Maheshwari Yugal Kishore Thesis

Master’s Thesis

A Comparative Simulation Study of Chemical EOR Methodologies (Alkaline, Surfactant and/or Polymer) Applied to Norne Field E-Segment August, 2011

Yugal Kishore Maheshwari

Supervisor: Professor Jon Kleppe

Department of Petroleum Engineering and Applied Geophysics

Faculty of Engineering and Technology Department of Petroleum Engineering and Applied Geophysics

Master’s Thesis

by

Yugal Kishore Maheshwari

Thesis started: March 20, 2011

Thesis submitted: August 16, 2011

Study program: MSc. Petroleum Engineering

Specialization: Reservoir Engineering

Title of Thesis: A Comparative Simulation Study of Chemical EOR Methodologies (Alkaline, Surfactant and/or Polymer) Applied to Norne Field E-Segment

This work has been carried out at the Department of Petroleum Engineering and Applied Geophysics, under the supervision of Professor Jon Kleppe

Trondheim, August 16, 2011

Jon Kleppe

Supervisor

Professor and Head of Department

ii

Abstract

Abstract

Primary and Secondary Recovery techniques together are able to recover only

about 35-50% of oil from the reservoir. This leaves a significant amount of oil

remaining in the reservoir. The residual oil left after the water flooding is either

from water swept part or area by-passed by the water flooding. The by-passed

residual oil has a high interfacial tension with the water. One way of recovering

this capillary trapped oil is by flooding the reservoir with chemicals (surfactant,

alkali-surfactant (AS), surfactant-polymer (SP), or alkaline-surfactant-polymer

(ASP)).

The Norne field which is the base case for this study having approximately

current recovery factor of 60% with water flooding is one of the best subsea

fields in the world. The oil production has peaked in 2001 and is now declining.

The pockets of residual oil saturation are still trapped in the reservoir especially

in the Ile and Tofte formations. The water flooding alone cannot recover capillary

trapped oil pockets efficiently, thus requires enhanced oil recovery techniques.

The EOR screening criteria suggested by Taber et al. (1) was applied to Norne E-

segment in order to come up with the right EOR method that would reduce

residual oil saturation to the minimum. Five EOR scenarios such as surfactant

flooding, alkaline-surfactant flooding, polymer flooding, surfactant-polymer

flooding, and alkaline-surfactant-polymer flooding were simulated for the Norne

E-segment.

The main objective of this study was to do a comparative simulation study to

evaluate the effectiveness of these chemical methods (scenarios) compared to a

conventional waterflooding in terms of incremental oil production. After this,

one of the flooding methods was to be concluded for the Norne E-segment based

on incremental net present value (NPV).

The injection well F-3H and producer E-2H were evaluated as the most promising

wells for above scenarios. A series of cases were run to ascertain the injection

length, appropriate surfactant quantity and concentration. Five scenarios with

different combination and concentrations of chemicals (alkali, surfactant and

polymer) were run using Eclipse 100. In addition, calculation of incremental NPV

based on incremental oil production for all scenarios; single parameter sensitivity

analysis (Spider plot) for low case, base case, and high case at different oil prices,

chemicals prices, and discount rate were also performed. It was found that

iii

Abstract

change in oil price has substantial effect on NPV compared to other parameters

while surfactant price is the least sensitive parameter i.e very low affect on NPV

for high/low case.

From simulation results and economics analysis, ASP flooding was found to be

better than other chemical methods (scenarios) in terms of incremental NPV for

the Norne E-segment. However, the 1.40 % incremental recovery factor by ASP

flooding seems not so high and will have an incremental net present value of

+123.53 million USD. It is noted that the additional costs regarding operations

and installations were not included in the economics calculation. The recovery

factor as well as NPV can be optimized with accurate chemical designing in

laboratory and modeling of compositional model in Chemical Compositional

Simulator. It is recommended that right alkali, surfactant and polymer structure

that would be compatible with fluid and rock properties of Norne field E-

segment, be developed in the laboratory. It is also important that up-scaling the

appropriate laboratory identified chemicals to a field-scale usage be done

correctly. The timing of ASP injection into Norne E-segment is also recommended

to be early in the life of the field because injection of ASP at a later time might

not lead to best possible oil recovery.

iv

Dedication

Dedication

My great thanks go to my respected parents, my in-laws, my siblings, my sweet

wife Hemlata Maheshwari, my two lovely kids Yash Kishore Maheshwari, Muskan

Maheshwari and cute nieces Jiya and Harsha for their prayers, patience and

support during my master program. I am sorry that I could not spend time with

you during my studies.

My sincere gratitude goes to Mr. Muhammad Mureed Rahimoon, my father-in-

law Mr. Abheman Ladher and, my relatives Mr. Ashok Kumar Ranjhani and Mr.

Chaman Lal for their cooperation.

v

Acknowledgements

Acknowledgements

I would like to express my deep gratitude to my supervisor Professor Jon Kleppe,

Head of Department and Richard Rwechungura, PhD Scholar for their continuous

help, encouragement and advice during the thesis period. Great thanks to Dr.

Lars Høier (Statoil), Nan Cheng (Statoil), specialist Jan Åge Stensen (SINTEF),

Charles A. Kossak (Schlumberger), and Kippe Vegard (Statoil). I thank you all for

providing me with alkaline/surfactant/polymer properties and advises through

email. I feel the need to thank my friends Arindam Arang, Chinenye Clara and

Samson Imoh Essien for their suggestions and ideas.

I also wish to say a big thank you to the Center of Integrated Operations at

NTNU, Statoil ASA and its license partners ENI and Petoro for the release of

Norne data.

Finally, I wish to give my sincere thanks to Lånekassen (QUOTA Scheme) and

department of Petroleum Engineering and Applied Geophysics for providing me

financial support to pursue my graduate studies.

Trondheim, August 2011

Yugal Kishore Maheshwari [email protected]

vi

Nomenclature

Nomenclature

ɸ porosity

interfacial tension between the displaced and the displacing fluids

mass density of the rock formation

Mobility

Todd-Longstaff mixing parameter

adsorption multiplier at alkaline concentration

alkaline concentrations

alkaline adsorption concentration

surfactant/polymer adsorbed concentration

polymer adsorption concentration

polymer and salt concentrations respectively in the aqueous

phase

cell center depth

water production rate

relative permeability reduction factor for the aqueous phase due

to polymer retention

dead pore space within each grid cell

transmissibility

displaced fluid viscosity

shear viscosity of the polymer solution (water + polymer)

effective water viscosity

µs Surfactant viscosity

µws Water-surfactant solution viscosity

µw Water viscosity

effective viscosity of salt

effective viscosity of the water (a=w), polymer (a=p) and salt (a=s).

pore velocity

vii

Nomenclature

block pore volume

ASP Alkaline, surfactant and polymer

AS Alkaline and surfactant

AP Alkaline and polymer

CDC Capillary Desaturation Curve

CMC Critical Micelle Concentration

Cunit A unit constant

CA(Csurf ) Adsorption as a function of local surfactant concentration

EOR Enhanced Oil Recovery

IEA International Energy Agency

IFT Interfacial Tension

K Permeability

MD Mass Density

Nc Capillary Number

NPD Norwegian Petroleum Directory

NPV Net Present Value

P Potential

Pcow Capillary pressure

Pcow(Sw) Capillary pressure from the initially immiscible curve scaled

according to the end points

Pref Reference pressure

PORV Pore volume in a cell

Sorw Residual oil saturation after water flooding

ST Interfacial tension

ST(Csurf) Surface tension with present surfactant concentration

ST(Csurf = 0) Surface tension with no surfactant present

viii

<Table of Contents

Table of Contents

Abstract ................................................................................................................................ ii

Dedication ........................................................................................................................... iv

Acknowledgements.............................................................................................................. v

Nomenclature ..................................................................................................................... vi

Table of Contents .............................................................................................................. viii

List of Figures ..................................................................................................................... xii

List of Tables ..................................................................................................................... xiv

1 Introduction ............................................................................................................... 1

1.1 Objective ............................................................................................................. 3

1.2 Enhanced Oil Recovery ....................................................................................... 5

1.3 Classification of EOR Processes ........................................................................... 5

1.4 Principles of Enhanced Oil Recovery (EOR)......................................................... 7

1.4.1 Improving the Mobility Ratio ...................................................................... 7

1.4.2 Increasing the Capillary Number ................................................................. 8

2 Norne Field ................................................................................................................. 9

2.1 General Field Information ................................................................................... 9

2.2 Development..................................................................................................... 10

2.3 Geology of the Norne Field ............................................................................... 11

2.3.1 Stratigraphy and Sedimentology .............................................................. 11

2.4 Reservoir Communication................................................................................. 13

2.4.1 Faults ......................................................................................................... 13

2.4.2 Stratigraphic barriers ................................................................................ 13

2.5 Drainage Strategy.............................................................................................. 14

3 Norne Field E-Segment ............................................................................................. 16

3.1 The Reservoir Simulation Model ....................................................................... 16

3.2 EOR Potential in Norne E-segment ................................................................... 18

3.3 EOR Screening Criteria of the Norne E-Segment .............................................. 20

4 Overview of Surfactant, Alkaline and Polymer Flooding ......................................... 22

4.1 Surfactant Flooding ........................................................................................... 22

ix

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4.1.1 Basic Surfactant Classification .................................................................. 22

4.1.2 Methods to Characterize Surfactants ....................................................... 23

4.1.3 Surfactant Flooding in Petroleum Reservoirs ........................................... 25

4.2 Polymer Flooding .............................................................................................. 31

4.2.1 Types of Polymers ..................................................................................... 31

4.2.2 Polymer Flow Behavior in Porous Media .................................................. 34

4.3 Alkaline Flooding ............................................................................................... 36

4.3.1 Alkaline Reaction with Crude Oil............................................................... 37

4.4 Alkaline Surfactant Polymer (ASP) Flooding ..................................................... 39

5 Simulation of Alkaline, Surfactant and Polymer Flooding ........................................ 41

5.1 The Surfactant Flood Model ............................................................................. 41

5.2 The Polymer Flood Model ................................................................................. 41

5.3 The Alkaline Flood Model ................................................................................. 41

5.4 Alkaline, Surfactant and Polymer Properties .................................................... 42

5.5 Performance and Economic Evaluation ............................................................ 42

5.6 Synthetic Model for Testing of Alkaline, Surfactant and Polymer Flooding

Models .......................................................................................................................... 43

5.7 Overview of Base Case ...................................................................................... 46

5.8 Selection of Injector and Producer ................................................................... 46

5.9 Assumptions ...................................................................................................... 48

