OPEX are expenses necessary for the daily operations on

the field. Revenue is a product of the volume of oil produced and the present oil market

value. Payback time is the time it would take for investment to equal returns. The NPV is the

summation of discounted cash flow.

For the purpose of simplicity, gas revenues are ignored, and the analysis is concentrated on

oil. Comprehensive results and plots are outlined in Appendix I

Some basic formula applied include:

CASH FLOW = REVENUE - (CAPEX+OPEX+ROYALTY+TAX)

TAXABLE INCOME = REVENUE-(OPEX+ROYALTY)

TAX = TAX RATE TAXABLE INCOME


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Table 3.3: Field Economics Values

VARIABLE OPEX ($) 0.1 * VOLUME OF WATER PRODUCED

(STB)

FIXED OPEX ($) 20,000,000 PER ANNUM

OIL PRICE ($) 110 (61)

ROYALTY (%) 10

DISCOUNT RATE (%) 10

TAX RATE (%) 20

Table 3.4: Breakdown of Fields CAPEX

BASE CASE SCENARIO UNITPARAMETER CAPEX ($)

COST OF OIL WELL 10,000,000 2 20,000,000

COST OF

COMPLETION

2,000,000 2 4,000,000

TOTAL CAPEX 24,000,000

MULTILATERAL WELL UNIT

PARAMETER CAPEX ($)

COST OF WELL 10,000,000 6 60,000,000

COST OF

MULTILATERAL

WELL

0.5 * COST OF

WELL

30,000,000

COST OF

COMPLETION

2,000,000 6 12,000,000

TOTAL CAPEX 102,000,000

ICD WELL UNIT

PARAMETER CAPEX ($)

COST OF WELL 10,000,000 6 60,000,000

COST OF

MULTILATERAL

WELL

0.5 * COST OF

WELL

30,000,000


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3.5.10 Risk Identification and Reliability

Risks are events that when triggered would cause unwanted consequences to occur (64).

There are numerous possible risks that may occur on a conventional oil and gas production

platform, these risks are further magnified when unconventional IWC technology is adopted.For the sake of simplicity, the Intelligent Completion related issues are mostly concentrated

on, in this thesis.

In most cases, it is believed that as far as a future event is anticipated, human intervention

can alter the occurrence of this event. Some risks are considered minor (i.e.: easily and

cheaply fixed), while others are major (i.e.: complicated and expensive to remedy). In all

cases, risks should be reduced to a level that is considered ALARP (As Low As Reasonably

Practicable). Common Cause failures (CCFs) or minimal cut sets are major failures that

occur in a critical component; when instigated, they would cause a chain reaction that would

trigger total system failure. CCFs should be avoided at all cost.

Reliability can be defined as the probability that a component or system would operate

adequately at any point of demand, or over a certain period of time; it is the life distribution

of the component.

Risk =Probability * Consequence (3. 2)

Thus, as proven mathematically, the lower the probability or consequence of failure, the

lower the risk (direct proportionality). Since reliability is the opposite of probability of

failure, the task at hand is to improve reliability so as to reduce risk. This is a very important

concept, and is the basis of all efficient design. Although there are multiple techniques for

risk management, the basic principle still follows this sequence of Risk identification and

Mitigation

For risk identification, a concept similar to the HAZID technique is employed, with a

structured set of questions asked about each component of the system, so as to prevent

possible failures. Past experience from literature search is drawn upon, so as to scrutinise the

IWC concept (from design to completion phase) for hazard identification and methods for

eliminating these hazards (65, 66).


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Safety Integrity Level (SIL) is the degree of safety requirement; the level of protection

(based on estimated risks of failure) that a system must achieve for a given degree of

reliability (64). These range from SIL 1 to SIL 4, with SIL 1 having the lowest level of

reliability, and SIL 4 with the maximum possible reliability (difficult to attain). These are

applicable to both existing and proposed systems. These concepts are applied in risk

mitigation, to measure increased system reliability.

3.5.11 Proactive Algorithm

A Proactive approach to Intelligent Well technology applies an optimization algorithm to

achieve an optimal ICD configuration, by using real time production data from downhole

sensors in decision making. The aim is to prevent a future undesired event; yield a maximum

hydrocarbon recovery, and a minimum water production without the need for continuous

operator intervention.