6 Simulation Results and Analysis ............................................................................... 50

6.1 Scenario 1: Surfactant Flooding ........................................................................ 51

6.1.1 Continuous Surfactant Injection ............................................................... 51

6.1.2 Surfactant Slug Injection ........................................................................... 52

6.1.3 Continuous Surfactant Injection vs. Surfactant Slug Injection .................. 52

6.1.4 Appropriate Surfactant Concentration ..................................................... 52

6.1.5 Cumulative incremental oil production for surfactant flooding ............... 56

6.2 Scenario 2: Alkaline-Surfactant (AS) Flooding .................................................. 57

6.2.1 AS flooding at concentrations of 2 kg/m3 and 0.3 kg/m3 respectively ..... 57

6.2.2 AS flooding at concentrations of 2 kg/m3 and 2 kg/m3 respectively ....... 57

6.2.3 AS flooding at concentrations of 2 kg/m3 and 5 kg/m3 respectively ....... 57

x

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6.3 Scenario 3: Polymer Flooding ........................................................................... 60

6.3.1 Polymer flooding at concentration of 0.5 kg/m3 ...................................... 60



6.4 Scenario 4: Surfactant-Polymer (SP) Flooding .................................................. 63

6.4.1 Surfactant slug (5 kg/m3) followed by polymer (0.4 kg/m3) ..................... 63

6.4.2 SP slug (5 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3) .............. 63

6.5 Scenario 5: Alkaline-Surfactant-Polymer (ASP) Flooding .................................. 65

6.5.1 ASP slug (2 kg/m3, 0.3 kg/m3 & 0.4 kg/m3) followed by polymer (0.4

kg/m3) 65

6.5.2 Desorption / no desorption of surfactant and polymer ........................... 65

6.6 Comparison between Incremental NPV for all Scenarios ................................. 68

6.7 Single Parameter Sensitivity Analysis (Spider Plot) for ASP Flooding ............... 70

7 Discussion and Summary ......................................................................................... 72

8 Conclusion and Recommendation ........................................................................... 75

8.1 Conclusion ......................................................................................................... 75

8.2 Recommendation .............................................................................................. 75

Bibliography ...................................................................................................................... 76

APPENDICES ...................................................................................................................... 79

A. The Surfactant Model in Eclipse ........................................................................... 80

A.1 Calculation of the Capillary Number ............................................................. 80

A.2 Relative Permeability Model ......................................................................... 80

A.3 Capillary Pressure .......................................................................................... 81

A.4 Water PVT Properties ................................................................................... 81

A.5 Adsorption .................................................................................................... 82

A.6 Keywords for Surfactant Flood Model in Eclipse 100 ................................... 82

B The Polymer Flood Model in Eclipse ..................................................................... 84

B.1 Material Balance for Polymer Flooding ........................................................ 84

B.2 Treatment of Fluid Viscosities ....................................................................... 85

B.3 Treatment of Polymer Adsorption ................................................................ 85

B.4 Treatment of Permeability Reductions and Dead Pore Volume ................... 85

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B.5 Treatment of Shear Thinning Effect .............................................................. 86

B.6 Keywords for Polymer Flood Model in Eclipse 100 ...................................... 86

C The Alkaline Flood Model in Eclipse ..................................................................... 88

C.1 Alkaline Conservation Equation .................................................................... 88

C.2 Treatment of Adsorption .............................................................................. 88

C.3 Alkaline Effect on Water-Oil Surface Tension ............................................... 88

C.4 Alkaline Effect on Surfactant/Polymer Adsorption ....................................... 89

C.5 Keywords for Polymer Flood Model in Eclipse 100 ...................................... 89

D Alkaline Input File.................................................................................................. 90

E Surfactant Input File .............................................................................................. 91

F Polymer Input File ................................................................................................. 92

Prediction Input File ........................................................................................................ 104

xii

List of Figures

List of Figures

Figure 1.1: Oil Recovery Mechanisms (12) ............................................................................ 6

Figure 1.2: Water fingering into the oil bank for unfavorable mobility ratios (M >1) (7) .... 8

Figure 2.1: Location of the Norne Field and Field Segments (14) ......................................... 9

Figure 2.2: Stratigraphical sub-division of the Norne reservoir (18) ................................... 12

Figure 2.3: Structural Cross section through the Norne Field with fluid contacts and faults

(16) ...................................................................................................................................... 14

Figure 2.4 NE-SW cross-section of fluid contacts and drainage strategy for the Norne

Field (19) .............................................................................................................................. 15

Figure 2.5: The drainage strategy for the Norne Field from pre-start to 2014 (19) ........... 15

Figure 3.1 The coarsened simulation model of Norne Field showing E-segment ............ 17

Figure 3.2: Recovery factor for the Norne E-Segment ...................................................... 18

Figure 3.3: Oil-in-place for the Norne E-Segment ............................................................. 18

Figure 3.4: Oil saturation in the Ile formation (layer 5), November 2004 ........................ 19

Figure 3.5: Oil saturation in the Ile formation (layer 8), November 2004 ........................ 19

Figure 3.6: Oil saturation in the Tofte formation (layer 12), November 2004 ................. 19

Figure 3.7: Oil gravity range of oil that is most effective for EOR methods (1).................. 21

Figure 4.1: Representative surfactant molecular structures (22) .......................................... 23

Figure 4.2: Classification of surfactants and examples (23) ................................................ 23

Figure 4.3: Schematic definition of the critical micelle concentration (CMC) (22) ............. 24

Figure 4.4: Effect of wettability on residual saturation of wetting and non-wetting phase

(24) ...................................................................................................................................... 26

Figure 4.5: Effect of pore-size distribution on the CDC (24) ................................................ 27

Figure 4.6: Schematic S-shaped adsorption curve (24) ....................................................... 30

Figure 4.7: Fingering effect with water flooding (27) .......................................................... 32

Figure 4.8: Decreased effects of fingering with polymer flooding (27) .............................. 32

Figure 4.9: Partially hydrolyzed polyacrylamide (22) .......................................................... 32

Figure 4.10: Molecular structure of Xanthan .................................................................... 33

Figure 4.11 : Schematic of alkaline recovery process (2) .................................................... 38

Figure 4.12 : ASP flooding process in 5-spot well pattern (31) ........................................... 40

Figure 5.1: Synthetic Model .............................................................................................. 43

Figure 5.2: Oil recovery factor for synthetic model .......................................................... 44

Figure 5.3: Oil production rate for synthetic model ......................................................... 45

Figure 5.4: Water production rate for synthetic model ................................................... 45

Figure 5.5: Recovery factor for base case ......................................................................... 46

Figure 5.6: Wells location in E-segment............................................................................ 47

Figure 5.7: Comparison of recovery factor between different surfactant injection wells 47

Figure 5.8: Comparison of total surfactant injection between different surfactant

injection wells ................................................................................................................... 48

xiii

List of Figures

Figure 6.1: Total Oil production for continuous surfactant flooding for 3 years and 6

years .................................................................................................................................. 53

Figure 6.2: Total surfactant injection for continuous surfactant flooding for 3 years and 6

years .................................................................................................................................. 53

Figure 6.3: Total oil production for surfactant slug flooding for 3 months and 6 months

intervals ............................................................................................................................ 54

Figure 6.4: Total surfactant injection for 3 months and 6 months intervals .................... 54

Figure 6.5: Total oil production: Comparison between slug and continuous injection .... 54

Figure 6.6: Total surfactant injection: Comparison between slug and continuous injection

.......................................................................................................................................... 55

Figure 6.7: Total oil production for different surfactant concentrations ......................... 55

Figure 6.8: Total surfactant injection for different surfactant concentrations ................ 55

Figure 6.9: Scenario 1: Cumulative incremental oil production for surfactant flooding .. 56

Figure 6.10: Scenario 2: Cumulative incremental oil production for alkaline-surfactant

flooding ............................................................................................................................. 58

Figure 6.11: Scenario 2: Total alkaline-surfactant injection for different surfactant

concentrations .................................................................................................................. 59

Figure 6.12: Scenario 2: Surfactant production rate for different concentrations ........... 59

Figure 6.13: Scenario 2: Reservoir pressure for alkaline-surfactant flooding .................. 59

Figure 6.14: Scenario 3: Water Production rate for polymer flooding ............................. 61

Figure 6.15: Buckley Leverett solution for polymer flooding compared to water flooding (35) ...................................................................................................................................... 62

Figure 6.16: Scenario 3: Cumulative incremental oil production for polymer flooding ... 62

Figure 6.17: Scenario 3: Reservoir pressure for polymer flooding ................................... 62

Figure 6.18: Scenario 4: Cumulative incremental oil production for surfactant-polymer

flooding ............................................................................................................................. 64

Figure 6.19: Scenario 4: Water production total for surfactant-polymer flooding .......... 64

Figure 6.20: Scenario 5: Cumulative incremental oil production for ASP flooding .......... 66

Figure 6.21: Scenario 5: Reservoir pressure for ASP flooding .......................................... 66

Figure 6.22: Scenario 5: Oil recovery factor for ASP flooding ........................................... 67

Figure 6.23: Scenario 5: Effect of desorption or no desorption of surfactant in block (7,

57, 9) ................................................................................................................................. 67

Figure 6.24: Scenario 5: Effect of desorption or no desorption of polymer in block (7, 57,

9) ....................................................................................................................................... 67

Figure 6.25: Incremental NPV for all scenarios ................................................................. 69

Figure 6.26: Single parameter sensitivity analysis (Spider Plot) for Scenario 5 ................ 71

xiv

List of Tables

List of Tables

Table 2.1: NPD's estimates of recoverable and remaining reserves as of 31.12.2010 (17) 10

Table 3.1: Wells Status in the Norne field E-Segment ...................................................... 16

Table 3.2: Fluid parameters for the Norne Field (20) .......................................................... 17

Table 3.3: Summary of screening criteria for EOR Methods (1; 7) ...................................... 21

Table 3.4 : Reservoir and oil parameters for the Norne Field (14; 16; 8) ............................... 21

Table 5.1: Discount rate, oil Price, and chemicals prices .................................................. 49

Table 5.2: Oil prices, chemical prices, and discount rate for sensitivity analysis (Spider

chart) of scenario 5 ........................................................................................................... 49

Table 6.1: Single parameter sensitivity analysis ............................................................... 71

Table G.12: Scenario 1: Incremental NPV for Surfactant Slug …………………………..…….……93

Table G.13: Scenario 2 (6.2.1): Incremental NPV for Alkali-Surfactant Slug…..………….….94

Table G.14: Scenario 2 (6.2.2): Incremental NPV for Alkali-Surfactant Slug…..……….…….95

Table G.15: Scenario 2 (6.2.3): Incremental NPV for Alkali-Surfactant Slug…..……..……….96

Table G.16: Scenario 3 (6.3.1): Incremental NPV for Continuous Polymer Injection ……..97

Table G.17: Scenario 3 (6.3.2): Incremental NPV for Continuous Polymer Injection …….98

Table G.18: Scenario 3 (6.3.3): Incremental NPV for Continuous Polymer Injection …….99

Table G.19: Scenario 4 (6.4.1): Incremental NPV for Surfactant Slug ………………………...100

Table G.20: Scenario 4 (6.4.2): Incremental NPV for SP Slug …………………………………….101

Table G.21: Scenario 5 (6.5.1): Incremental NPV for ASP ………………………………………..…102

Table G.22: Single parameter sensitivity analysis ……………………………………………………….103

1

Introduction

1 Introduction

Today fossil fuels supply more than 85% of the world’s energy, with oil and gas

share in the world demand being more than 30%. Currently, we are producing

approximately 87 million barrels per day - 32 billion barrels per year in the world

(2). Last year, world energy consumption grew 5.6% and energy consumption is

now growing faster than the world economy (3). That means every year the

industry has to find twice the remaining volume of oil in the North Sea just to

meet the target to replace the depleted reserves. To satisfy the increasing global

energy demand and consumption forecast during the next decades, a more

realistic solution to meet this need lies in sustaining production from existing

fields for several reasons (2):

The industry cannot guarantee new discoveries.

New discoveries are most likely to lie in offshore, deep offshore or

problematic areas and will not be sufficient to meet our needs.

Producing unconventional resources like oil sands and oil shales would be

more expensive than producing from existing brown field by enhanced oil

recovery (EOR) methods.

The average recovery rate from fields on the Norwegian shelf is currently 46 %,

whereas an average of 50% is set as the target by Norwegian Petroleum of

Directorate (NPD) (4; 5). Among other technologies, EOR is one of the solutions to

meet this goal. Some of the EOR technologies that have been initiated in the

North Sea from 1975 to 2005 include hydrocarbon (HC) miscible gas injection,

water-alternating-gas injection (WAG), simultaneous water-and-gas injection

(SWAG), foam assisted WAG (FAWAG) injection, and microbial enhanced oil

recovery (MEOR) (4). Other methods including chemical flooding (surfactant,

polymer, ASP) have not been tested on fields in the North Sea. This is due to

some of the environmental issues (5). The research regarding the flooding of

these chemicals in North Sea reservoirs is going on and is planned to be carried

out in the future (4).

The Norne Field which is located on the Norwegian shelf achieved peak

production in 2001 and now declining. The current recovery factor of Norne Field

(6) is around 60% and is expected to end at approx. 65% by simply water flooding.

Although, the end recovery of Norne field is quite higher than the overall target

2

Introduction

set by NPD for the Norwegian shelf, it can further be improved by Chemical EOR

methods.

Many graduates have published their master thesis, projects and group projects

regarding reservoir simulation of chemical EOR techniques applied to Norne E-

segment. Clara (2010) (7) and Kalnaes (2010) (8), in their master thesis reported

that the surfactant flooding is a good option for Norne E-segment when Ile and

Tofte formations are targeted.

Awolola et al. (2011) (9) concluded that the surfactant flooding with high oil price

can be a good alternative for enhanced oil recovery in the Norne E-segment.

Sundt et al. (2011) (10) after comparative simulation study of polymer flooding

and surfactant flooding recommended surfactant flooding as an EOR method in

the Norne E-segment based on Net Present Value. All of the student researchers

emphasized on surfactant flooding for Norne E-segment.

This thesis focuses on simulation of different combination and concentrations of

chemicals (alkali, surfactant and polymer) using Eclipse 100. Based on

incremental NPV, one of the chemical EOR methods for Norne E-segment is to be

concluded among the five scenarios such as surfactant flooding, alkaline-

surfactant flooding, polymer flooding, surfactant-polymer flooding, and alkaline-

surfactant-polymer flooding. In addition, single parameter sensitivity analysis

(Spider plot) for low case, base case, and high case at different oil prices,

chemical prices, and discount rate will be performed.

3

Introduction

1.1 Objective

In the Norne field E-segment, the pockets of by-passed residual oil are still

trapped in the reservoir especially in the Ile and Tofte formations. With

continuous rise in water cut and reduced oil production, it becomes obvious that

water flooding alone cannot recover oil effectively, thus requires chemical EOR

methods to release capillary trapped oil pockets.

The main aim of this study is to do a comparative simulation study to evaluate

the effectiveness of following chemical EOR methods compared to a

waterflooding in terms of incremental oil production and in the end comparison

between all five scenarios will be discussed in order to see which is the most

suitable and profitable method in terms of net present value (NPV) for the Norne

E-segment.

Scenario 1: Surfactant Flooding

a. Continuous Surfactant Injection

b. Surfactant Slug Injection

c. Continuous Surfactant Injection vs. Surfactant Slug Injection

d. Appropriate Surfactant Concentration

e. Cumulative incremental oil production for surfactant flooding

Scenario 2: Alkaline-Surfactant (AS) Flooding

a) AS flooding at concentrations of 2 kg/m3 and 0.3 kg/m3 respectively

b) AS flooding at concentrations of 2 kg/m3 and 2 kg/m3 respectively

c) AS flooding at concentrations of 2 kg/m3 and 5 kg/m3 respectively

Scenario 3: Polymer Flooding

a) Polymer flooding at concentration of 0.5 kg/m3

b) Polymer flooding at concentration of 0.7 kg/m3

c) Polymer flooding at concentration of 1.0 kg/m3

Scenario 4: Surfactant-Polymer (SP) Flooding

a) Surfactant slug (5 kg/m3) followed by polymer (0.4 kg/m3)

b) SP slug (5 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3)

4

Introduction

Scenario 5: Alkaline-Surfactant-Polymer (ASP) Flooding

a) ASP slug (2 kg/m3, 0.3 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3)

b) Desorption / no desorption of surfactant and polymer

Comparison between Incremental NPV for all Scenarios

Plot of Incremental NPVs of all Scenarios Single Parameter Sensitivity Analysis (Spider Chart)

low case, base case and high case

5

Introduction

1.2 Enhanced Oil Recovery

The life of an oil well goes through three distinct phases where a variety of

techniques are employed to sustain crude oil production at maximum levels. The

primary importance of these techniques is to force oil into wellhead where it can

be pumped to the surface. Techniques employed at the third phase, commonly

known as Enhanced Oil Recovery (EOR), can substantially improve extraction

efficiency. Depending on the producing life of a reservoir, oil recovery can be

defined in three stages: primary, secondary and tertiary (2) (Figure 1.1).

Primary recovery: is the first step of recovery by natural drive energy

initially available in the reservoir without injection of any fluids or heat

into the reservoir. The natural energy sources include rock and fluid

expansion, solution gas, water influx, gas cap, and gravity drainage.

Secondary recovery: is the second step of recovery by injection of

external fluids, such as water flooding and/or gas injection, mainly for the

purpose of pressure maintenance and volumetric sweep efficiency.

Tertiary recovery: is the third step of recovery after secondary recovery

also known as Enhanced Oil Recovery, introduces fluids that reduce

viscosity and improve flow. It is characterized by injection of special fluids

such as chemicals, miscible gases, and/or the injection of thermal energy.

Another term, improved oil recovery (IOR), is also in used oil industry, which

includes EOR but also encompasses a broader range of activities e.g., reservoir

characterization, improved reservoir management and infill drilling (2).

1.3 Classification of EOR Processes

The main objective of all methods of EOR is to increase the volumetric

(macroscopic) sweep efficiency and to enhance the displacement (microscopic)

efficiency, as compared to ordinary waterflooding. One mechanism is aimed

towards the increase in volumetric sweep by reducing the mobility ratio between

the displacing and displaced fluids. Since the mobility of the injected fluid is

reduced, the tendency to the fingering effect is much lowered.

The other mechanism is targeted to the reduction of the amount of oil trapped

due to the capillary forces (microscopic entrapment). By reducing interfacial

tension between the displacing and displaced fluids the effect of microscopic

trapping is lowered, yielding a lower residual oil saturation and hence higher

6

Introduction

ultimate recovery. So, the final recovery factor depends upon the microscopic

displacement efficiency and on volumetric efficiency of the displacement front (11).

There are four major categories of enhanced oil recovery:

1. Chemical Process

2. Thermal Recovery

3. Miscible Injection

4. Other (Microbial, electrical)

The further classification of EOR is shown in Figure 1.1; the arrangement shows

several methods which are outlined in a systematic and balanced manner. Often

these methods are used in combinations of one or more other methods in order

to bring the effect and efficient in oil recovery process than using individual

method. Alkaline-Surfactant-Polymer flooding can be an example which shows

the combination of the methods indicated.

Figure 1.1: Oil Recovery Mechanisms (12)

7

Introduction

1.4 Principles of Enhanced Oil Recovery (EOR)

A given EOR method can have one or more of several goals, which are as follows,

1.4.1 Improving the Mobility Ratio

Mobility ratio (M) is defined as the mobility of the displacing fluid divided by the

mobility of the displaced fluid (13).

(1.1)

where

= Mobility

k = Effective permeability

µ = Fluid viscosity

i = oil, water or gas

For maximum displacement efficiency, M should be ≤1, usually denoted as

favorable mobility ratio. If M>1 (unfavorable), then it means that the displacing

fluid, e.g., water in a waterflood, moves more easily than the displaced liquid,

i.e., oil. This is not desirable, because the displacing fluid will flow past the

displaced fluid, given rise to a phenomenon called ‘viscous fingering’ where most

of the oil is by-passed (Figure 1.2). However, if M >1, then in the absence of

viscous fingering, it means that more fluid will be injected to attain a given

residual oil saturation in the pores. Thus, for effective displacement of fluid, the

mobility ratio is very important.

Mobility ratio M can be made smaller, in order words, improved by one of the

following ways;

Lowering the viscosity of the displaced fluid, i.e., oil

Increasing the viscosity of displacing fluid

Increasing the effective permeability to oil

Decreasing the effective permeability to the displacing fluid.

8

Introduction

1.4.2 Increasing the Capillary Number

The Capillary number, Nc is defined as the dimensionless ratio between the

viscous and the capillary forces, given by (13)

(1.2)

where

= displaced fluid viscosity

= pore velocity

= interfacial tension between the displaced and the displacing fluids

= effective permeability to the displaced fluid

= pressure gradient across distance L

The capillary number can be increased, and thereby the residual oil saturation

decreased, by either reducing oil viscosity or increasing pressure gradient, but

more than anything, by decreasing the interfacial tension (IFT).

Figure 1.2: Water fingering into the oil bank for unfavorable mobility ratios (M >1) (7)

9

Norne Field

2 Norne Field

2.1 General Field Information

The Norne Field is located 200 km from the Norwegian coastline and 85 km north

of Heidrun field. It is situated in the blocks 6608/10 and 6508/1 in the Southern

part of the Nordland II area. The Horst block is approximately 9 km x 3 km. Figure

2.1 shows a map of the location of the Norne field relative to other fields. The

water depth in the area is about 380 m. The field is operated by Statoil ASA and

license partners Eni Norge AS and Petoro.

The Norne field was discovered with well 6608/10-2 in 1991. Based on discovery

well, the total hydrocarbon bearing column of 135 m was found with a 110 m

thick oil leg and 25 m overlying gas cap. The reservoir comprises sandstones of

Middle and Late Jurassic age of excellent quality. These findings were later

confirmed with the appraisal well (6608/10-3) in 1993. The reservoir depth is

about 2500 meters (14; 15).