The steps taken in making the Chevron Field X "smarter" are combined with downhole

sensors functionalities to propose a proactive flowchart.


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CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 Overview

The results derived from various modifications to the Base Case Scenario field model to

increase its "smartness", thus maximizing oil production and restraining water production

below 90% water cut, are presented in this chapter. The main output focus is on field

production total. The oil outputs are of greater importance than the gas yield, since oil has a

higher market value. Investigations on risks and reliability, economic feasibility, sensitivity

analysis, and the generated proactive algorithm are also presented.

4.2 Base Case Scenario

A summary of the result output that was generated by running the data file of the base case

scenario field model i.e.: No "Intelligent" modifications included (Eclipse syntax in

Appendix A), are presented here.

Figure 4.1: Field Production Rate of Base Case Scenario


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Figure 4.2: Field Production Total of Base Case Scenario

Table 4.1: Field Production Outputs of Base Case Scenario

FOPT (MSTB) FWPT (MSTB) FOE FWCT

18,199.91 582.25 14 % 6.2 %

**NOTE: 1 MSTB= 1,000 STB

At initial conditions, the field is producing oil at 18,200,000 stb volumetric total, with a field

recovery factor (field oil efficiency) of 14%. Initial water production is quite low at 585,250

stb and a corresponding water cut value of 6.2%. The foremost target is to produce more oil,

generating higher economic returns.

4.3 Adopting Intelligent Well Completions

4.3.1 Flow Rate

A) Trial and error method; Varying the oil flow rates for maximum productivity. The

altered oil flow rates and accompanying field oil production totals are displayed in Table 4.2


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B)Bottom Hole Pressure (BHP) of 14.7 psi, and a default oil rate

For the purpose of further verification, the well oil flow rate is defaulted, while the

constraining factor is set as a BHP of 14.7 psi (Appendix B2). A BHP of 14.7 psi is

unrealistic in practical, but is adapted for making comparisons.

Figure 4.4: Comparison of FOPT and FOPR of 10,000stb/day and BHP constraints

As can be seen from the plots of methods A and B (Figure 4.4), the FOPT and FOPR values

of BHP constraint and 10,000stb/day virtually overlap.

These results further validate the selection of 10,000stb/day oil flow rate as the optimal

choice. The results of the Eclipse simulations are presented in Table 4.2 above. These

indicate that production at an oil rate of 10,000stb/day gives the highest cumulative oil

production and field oil efficiency values.

4.3.2 Well Location

Based on the field permeability and saturation, investigations were carried out to determine

the ideal well location for optimal oil production. Four (4) high permeability, high saturation


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layers were detected; i.e.: Layers 6, 9, 13 and 14. The Base Case Scenario well was located

in layer 9 (As seen in Figure 4.6).

The results from varying well location amongst the favorable layers can be seen in Table 4.3,

and the Eclipse syntax for accomplishing this is presented in Appendix C.

Figure 4.5: Comparison of Field Oil Production Total of wells located in the various layers

Table 4.3: Field output data of wells located in the various layers

LAYER 6 9 13 14

FOPT (MSTB) 18,930.47 19,218.36 20,843.39 19,539.91

FWPT 804.53 744.612 21,170.32 26,647.35

FWCT 7.2 % 6.8 % 76 % 78 %

FOE 14.5 % 14.5 % 16 % 15 %


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Figure 4.6: FloViz Image of Base Case Scenario Well Located In Layer 9

Figure 4.7: FloViz Image of Optimal Well Located In Layer 13

Interpreting the results from simulations in table 4.3, the upper layers 6 and 9 have a low

water production with water cuts of 7.2% and 6.8% respectively. However, there is a sharp

rise in water production as the wellbore is further drilled to layers 13 and 14 with water cut

values of 76% and 78% respectively.

On the other hand, the highest cumulative field oil production was achieved in layer 13.

Since the target at this point in time is for optimal oil production, and the water production is

still below the acceptable water cut of 90%, the well location is adjusted to layer 13, as it is

considered a "smarter" decision to complete the well in this layer.


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4.3.3 Well Inclination

Based on modifications made to the well completion model i.e.: inclination, to determine the

option for maximum oil production (Syntax in Appendix. D1 and D2), the following plots

and results were produced as outputs.