Figure 2.1: Location of the Norne Field and Field Segments (14)

10

Norne Field

The Norne field consists of two separate oil compartments;

Norne Main Structure (C-, D- and E-segment), discovered in December 1991, containing 97% of the oil in place.

Norne North-East Segment (G-segment) (Figure 2.1)

The Norne main structure is relatively flat containing approximately 80% of oil in

Ile and Tofte formation and gas in the Garn formation above the Not formation

claystone (shale). The underlying water zone is generally in the heterogeneous

Tilje formation and there is no much evidence for aquifer support. The Gas-oil

contact (GOC) is in the vicinity of Not formation Shale which acts as a barrier

throughout the field. This was shown by acquired pressure data from

development wells that increase in pressure in Garn formation and pressure

decline in Ile and Tofte formation, indicating that there is no reservoir

communication across the Not Shale formation during production/injection (14;

16).

2.2 Development

The development drilling began with well 6608/10-D-1 H in August 1996. The oil

production started on November 6th 1997 and oil is being produced by only

water injection as the drive mechanism. In the early days gas was injected, which

was ceased in 2005 and all gas is now being exported. The field has been

developed with six subsea wellhead templates named B, C, D, E, F and K that are

connected to floating production and storage vessel, ‘Norne FPSO’ with flexible

risers. The oil is loaded to tankers for export, while the gas is transported

through pipeline to Åsgard and onward to Kårstø (14; 15)

The NPD has provided the figures in Table 2.1 for the total production,

estimation of recoverable and remaining reserves as of 31st December 2010

(Norwegian share).

Table 2.1: NPD's estimates of recoverable and remaining reserves as of 31.12.2010 (17)

Reserves Oil

(mill Sm3) Gas

(mill Sm3) NGL

(mill ton) Condensate (mill Sm3)

Recoverable 93.40 11.70 1.70 0.00

Produced 84.60 6.20 0.80 0.00

Remaining 8.80 5.50 0.90 0.00

11

Norne Field

2.3 Geology of the Norne Field

The Norne reservoir rock is comprised of Jurassic sandstones, mainly dominated

by fine-grained and well to very well sorted sub-arkosic arenites. The sandstones

are buried at a deep depth of 2500 m to 2700 m and are affected by digenetic

process, which reduces reservoir quality due to mechanical compaction. Even

though, most of the sandstones are of good quality and the porosity is in the

range of 25 % to 30 % while permeability is between 20-2500 mD (15).

2.3.1 Stratigraphy and Sedimentology

The Norne reservoir is classified in two major groups, the FANGST which consists

of the Garn, Not and Ile formations and the BÅT which includes the ROR, Tofte,

Tilje and Åre formations. These formations are further subdivided into sub

formations as shown in Figure 2.2. The Ile and Tofte containing 36% and 44% of

the proven oil respectively are the most important reservoir formations and

therefore will be focused during this thesis work (18).

2.3.1.1 Ile Formation

The Ile formation was deposited during the Aalenian and is 32-40 m thick

sandstone. The formation is divided into three zones; Ile 1, Il 2 and Ile 3. The Ile 1

& Ile 2 and Ile 1 & ROR formations are separated by a cemented calcareous layer

as can be seen in Figure 2.2. These calcareous layers are probably the result of

minor flooding events in a generally regressive period, which might form barrier

to vertical fluid flow and is therefore important in the reservoir modeling (18). The

Ile formation is subdivided into 7 parts (layer 5 to 11) in the reservoir model.

2.3.1.2 Tofte Formation

The Tofte formation was deposited on the top of the unconformity during Late

Toarcian and is approximately 50 meter thick sandstone. As can be seen in Figure

2.2, the formation is divided into three reservoir zones; Tofte 1, 2 and 3. Tofte 1

consists of medium to coarse grained sandstone with variable but generally very

good reservoir properties. In the middle, the Tofte 2 is a composed of muddy and

fine grained sandstone unit and the top represents Tofte 3 which is very fine to

fine grained sandstone (18). The Tofte formation is subdivided into 7 parts (layer

12 to 18) in the reservoir model.

12

Norne Field

Figure 2.2: Stratigraphical sub-division of the Norne reservoir (18)

13

Norne Field

2.4 Reservoir Communication

The Norne Field reservoir consists of both faults and stratigraphic barriers which

act as restrictions to the vertical and lateral flow. In order to better understand

the reservoir communication and drainage pattern during production, vertical

transmissibility multipliers and fault transmissibility multipliers have been

implemented in the reservoir simulation (16).

2.4.1 Faults

As the Norne field is situated on a horst, a number of faults are expected. A horst

is the raised fault block bounded by normal faults or graben. Figure 2.3 shows

the faults and fluid contacts in the Norne Field reservoir.

To describe the faults in the reservoir simulation model, the fault planes are

divided into sections which follow the reservoir zonation. Each sub-area of the

fault planes has been given transmissibility multipliers. The transmissibility

multipliers are functions of fault rock permeability, the matrix permeability, fault

zone width, and dimensions of the grid blocks (16).

2.4.2 Stratigraphic barriers

Numerous stratigraphic barriers are present in the field, which have been

identified and their lateral extent and thickness variation are assessed by the use

of cores and logs. Some of the continuous intervals which restrict the vertical

fluid flow within the Norne Field are:

Garn 3/Garn 2: Carbonate cemented layer at top Garn 2

Not formation: Claystone formation

Ile 3 /Ile 2: Carbonate cementations and increased clay content at the

base Ile 3

Ile 2/Ile 1: Carbonate cemented layer at base Ile 2

Ile 1 / Tofte 4: Carbonate cemented layer at top Tofte 4

Tofte 2 / Tofte 1: Significant grain size constant

Tilje 3 / Tilje 2: Claystone formation

The Not formation is the most prominent barriers to flow, the carbonate

cemented layers which isolates Ile 1 and Tofte 4, and the interbedded claystone

separating the Tilje 3 and Tilje 2 formations (16).

14

Norne Field

Figure 2.3: Structural Cross section through the Norne Field with fluid contacts and faults (16)

2.5 Drainage Strategy

The main goal to develop the Norne was to obtain an economic optimum

production profile. In 2006, the focus was on optimizing the value creation by (19):

Safe and cost effective drainage of proven reserves

Prove new reserves at optimal timing to utilize existing infrastructure

Explore the potential in the license

Adjust capacities where this could be done cost effectively

Improve drainage strategy with low cost infill wells as multilateral/MLT

and through tubing drilled wells (TTRD and TTML).

Increase reservoir pressure in the Ile formation and the Norne G-

segment.

15

Norne Field

Initially, the drainage strategy was to maintain the reservoir pressure by re-

injection of produced gas into the gas cap and water injection into the water

zone. But, during the first year of production it was experienced that the Not

shale is sealing over the Norne Main Structure, so this non-communication

between the Garn and Ile formations made the plan to be revised. The gas

injection was changed to inject in the water zone and the lower part of the oil

zone with proper monitoring to prevent early breakthrough and increase GOR.

The water injection was started in July 1998 and is being injected in the Tilje

formation (water zone). The gas injection was stopped in 2005 and is now being

exported (Figure 2.4 and Figure 2.5) (19).

Figure 2.4 NE-SW cross-section of fluid contacts and drainage strategy for the Norne Field (19)

Figure 2.5: The drainage strategy for the Norne Field from pre-start to 2014 (19)

16

Norne Field E-Segment

3 Norne Field E-Segment

The Norne E-segment is a part of the Norne main structure (C-, D- and E-

segment). The Ile and the Tofte are the key formations in this segment because

80% oil in the Norne field is contained in these formations. The E-segment is

separated from the rest of the field on an assumption of a constant flux

boundary. This means that we have considered a hypothetical boundary across

which the flow of fluid flowing into the E-segment is equal to the liquid flowing

out. Hence any change in any other segment of reservoir, theoretically will have

no effect on any parameter inside the reservoir.

In the Eclipse model of Norne Field, the E-segment consists of 3 producers and 2

injectors as on 2004 (Table 3.1).

Table 3.1: Wells Status in the Norne field E-Segment

Well Name Type Status

F-1H Water Injector (vertical) Active

F-3H Water Injector (vertical) Active

E-2H Oil Producer (horizontal) Active

E-3H Oil Producer (vertical) Shut

E-3AH Oil Producer (horizontal) Active

3.1 The Reservoir Simulation Model

The Norne field is modeled in Eclipse 100; a fully implicit, three phase, three

dimensional black oil simulator. The reservoir model has 46 grids in the X-

direction, 112 in the Y-direction and 22 layers. Each geological layer (reservoir

zone) is represented by one layer, for example, the Ile is represented by layers 5-

11 and Tofte by 12-18. The model used in this study is a coarsened model which

is made from the original full field reservoir simulation model by Mohsen

Dadashpur at the IO Center. Figure 3.1 shows a coarsened model where only E-

segment is fine gridded (blue color). The simulation with history matched runs

from November 1997 until December 2004 (15). Norne’s fluid properties are given

in Table 3.2.

17


Table 3.2: Fluid parameters for the Norne Field (20)

Fluid Properties Units Norne Main Structure (C,D & E Segment)

Norne G-Segment

Initial pressure bar 273.2 273.2

Bubble point pressure bar 251 216

Gas oil ratio Sm3/Sm3 111 96

Oil formation volume factor at bubble point

Rm3/Rm3 1.347 1.30

Oil viscosity at bubble point cp 0.58 0.695

Oil density at bubble point g/cm3 0.712 0.729

Gas formation volume factor

Rm3/Rm3 4.74 E-3

Initial temperature 0C 98.3 98.3

Figure 3.1 The coarsened simulation model of Norne Field showing E-segment

18


3.2 EOR Potential in Norne E-segment

Petrophysical and geological data shows that approximately 80% of the oil

reserves in the Norne E-segment are located in Ile and Tofte formation; therefore

these two formations are selected as the target area for EOR (18).

In the eclipse model, the Ile and Tofte formations are represented by 5-18 layers.

The simulation was run from 1997 till 2004, and it can be seen from Figure 3.4

and Figure 3.5, the oil saturation is still very high in the Ile formation. Tofte

formation has comparatively low oil saturation (Figure 3.6). The recovery factor

and oil in place of the history matched model are shown in Figure 3.2 and Figure

3.3, where pink line represents history and blue line as prediction. The recovery

factor and oil-in-place are 41.30 % and 1.60 x 106 sm3 in November 2004 and

with future predictions these are recorded as around 54.30 % and 1.24 x 106 sm3

respectively in December 2015. A recovery factor at 54.30 % is an excellent result

with only water flooding, which means that sweep efficiency in the E-segment is

very good. As shown in Figure 3.4 and Figure 3.5, the pockets of oil are still

trapped in the Norne E-segment after water flooding for a number of years,

which means that this can be a good candidate of suitable EOR method.

Figure 3.2: Recovery factor for the Norne E-Segment

Figure 3.3: Oil-in-place for the Norne E-Segment

19


Figure 3.4: Oil saturation in the Ile formation (layer 5), November 2004

Figure 3.5: Oil saturation in the Ile formation (layer 8), November 2004

Figure 3.6: Oil saturation in the Tofte formation (layer 12), November 2004

20


3.3 EOR Screening Criteria of the Norne E-Segment

Screening criteria have been widely used to identify EOR applicability in a

particular field before any detailed evaluation is started. EOR screening

represents a key step to reducing the number of options for further detailed

evaluations. Over the past two decades, many researchers- for example, Taber et

al. (1997a, 1997b), Al-Bahar et al. (2004), Henson et al. (2002) and Dickson et al.

(2010) have developed detailed economic and technical screening criteria for

different EOR processes through modeling/simulation and using laboratory and

field data (2). To perform the EOR screening on the Norne E-segment, we will use

the screening criteria of Taber et al. (1). Table 3.3 shows the summary of

screening criteria which is based on a combination of the reservoir and oil

characteristics of successful projects plus the optimum conditions needed for

good oil displacement by the different fluids. The suggested criteria in Table 3.3

are informative and intended to show approximate ranges of good projects but

they may be misleading (1).

According to Taber et al., a convenient way to show different EOR methods is to

arrange them by oil gravity as shown in Figure 3.7. The size of the type in Figure

3.7 illustrates the relative importance of each of the EOR methods in terms of

incremental oil produced (1).

The reservoir and oil characteristics used for EOR screening of Norne E-segment

are presented in Table 3.4. The API gravity of Norne oil is 32.7o and the other

parameters like oil saturation, formation type, net thickness, permeability and

depth given in Table 3.4 almost satisfy the characteristics for chemical methods

given in Table 3.3 and Figure 3.7, whereas the Norne oil viscosity is lower and

temperature is slightly higher (5 oC more) than the range suggested in Table 3.3.

The author in the light of above discussion decided to simulate chemical

methods (alkaline, surfactant and/or polymer) for the Norne E-segment because

the current drainage strategy is water flooding, which is also an advantageous for

these methods. However, the chemical EOR processes are complex and

expensive, high adsorption and degradation of chemicals can occur at high

temperatures.

21


Table 3.3: Summary of screening criteria for EOR Methods (1; 7)

Table 3.4 : Reservoir and oil parameters for the Norne Field (14; 16; 8)

Reservoir Characteristics Oil Properties

Permeability, mD 20-2500 Gravity (API) 32.7o

Porosity, % 25-30 Viscosity, cp <1.2

Formation type Sandstone Density, kg/m3 859.5

Net thickness, m 110

Reservoir Depth, m 2500-2700

Temperature, oC 98.3

Oil Saturation, % 35-92

Figure 3.7: Oil gravity range of oil that is most effective for EOR methods (1)

22

Overview of Surfactant, Alkaline and Polymer Flooding

4 Overview of Surfactant, Alkaline and Polymer Flooding

4.1 Surfactant Flooding

The term surfactant is a blend of surface acting agents that adsorb on or

concentrate at a surface or fluid/fluid interface to alter the surface properties

significantly; in particular, they decrease surface tension or interfacial tension

(IFT). Surfactants are usually organic compounds that are amphiphilic (Figure

4.1), meaning they are made up of two functional groups, hydrophobic (water-

hating, the “tail”) and polar hydrophilic (water-loving, the “head”). Due to this

nature, they are soluble in both organic solvents and water (2).

4.1.1 Basic Surfactant Classification

Surfactants are categorized into four groups according to the ionic nature of

head group as anionic, nonionic, cationic and Zwitterionic (amphoteric).

1. Anionic This surfactant is classified as anionic because of the negative charge on its head

group. They are most widely used in chemical EOR processes because they are

stable, efficiently reduce IFT, relatively resistant to retention, exhibit relatively

low adsorption on sandstone rocks whose surface charge is negative. Anionic

surfactants can strongly adsorb in carbonate rocks (having positive surface

charge), therefore, they are not used in carbonate rocks.

2. Nonionic Nonionic surfactants have no charge and primarily serve as co-surfactants to

improve the phase behavior. They are more tolerant of high salinity brine, but

their surface active properties to reduce IFT are not as good as anionic

surfactants. Mostly, a mixture of anionic and nonionic is used to increase the

tolerance to salinity.

3. Cationic Cationic surfactants are positively charged and they strongly adsorb in sandstone

rocks; therefore, they are not used in sandstone reservoirs, but they can be used

in carbonate rocks to change wettability from oil-wet to water-wet.

4. Zwitterionic Zwitterionic surfactants also known as amphoteric (positive and negative

charges) contain two active groups. The types of zwitterionic surfactants can be

23


nonionic-anionic, nonionic-cationic, or anionic-cationic. These surfactants are

expensive because they are temperature and salinity-tolerant (2; 21).

Figure 4.1: Representative surfactant molecular structures (22)

Figure 4.2: Classification of surfactants and examples (23)

4.1.2 Methods to Characterize Surfactants

The most common surfactants used in surfactant flooding are petroleum

sulfonates. These are anionic surfactants produced when an intermediate-

molecular-weight refinery stream is sulfonated, and synthetic sulfonates are the

products when a relatively pure organic compound is sulfonated. Sulfonates are

stable at high temperatures but sensitive to divalent ions (2). Several methods to

characterize surfactants are discussed next.

24


4.1.2.1 Hydrophile–Lipophile Balance (HLB)

The hydrophile–lipophile balance (HLB) number indicates the tendency to form

water-in-oil or oil-in-water emulsions. Low HLB numbers are assigned to

surfactants that tend to be more soluble in oil and to form water-in-oil

emulsions. When the formation salinity is low, a low HLB surfactant should be

selected. Such a surfactant can make middle-phase microemulsion at low

salinity. When the formation salinity is high, a high HLB surfactant should be

selected. Such a surfactant is more hydrophilic and can make middle-phase

microemulsion at high salinity (2).

4.1.2.2 Critical Micelle Concentration

CMC is defined as the concentration of surfactants above which micelles are

spontaneously formed. Micelle is an aggregation of molecules which usually

consists of 50 or more surfactant molecules. When surfactants are injected into

the system, they will initially partition into the interface, reducing the system

free energy by lowering the energy of the interface and removing the

hydrophobic parts of the surfactant from contact with water. As the

concentration of surfactant increases and the surface free energy (surface

tension) decreases, the surfactants start aggregating into micelles. Above a

specific concentration, called as critical micelle concentration (CMC), further

addition of surfactants will just increase number of micelles as shown in Figure

4.3. In other words, before reaching the CMC, the surface tension decreases

sharply with the concentration of the surfactant whereas the surface tension

stays more or less constant after reaching the CMC (2; 21).

Figure 4.3: Schematic definition of the critical micelle concentration (CMC) (22)

25


4.1.2.3 Solubilization Ratio

Solubilization is the process of making a normally insoluble material soluble in a

given medium. Solubilization ratio for oil (water) is defined as the ratio of the

solubilized oil (water) volume to the surfactant volume in the microemulsion

phase. Huh (1979) formulated that solubilization ratio is closely related to IFT.

When the solubilization ratio for oil is equal to that for water, the IFT reaches its

minimum (2).

4.1.3 Surfactant Flooding in Petroleum Reservoirs

The purpose of surfactant flooding is to recover the capillary trapped oil after

water flooding. When a surfactant solution has been injected, the trapped oil

droplets are mobilized due to a reduction in the interfacial tension between oil

and water. The coalescence of these drops leads to a local increase in oil

saturation and oil bank is generated. The oil bank will start to flow, mobilizing

any residual oil in front of the bank. Behind the flowing oil bank, the surfactant

will prevent the mobilized oil to be re-trapped. The interfacial tension, the

viscosity, and the volume of the surfactant solution behind the oil bank will

therefore be of importance for the final residual oil saturation.

If the efficiency of surfactant is very good, then the reduction in Interfacial

tension (IFT) could be as much as 104 which corresponds to a value in the

neighborhood of 1μN/m. Due to high cost of surfactant, mostly a small surfactant

slug is displaced by water, usually containing polymer to increase viscosity which

prevents fingering and breakdown down of slug (24). The main aspects of

surfactant flooding are discussed below;

4.1.3.1 Capillary Desaturation Curve (CDC)

To reduce waterflood residual oil saturation, the pressure drop across the

trapped oil has to overcome the capillary forces that keep the oil trapped. This is

done with surfactant which provides such a pressure drop. A large number of

studies have shown that the residual oil saturation corresponds to the capillary

number (Nc), the dimensionless ratio between the viscous and capillary forces. In

general, the capillary number must be higher than a critical capillary number,

(NC)c, for a residual phase to start to mobilize. Practically, this (NC)c is much

higher than the capillary number at normal waterflooding conditions. Another

parameter is maximum desaturation capillary number, (NC)max, above which the

26


residual saturation would not be further decreased in practical conditions even if

the capillary number is increased (2; 24).

The general relationship between residual saturation of a non-aqueous

(nonwetting phase) or aqueous phase (wetting phase) and a local capillary

number is called capillary desaturation curve (CDC). The residual saturations start

to decrease at the critical capillary number as the capillary number increases,

and cannot be decreased further at the maximum capillary number (Figure 4.4).

The CDC for the wetting phase is shifted to the right of the CDC of the non-

wetting phase by two orders of magnitude (see Figure 4.4); this indicates that

surfactant should have better performance in a water-wet system. Figure 4.5

shows that oil saturation starts to drop as pore size becomes narrower at high

capillary number (NC), which means that a reservoir with narrow pore-size

distribution will give the lowest residual oil saturation. In a simulation model, the

efficiency of the surfactant will rely upon CDC, and should therefore be

measured for every distinct rock type (2; 24).

Figure 4.4: Effect of wettability on residual saturation of wetting and non-wetting phase (24)

27


Figure 4.5: Effect of pore-size distribution on the CDC (24)

4.1.3.2 Volumetric Sweep Efficiency and Mobility Ratio

Volumetric sweep efficiency Ev is the volume of oil contacted divided by the

volume of target oil. Ev is a function of surfactant/polymer slug size, retention

and heterogeneity. The mobility ratio has to be as low as possible for an efficient

displacement of the oil bank towards the producing wells. Low mobility ratio

prevents fingering of the surfactant slug into the oil bank and also reduces large-

scale dispersion due to permeability contrasts, gravity segregation and well

pattern. The mobility control agent in the slug can be a polymer or oil. It is of

paramount importance that the slug-oil bank front be made viscosity stable since

small slugs cannot tolerate even a small amount of fingering. It has been

confirmed from simulation studies that low mobility ratio is of great importance

according to recovery, while the size of the surfactant slug gave small differences

in performance (22; 24).

4.1.3.3 Relative Permeabilities

In chemical flooding process, relative permeability is most likely one of the least-

defined parameters. The classic relative permeability curves represent a situation

in which fluid distribution in the system is controlled by capillary forces (2). The

concept of relative permeability is fundamental to the study of the simultaneous

28


flow of immiscible fluids in porous media. Relative permeabilities are influenced

by the following factors; saturation, saturation history, wettability, temperature,

viscous, capillary and gravitational forces (7).