Figure 4.8: FOPR of the various well inclinations

As seen in Fig 4.8, the well with a horizontal inclination (blue curve) has the highest oil

production rate for over 2,200 days. This is deemed a major benefit, considering the time

value of money, and the effects of depreciation.


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Figure 4.9: FOPT of the various well inclinations

As seen in Fig 4.9, the well with a horizontal inclination (blue curve) has the highest oil

production total right from production onset till the end of flow simulation (20,843,490 stb).

This is the major basis for selection of well completion type, as the water cut is still below

90%. Simultaneously, the horizontally drilled well also has the highest recovery factor, as

seen in Fig D1 (Appendix D).

Combining the results from Figures D1, 4.8 and 4.9, a horizontal well is the obvious victor.

Thus, the Base Case horizontal well is retained. Further comparative plots can be viewed in

Appendix D.

Table 4.4: Field output data of the various completion methods

INCLINATION HORIZONTAL COMMINGLED

DEVIATED

COMMINGLED

VERTICALFOPT (MSTB) 20, 843.39 13, 833 8,913.9

FWPT 21,170.32 19,765.75 15,379.89

FWCT 76 % 70 % 74 %

FOE 15.8 % 10.5 % 6.8 %


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4.3.4 Well Contact Area

It is a wide spread theoretical belief that a wellbore kept in the reservoir over a longer

distance with a high reservoir contact area would have an optimized well productivity and

drainage (67). This theory is put to the test by applying the concept of Extended ReachDrilling with a Maximum Reservoir Contact (MRC) area in modeling the production and

injection wells. The syntax codes for these modifications can be found in Appendix E.

Figure 4.10: FOPR and FOPT of conventional horizontal well and ERD

Table 4.5: Field output data of conventional and ERD wells

COMPLETION LENGTH CONVENTIONAL ERD

FOPT (MSTB) 20, 843.39 24,973.5

FWPT (MSTB) 21,170.32 13,746.92

FWCT 76 % 70 %

FOE 15.8 % 19 %

LENGTH (ft) 2,296 6,890


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Figure 4.12: Plot showing effect of thief zone on FOPT from Horizontal and Deviated wells

Figure 4.13: Plot showing effect of thief zone on FWPT from Horizontal and Deviated wells


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Figure 4.14 : Plot showing effect of Isolated zones on FOPT from Horizontal and Deviated wells

Figure 4.15: Plot showing effect of Isolated zones on FWPT from Horizontal and Deviated wells.


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Table 4.7: FOPT and FWPT from Horizontal and Deviated wells with Isolated zone effect

INCLINATION HORIZONTAL COMMINGLED DEVIATED

FOPT (MSTB) without

Isolated Zone

20, 843.39 13, 833

FOPT (MSTB) with Isolated

zone

20, 678.46 12,444.27

FWPT (MSTB) without

Isolated Zone

21,170.32 19,765.75

FWPT (MSTB) with Isolated

zone

21,038.75 20,028.15

As can be deduced from the plots in Figures 4.14 and 4.15, and the production results in

table 4.7, the presence of isolated zones leads to a reduction of oil production in both

horizontal and deviated wells. The isolated zone acts as a seal, preventing free flow of the

hydrocarbons from the reservoir to the wellbore, hence the reduction in oil production

volume as noted above. Although, the effect is much more significant in deviated wells due

to its completion across multiple layers that contain isolated zones, unlike the horizontal well

that is completed across a single layer.

The effect of isolated zones on water production is apparent in both deviated (FWPT

increases by 262,400 STB) and horizontal wells (FWPT reduces by 131,570 STB). (Figure

4.21). Different reservoirs possess varying degrees of randomness in properties, the effects

of reservoir variation on production volume cannot always be predicted before production.

4.3.6 Multilateral Well

The objective of Multilateral wells is to produce more hydrocarbons from a well, while

reducing the total cost of producing each STB of oil (68).

The next steps in IWC application are an upgrade of the ERD well configuration model to a

multilateral layout, and an addition of a 2nd water injector to efficiently flood the added

layers (see Appendix F for syntax). The multilateral well is completed horizontally in layers


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6, 9, 13, 14; which had hitherto been identified as optimal production

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