In surfactant-related processes, the interfacial tension is reduced. As IFT is

reduced, the capillary number is increased, leading to reduced residual

saturations. Obviously, residual saturation reduction directly changes relative

permeabilities and the relative permeability curves become closer to straight

lines (exponents close to 1), and the immobile saturations are closer to 0.

Many researchers observed from their experimental results that as water/oil IFT

was reduced, both oil and water relative permeabilities were increased, their end

points were raised, had less curvature, and residual saturations were decreased.

These observations were obvious only when the IFT was below 0.1 mN/m (2).

4.1.3.4 Surfactant Retention

Control of surfactant retention in the reservoir is one of the most important

factors in determining the success or failure of a surfactant flooding project.

Based on the mechanisms, surfactant retention has been identified as

precipitation, adsorption, and phase trapping. These mechanisms all result in

retention of surfactant in a porous medium and deterioration of the composition

of the chemical slug, leading to poor displacement efficiency. Surfactant

retention in reservoirs depends on surfactant type, surfactant equivalent weight,

surfactant concentration, rock minerals, clay content, temperature, pH, flow rate

of the solution, etc. As the equivalent weight of the surfactant increases,

surfactant retention in general also increases (2). Petroleum sulfonates are widely

used in surfactant flooding. The presence of divalent cations (Ca2+, Mg2+) in the

solution causes surfactant precipitation.

Adsorption

Most solid surfaces including reservoir rocks are charged due to different

mineralogy. The reservoir minerals like quartz (silica), kaolinite show a negative

charge while calcite, dolomite and clay have positive charge on their surfaces at

neutral pH of the brine. The adsorption of surfactants at the solid/liquid interface

comes into play by electrostatic interaction between the charged solid surface

(adsorbent) and the surfactant ions (adsorbate). Ion exchange, ion pairing and

hydrophobic bonding are some of mechanisms by which surfactants adsorb onto

mineral surfaces of rock (7). Nonionic surfactants have much higher adsorption on

29


a sandstone surface than anionic surfactants whereas for calcite is reverse. Thus,

nonionic surfactants might be candidates for use in carbonate formations from

the adsorption point of view (2; 25).

An example of an isotherm for the adsorption of a negatively charged surfactant

onto an adsorbent with positively charged sites is S-shaped. Figure 4.6 shows

four different regions reflecting distinct modes of adsorption (24).

Region 1: In this region, the surfactant is mainly adsorbed by anionic exchange

and shows a linear relationship between adsorbed material and equilibrium

concentration.

Region 2: A remarkable increase in adsorption due to the interaction between

the hydrophobic chains of the oncoming surfactant and the surfactant that

already has been adsorbed.

Region 3: A decrease in adsorption of surfactants because the adsorption has to

overcome the electrostatic repulsion between surfactant and the similarly

charged solid.

Region 4: A plateau adsorption is obtained above the Critical Micelle

Concentration (CMC), which means that surfactant adsorption will not increase

onto the surface.

The interfacial tension between oil and water decreases until the CMC is

reached. The shape of the adsorption isotherm may vary for different systems,

and some factors that influence the plateau are salinity, pH-value, temperature

and wettability. With increased salinity the plateau adsorption will increase while

a decrease in pH will cause an increase in adsorption. It is suggested that

surfactant adsorption decrease as the temperature increases (8).

30


Figure 4.6: Schematic S-shaped adsorption curve (24)

One of the ways of reducing adsorption in chemical flooding is by doing a ‘pre-

flush’ with different type of sacrificial chemicals like NaCl, NaOH, phosphates,

silicates, lignosulfonates and polyethylene oxide in order to reduce hardness,

make the reservoir rock more negative charged and block the active sites of the

rock (24).

Phase Trapping

This form of retention is strongly affected by the phase behavior. Phase trapping

could be caused by mechanical trapping, phase partitioning, or hydrodynamical

trapping. It is related to multiphase flow. The mechanisms are complex, and the

magnitude of surfactant loss owing to phase trapping could be quite different

depending on multiphase flow conditions. Glover et al. (1979) found that the

onset of phase trapping with a surfactant flooding process generally occurred at

higher salt concentrations because it would form upper-phase microemulsion so

that the surfactant would be trapped in the residual oil. Krumrine (1982)

proposed that the addition of alkali would reduce the concentration of hardness

ions that may cause surfactant retention. Therefore, ASP will have little

surfactant retention due to ion exchange (25).

31


4.2 Polymer Flooding

Mobility control is one of the most important concepts in any enhanced oil

recovery process. It can be achieved through injection of chemicals to change

displacing fluid viscosity or to preferentially reduce specific fluid relative

permeability through injection of foams. The commonly used mobility control

agent is polymer because it can significantly increase the apparent viscosity of

the injected fluid. Foam is also a good mobility control method with water,

surfactant and gas, but here we will only focus on polymer (2).

Polymer flooding consists of adding polymer to the water of a waterflood to

decrease its mobility. The resulting increase in viscosity, as well as a decrease in

aqueous phase permeability, causes a lower mobility ratio. This lowering

increases the volumetric sweep efficiency and lower swept zone oil saturation.

The polymer flooding will be economic and useful only when the waterflood

mobility ratio is high, the reservoir heterogeneity is serious, or a combination of

these two happens (22).

Polymer flooding can yield a significant increase in oil recovery compared to

conventional water flooding techniques. A typical polymer flood project involves

mixing and injecting polymer over an extended period of time until about 1/3–

1/2 of the reservoir pore volume has been injected. This polymer “slug” is then

followed by continued long term water flooding to drive the polymer slug and

the oil bank in front of it toward the production wells. Polymer is injected

continuously over a period of years to reach the desired pore volume (26).

Polymers are often used with surfactant and alkali agents to improve volumetric

sweep efficiency, reduce channeling and breakthrough and they can also provide

mobility control at the low IFT front. Otherwise, the front is not stable and will

finger and dissipate. Figure 4.7 shows the fingering effect with water flooding

while use of polymers (Figure 4.8) has reduced the effect of fingering

significantly.

4.2.1 Types of Polymers

Two main types of polymers, synthetic polymers such as hydrolyzed

polyacrylamide (HPAM) and biopolymers such as xanthan gum are commonly

used in enhanced oil recovery. Less commonly used are natural polymers and

their derivatives, such as guar gum, sodium carboxymethyl cellulose, and

hydroxyl ethyl cellulose (HEC).

32


Figure 4.7: Fingering effect with water flooding (27)

Figure 4.8: Decreased effects of fingering with polymer flooding (27)

4.2.1.1 Hydrolyzed Polyacrylamide (HAPM)

HPAM is the most widely used polymer in EOR applications. In China’s Daqing

field, HPAM solutions have provided significantly greater oil recovery for either a

given polymer concentration or viscosity level. The reason is that HPAM solutions

exhibit significantly greater viscoelasticity than xanthan solutions. Polyacrylamide

adsorbs strongly on mineral surfaces, therefore it is partially hydrolyzed to

reduce adsorption by reacting polyacrylamide with a base (sodium or potassium

hydroxide or sodium carbonate) (2). Hydrolysis converts some of the amide

groups (CONH2) to carboxyl groups (COO−), as shown in Figure 4.9.

Figure 4.9: Partially hydrolyzed polyacrylamide (22)

33


Typical degrees of hydrolysis are 15 to 35% of the acrylamide monomers; hence

the HPAM molecule is negatively charged that have a large effect on the

rheological properties of the polymer solution. According Green and Willhite

(1998), polyacrylamide is mainly anionic, but could be nonionic or cationic. The

molecular weights of HPAM used in EOR processes are up to higher than 20

million Daltons (2; 22).

Advantages/disadvantages: They are relatively cheap, develop high viscosities in

fresh water, and adsorb on the rock surface to give a long-term permeability

reduction. The main disadvantages are their tendency to shear degradation at

high flow rates, sensitive to high temperature and their poor performance in high

salinity brine.

4.2.1.2 Xanthan Gum

Another widely used polymer, a biopolymer, is xanthan gum (corn sugar gum).

These polymers are formed from the polymerization of saccharide molecules, a

bacterial fermentation process. The structure of a xanthan biopolymer is shown

in Figure 4.10. Xanthan gum has a more rigid structure and is quite resistant to

mechanical degradation. These properties make it relatively insensitive to salinity

and hardness. It is susceptible to bacterial attack after it has been injected into

the reservoir. The polymer is relatively nonionic and, therefore, free of ionic

shielding effects of HPAM. Molecular weights of xanthan biopolymer used in EOR

processes are in range of 1 million to 15 million.

Xanthan is supplied as a dry powder or as a concentrated broth. It is often

chosen for a field application when no fresh water is available for flooding. Some

permanent shear loss of viscosity could occur for polyacrylamide, but not for

polysaccharide at the wellbore. However, the residual permeability reduction

factor of polysaccharide polymers is low. Other potential EOR biopolymers are

scleroglucan, simusan, alginate, etc (2; 22).

Figure 4.10: Molecular structure of Xanthan

34


4.2.2 Polymer Flow Behavior in Porous Media

4.2.2.1 Polymer Retention

Retention of polymer in a reservoir includes adsorption, mechanical trapping,

and hydrodynamic retention. Adsorption refers to the interaction between

polymer molecules and the solid surface. This interaction causes polymer

molecules to be bound to the surface of the solid, mainly by physical adsorption,

and hydrogen bonding. Mechanical entrapment and hydrodynamic retention are

related and occur only in flow-through porous media. Retention by mechanical

entrapment occurs when larger polymer molecules become lodged in narrow

flow channels. The level of polymer retained in a reservoir rock depends on

permeability of the rock, nature of the rock (sandstone, carbonate, minerals, or

clays), polymer type, polymer molecular weight, polymer concentration, brine

salinity, and rock surface (2; 22).

4.2.2.2 Inaccessible Pore Volume

When size of polymer molecules is larger than some pores in a porous medium,

the polymer molecules cannot flow through those pores. The volume of those

pores that cannot be accessed by polymer molecules is called inaccessible pore

volume (IPV) (2). The inaccessible pore volume is a function of polymer molecular

weight, medium permeability, porosity, salinity, and pore size distribution. In

extreme cases, IPV can be 30% of the total pore volume (22).

4.2.2.3 Permeability Reduction and the Resistance Factor

Polymer adsorption/retention causes the reduction in apparent permeability.

Therefore, rock permeability is reduced when a polymer solution is flowing

through it, compared with the permeability when water is flowing. This

permeability reduction is defined by the permeability reduction factor (Rk) (2):

(4.1)

The resistance factor (Rf) is defined as the ratio of mobility of water to the

mobility of a polymer solution flowing under the same conditions.

(4.2)

The residual resistance factor (Rrf) is the ratio of the mobility of water before to

that after the injection of polymer solution (22).

35


(4.3)

Residual resistance factor is a measure of the tendency of the polymer to adsorb

and thus partially block the porous medium. Permeability reduction depends on

the type of polymer, the amount of polymer retained, the pore-size distribution,

and the average size of the polymer relative to pores in the rock (21).

4.2.2.4 Relative Permeabilities in Polymer Flooding

Some of the researchers have proved from their experiments that polymer

flooding does not reduce residual oil saturation in a micro scale. The polymer

function is to increase displacing fluid viscosity and thus to increase sweep

efficiency. Also, fluid viscosities do not affect relative permeability curves.

Therefore, it is believed that the relative permeabilities in polymer flooding and

in water flooding after polymer flooding are the same as those measured in

waterflooding before polymer flooding (2).

4.2.2.5 Polymer Rheology in Porous Media

The rheological behavior of fluids can be classified as Newtonian and Non-

Newtonian. Water is a Newtonian fluid in that the flow rate varies linearly with

the pressure gradient, thus viscosity is independent of flow rate. Polymers are

Non-Newtonian fluids. Rheological behavior can be expressed in the terms of

‘apparent viscosity’ which can be defined as

(4.4)

where

= shear stress

= shear rate

The apparent viscosity of polymer solutions used in EOR processes decreases as

shear rate increases. Fluids with this rheological characteristic are said to be

shear thinning. Materials that exhibit shear thinning effect are called

pseudoplastic. Polysaccharides such as Xanthan are not shear sensitive and even

high shear rate is employed to Xanthan solutions to obtain proper mixing, while

polyacrylamides are more shear sensitive. Most significant change in polymer

mobility occurs near the wells where fluid viscosities are large (21).

36


4.3 Alkaline Flooding

The alkaline flooding method relies on a chemical reaction between high-pH

chemicals such as sodium carbonate and sodium hydroxide (most common alkali

agents) and organic acids (saponifiable components) in crude oil to produce in

situ surfactants (soaps) that can lower interfacial tension. In most of the

literature, these saponifiable components are described as petroleum acids, even

though their structure is not known. The addition of the alkali increases pH and

lowers the surfactant adsorption so that very low surfactant concentrations can

be used to reduce cost. This process is generally applied with crude oils of

relatively low API gravity and containing high acidic components (2).

Mobility control can improve displacement efficiency in alkaline floods. For this,

mostly polymer is used as a mobility buffer to displace the primary slug. In

addition, the reservoir is also conditioned with preflush before the injection of

primary slug (21).

Alkaline flooding is also called caustic flooding. Most commonly used alkalis for in

situ generation of surfactants are sodium hydroxide, sodium carbonate, sodium

orthosilicate, sodium tripolyphosphate, sodium metaborate, ammonium

hydroxide, and ammonium carbonate. Nowadays, ASP formulations use

moderate pH chemicals such as sodium bicarbonate (NaHCO3) or sodium

carbonate (Na2CO3) instead of sodium hydroxide (NaOH) to reduce emulsion and

scale problems. Chinese Daqing oil field ASP projects have had difficulty in

breaking emulsion when using a strong alkali such as NaOH.

Addition of the alkali chemicals results in a high pH because of the dissociation in

the aqueous phase. High pH indicates large concentration of hydroxide ions

(OH-). For example, NaOH dissociates to yield (OH-) as below:

(4.5)

Sodium carbonates dissociates as

(4.6)

followed by the hydrolysis reaction

(4.7)

In carbonate reservoirs where anhydrite (CaSO4) or gypsum (CaSO4·2H2O) exists,

the CaCO3 or Ca(OH)2 precipitation occurs when Na2CO3 or NaOH is added.

37


Carbonate reservoirs also contain brine with a higher concentration of divalents

and could cause precipitation. To overcome this problem, Liu (2007) suggested

NaHCO3 and Na2SO4. NaHCO3 has a much lower carbonate ion concentration,

and additional sulfate ions can decrease calcium ion concentration in the

solution (2).

4.3.1 Alkaline Reaction with Crude Oil

4.3.1.1 In Situ Soap Generation

In alkaline flooding, the injected alkali reacts with the saponifiable components

in the reservoir crude oil. These saponifiable components are described as

petroleum acids (naphthenic acids). Naphthenic acid consists of carboxylic acids,

carboxyphenols, porphyrins, and asphaltene with molecular weight of 120 to well

over 700. If the crude oil contains an acidic hydrocarbon component then

hydroxide ion must react with a pseudo-acid component (HA) to form a

surfactant. If no HA is originally present in the crude oil, little surfactant can be

generated. A useful procedure for identifying crudes for their attractiveness to

alkaline flooding is through acid number which (also called total acid number,

TAN) is a measure of the potential of a crude oil to form surfactants. The acid

number is the mass of potassium hydroxide (KOH) in milligrams required to

neutralize one gram of crude oil. The alkali–oil chemistry is described by

partitioning of this pseudo-acid component between the oleic and aqueous

phases (equation 4.9) and subsequent hydrolysis in the presence of alkali to

produce a soluble anionic surfactant A-, as shown in Figure 4.11 (2).

The overall hydrolysis and extraction are given by

(4.8)

The extent of equation 4.8 reaction depends strongly on the aqueous solution

pH. This reaction occurs at the water/oil interface. A fraction of organic acids in

oil become ionized with the addition of an alkali, whereas others remained

electronically neutral. The hydrogen-bonding interaction between the ionized

and neutral acids can lead to the formation of a complex called acid soap. Thus,

the overall reaction, equation 4.8, is decomposed into a distribution of the

molecular acid between the oleic and aqueous phases,

(4.9)

and an aqueous hydrolysis,

38


(4.10)

where, HA denotes a single acid species, A- denotes anionic surfactant, and

subscripts o and w denote oleic and aqueous phases, respectively (2).

Figure 4.11 : Schematic of alkaline recovery process (2)

4.3.1.2 Emulsification

Alkaline chemicals can cause improved oil recovery through the formation of

emulsions. In alkaline flooding, emulsification is instant, and emulsions are very

stable. Emulsification mainly depends on the water/oil IFT. The lower the IFT, the

easier the emulsification occurs. The stability of an emulsion mainly depends on

the film of the water/oil interface. The acidic components in the crude oil could

reduce IFT to make emulsification occur easily, whereas the asphaltene

surfactants adsorb on the interface to make the film stronger so that the stability

of emulsion is enhanced. Local formation of highly viscous emulsions is not

desirable since these would promote viscous instability (2; 22).

39


4.4 Alkaline Surfactant Polymer (ASP) Flooding

Alkaline-Surfactant-Polymer (ASP) flooding has been recognized to be one of the

major EOR techniques that can be successfully used in producing light and

medium oils left in the reservoirs after primary and secondary recovery (28). The

success of this method depends on the identification of the proper alkali,

surfactant, and polymer and on the way they are combined to produce

compatible formulation that yields good crude oil emulsification / mobilization,

low chemical losses and good mobility control (29). The synergistic effect of

alkaline, surfactant and polymer results in less surfactant required to recover

significantly incremental oil. An ASP flood involves injecting a predetermined

pore volume of ASP slug into the reservoir. Typically, the ASP formulation

consists of about 0.5-1% alkali, 1% surfactant, and 0.1% polymer. The alkali

reacts with acidic components in the crude oil creating natural soap and also

helps with reducing the adsorption of the surfactant on the rock. It also alters

rock wettability (from oil-wet to water-wet) and adjusts pH and salinity.

Surfactant component helps in releasing the oil from the rock by reducing the

interfacial tension between oil and water while polymer acts as viscosity modifier

and helps mobilize the oil. Often, the ASP slug is followed by polymer “push”

solution for conformance control, mobility control (reduce fingering). This also

helps reduce the slope of oil recovery decline and helps extend the production

for a longer period of time. Upon the completion of polymer injection, driving

fluid (water) is injected to move the chemicals and resulting oil bank towards

production wells. Generally, the reservoir is conditioned by preflush (with water,

alkali or polymer depending on rock mineralogy) before the injection of ASP slug

into reservoir (30). Figure 4.12 shows the typical ASP flooding process in a 5-spot

well pattern.

Displacement mechanisms in ASP may be summarized as follows (2):

Increased capillary number effect to reduce residual oil saturation

because of low to ultralow IFT.

Surfactant adsorption is reduced on both sandstones and carbonates at

high pH.

High pH also improves microemulsion phase behavior.

Improved macroscopic sweep efficiency because of the viscous polymer

drive.

40


Improved microscopic sweep efficiency and displacement efficiency as a

result of polymer viscoelastic property.

Emulsification, entrainment, and entrapment of oil droplets because of

surfactant and alkaline effects.

Figure 4.12 : ASP flooding process in 5-spot well pattern (31)

41

Simulation of Alkaline, Surfactant and Polymer Flooding

5 Simulation of Alkaline, Surfactant and Polymer Flooding

Different chemical EOR methods such as surfactant flooding, alkaline-surfactant

(AS) flooding, polymer flooding, surfactant-polymer (SP) flooding, and alkaline-

surfactant-polymer (ASP) flooding in Norne E-segment were simulated using

Eclipse-100. The economic evaluations were performed based on the

incremental oil recovery over water flooding using the Net Present Value (NPV)

model.

5.1 The Surfactant Flood Model

Eclipse 100 does not provide the detailed chemistry of a surfactant process, but

models the important features of a surfactant flood on a full field basis. It does

not provide options as to what type of surfactant to use for given reservoir

structures or certain fluid characteristics. It just presents the surfactant option as

a blanket over all types of reservoir and fluid characteristics. The injected

surfactant is modeled by solving a conservation equation for surfactant within

the water phase. The surfactant concentrations are updated fully-implicitly at the

end of each time-step after the oil, water and gas flows have been computed.

The surfactant is assumed to exist only in the water phase, and the input to the

reservoir is specified as a concentration at water injector (32). The detailed

description of the surfactant model is presented in Appendix A.

5.2 The Polymer Flood Model

The Polymer Flood option uses a fully implicit five-component model (oil/ water/

gas/ polymer/ brine) to allow the detailed mechanisms involved in polymer

displacement process to be studied. The flow of the polymer solution through

the porous medium is assumed to have no influence on the flow of the

hydrocarbon phases (32). A full description of the polymer model can be found in

Appendix B.

5.3 The Alkaline Flood Model

The Eclipse 100 does not take into account the in-situ surfactant creation and the

phase behavior. This simplified model is targeted at looking at some of the

effects of the alkaline on an ASP flooding performance and also to analyze its

effect on the water-oil surface tension and adsorption reduction of surfactant

42


and polymer (32). A more detailed description of the alkaline model is given in

Appendix C.

5.4 Alkaline, Surfactant and Polymer Properties

The current drive mechanism for Norne field is water flooding and there has

been no study of chemical EOR for the field. Therefore, the chemical properties

(alkali, surfactant and polymer) used in this study are not actually related to

Norne E-segment and practically may or may not be compatible with Norne

reservoir and fluid characteristics. In general, these data are field specific

parameters which need to be measured in laboratory for the actual system to be

used. Relatively, I assumed that these properties are compatible with Norne

reservoir and fluid properties.

The chemical properties (alkali, surfactant and polymer) for this study were

gathered from different sources. The surfactant properties were provided by Nan

Cheng (Statoil) and Lars Høier (Statoil) from Norne village, while alkaline

properties were provided by Charles A. Kossak (Schlumberger) who conducted

ASP short course at IPT, NTNU. The Polymer (NBF Xanthan) properties were

taken from master thesis of Eldar Sadikhzadeh (2007) after permission from his

supervisor Professor Jon Kleppe and co-supervisor specialist Jan Åge Stensen

(SINTEF). These are realistic data and might have a practical implementation. The

reduced input file of alkali, surfactant and polymer properties can be seen in

Appendices D, E and F respectively as 110 tables of each property need to be

included in the simulation run.

5.5 Performance and Economic Evaluation

A universal technical measure of the success of an EOR process is the amount of

incremental oil recovered (22). For a chemical EOR project, the EOR oil is generally

the incremental over water flooding (2). In order to determine the most suitable

chemical EOR method for the Norne E-segment, the Net Present Value (NPV)

criterion was selected. The NPV calculation is based on incremental oil

production from chemical flooding compared to water flooding.

NPV is a central tool in discounted cash flow (DCF) analysis, and is a standard

method for using the time value of money to appraise long-term projects. The

NPV must be positive for a project to be accepted. It is defined by the formula

(33):

http://en.wikipedia.org/wiki/Discounted_cash_flow

http://en.wikipedia.org/wiki/Time_value_of_money

43


(5.1)

where r is the discount rate, t is the time, Ct cash flow in year t, and n is time

period of the project/investment.

5.6 Synthetic Model for Testing of Alkaline, Surfactant and

Polymer Flooding Models

Synthetic model of dimension 10, 10, 3 in I, J and K directions respectively was

created in eclipse-100 to ascertain the effectiveness of each of the provided

chemical models (surfactant, alkaline-surfactant and ASP) in terms of enhanced

oil recovery. The model contains two wells i.e injector and producer which are

placed in grids 1, 1, 3 and 10, 10, 3 respectively. As shown in Figure 5.1, the

model is flat and homogeneous having Norne fluid and rock properties.

Figure 5.1: Synthetic Model

The Simulation was run for 500 days and following cases were simulated.

1. Water flooding: Base case

2. Surfactant flooding: Water flooding for 150 days and then continuous

surfactant injection for 350 days.

44


3. Alkaline-Surfactant (AS) flooding: Water flooding for 150 days and then

continuous Alkaline-surfactant injection for 350 days.

4. Alkaline-Surfactant-Polymer (ASP) flooding: Water flooding for 150 days and

then continuous ASP injection for 350 days.

From Figure 5.2, Figure 5.3, and Figure 5.4, the green line represents base case

(water flooding) while other lines such as blue, light blue and pink symbolize

surfactant flooding, AS flooding and ASP flooding respectively. All of the chemical

flooding methods are performing efficiently over water flooding in terms of

increased oil recovery and less water production. The performance of ASP

flooding is comparatively better than others because addition of polymer leads

to improved volumetric sweep efficiency and less water fingering.

Figure 5.2: Oil recovery factor for synthetic model

45


Figure 5.3: Oil production rate for synthetic model

Figure 5.4: Water production rate for synthetic model

Thus, it is evident from the above graphs that these chemical models are

performing better in the recovery of capillary trapped oil and can be applied to

Norne E-segment. However, the synthetic model is homogeneous and flat

therefore the recovery factor is high but this might not be the case with actual

Norne field model.

46


5.7 Overview of Base Case

The simulation of Norne E-segment history matched model starts from

November 1997 and ends at December 2015. The time range from 1997 to 2004

is the history (pink) whereas from 2005 to 2015 is the future prediction (blue).

The base case is the simulation model with continuous water flooding for the

future prediction case. The water flood was ended at a recovery factor of 54.30

% OOIP as shown in Figure 5.5.

Figure 5.5: Recovery factor for base case

5.8 Selection of Injector and Producer

In Norne E-segment both F-1H and F-3H are injection wells. F-1H is located in

water zone, whereas F-3H is in the oil zone as shown in Figure 5.6. Three cases

were run in order to see which injector will perform efficiently with less

surfactant consumption. From Figure 5.7 and Figure 5.8, we can see that the

surfactant injection in well F3-H gives comparatively little higher recovery factor

with less surfactant consumption than injection in F-1H or F-1H & F-3H. Since, F-

1H is located in water zone; the injected surfactant will spread out, resulting in

loss of surfactant. Also, surfactant has to travel for long before it can start to

mobilize capillary trapped oil and lot of surfactant is required due to

47


adsorption/retention. This implies that chemicals injection only in F-3H will be a

good option for profitable solution.

Norne E-segment has two active producers E-2H and E-3AH as on November

2004. E-2H is the horizontal well, penetrating layers 8-9 of Ile formation whereas

E-3AH is also horizontal producer located in layers 1-2 (garn formation). As

already discussed in section 3.2, Ile and Tofte are our main target formations, so

only E-2H is selected as a producer during our future predictions.

Figure 5.6: Wells location in E-segment

Figure 5.7: Comparison of recovery factor between different surfactant injection wells

48


Figure 5.8: Comparison of total surfactant injection between different surfactant injection wells

5.9 Assumptions

Norne E-segment is assumed to be producing at its residual oil saturation.

The provided properties of alkali, surfactant and polymer are compatible

with Norne E-segment reservoir and fluid properties.

No desorption of alkali, surfactant and polymer during the simulation

runs.

All chemicals are injected with pure water.

No salinity effect is considered.

The fixed alkaline concentration of 2 kg/m3 will be used in scenario 2 and

scenario 5.

Maximum constrain on reservoir pressure is assumed to be 300 bara to

avoid fracturing of formation.

Maximum allowable bottom hole injection pressure for injector F-1H and

F-3H is 450 bara.

Minimum allowable bottomhole pressure for producer E-2H is 235 bara.

The assumed Discount rate, price of oil and prices of chemicals are given

in Table 5.1.

0.0

10.0

20.0

30.0

40.0To

tal S

urf

acta

nt

Inje

ctio

n (

E-s

egm

ent)

, M

illio

n K

G

Time (Years)

Total surfactant injection v/s Time

Surfactant Injection in well F1-H

Surfactant injection in well F-3H

Surfactant injection in wells F-1H & F-3H

49


For simplification, only chemicals cost is assumed to be major expense

and no operational and additional chemicals facilities costs are

considered.

For sensitivity analysis (Spider chart) of scenario 5, different oil prices,

chemical prices, and discount rate for low case, base case, and high case

are given in Table 5.2.

Table 5.1: Discount rate, oil Price, and chemicals prices

Discount Factor

0.08

Oil Price USD/bbl 90

Alkaline Price USD/kg 1.50

Surfactant Price USD/kg 3.50

Polymer Price USD/kg 4.00

Table 5.2: Oil prices, chemical prices, and discount rate for sensitivity analysis (Spider chart) of scenario 5

Cases Oil Price

(USD/bbl) Alkaline (USD/kg)

Surfactant (USD/kg)

Polymer (USD/kg)

Discount Rate

Low Case 40 2.25 5.00 5.50 0.10

Base Case 90 1.50 3.50 4.00 0.08

High Case 140 0.75 2.00 2.50 0.06

50

Simulation Results and Analysis

6 Simulation Results and Analysis

The objective of simulation is to optimize the chemical flooding (alkali, surfactant

and polymer) efficiency to maximize the volume of incremental oil production

per unit mass of chemicals injected. Five different scenarios such as, surfactant

flooding, alkaline-surfactant (AS) flooding, polymer flooding, surfactant-polymer

(SP) flooding, alkaline-surfactant-polymer (ASP) flooding and in the end

comparison between all five scenarios will be discussed in order to see which is

the most suitable and profitable method in terms of Net Present Value (NPV) for

the Norne E-segment. In addition, the Single Parameter Sensitivity Analysis

(Spider Chart) at different oil prices, chemical prices, and discount rate for low

case, base case and high case will be addressed in this chapter.

Scenario 1: Surfactant Flooding a. Continuous Surfactant Injection

b. Surfactant Slug Injection

c. Continuous Surfactant Injection vs. Surfactant Slug Injection

d. Appropriate Surfactant Concentration

e. Cumulative incremental oil production for surfactant flooding

Scenario 2: Alkaline-Surfactant (AS) Flooding a. AS flooding at concentrations of 2 kg/m3 and 0.3 kg/m3 respectively

b. AS flooding at concentrations of 2 kg/m3 and 2 kg/m3 respectively

c. AS flooding at concentrations of 2 kg/m3 and 5 kg/m3 respectively

Scenario 3: Polymer Flooding a. Polymer flooding at concentration of 0.5 kg/m3

b. Polymer flooding at concentration of 0.7 kg/m3

c. Polymer flooding at concentration of 1.0 kg/m3

Scenario 4: Surfactant-Polymer (SP) Flooding a. Surfactant slug (5 kg/m3) followed by polymer (0.4 kg/m3)

b. SP slug (5 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3)

Scenario 5: Alkaline-Surfactant-Polymer (ASP) Flooding a. ASP slug (2 kg/m3, 0.3 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3)

b. Desorption / no desorption of surfactant and polymer

Comparison between Incremental NPV for all Scenarios Plot of Incremental NPVs of all Scenarios

51


Single Parameter Sensitivity Analysis (Spider Chart) low case, base case and high case

6.1 Scenario 1: Surfactant Flooding

For enhanced oil recovery, modeling of chemicals into an oil reservoir should be

a systematic process. Since chemicals are very expensive and complicated,

therefore it is pertinent to ensure that unnecessary waste is prevented during

the injection process. This can be explained as, if chemical injection in a slug form

gives almost same incremental oil recovery as for continuous injection then latter

choice might be uneconomical because this will give rise to increased chemical

injection, also resulting in excess chemical production at the producers.

This scenario involves five cases of surfactant injection like continuous injection

or slug injection, the comparison between continuous and slug injection,

selection of appropriate surfactant concentration and calculation of NPV based

on incremental oil production over water flooding.

6.1.1 Continuous Surfactant Injection

In this case, two sub-cases were considered; continuous surfactant injection for 3

years and continuous surfactant injection for 6 years. The injection of surfactant

starts at January 2005; total oil production and total surfactant injection are

shown in Figure 6.1 and Figure 6.2 respectively. Both cases gave improved oil

production over water flooding (base case).

Figure 6.1 shows that total oil production for 6 years case is 0.017 x 106 Sm3

higher than the total oil production for 3 years case, but it should be

economically viable. Figure 6.2 shows that continuous 6 years injection requires

85.58 x 106 kg surfactant while for continuous 3 years it is about 47. 22 x 106 kg.

The anticipated incremental oil recovery for 6 years injection compared to 3

years injection is not encouraging and it is rather wasteful to inject surfactant for

6 years. Thus, surfactant injection for 3 years seems better than 6 years injection.

However, 3 years continuous injection still requires considerable volume of

surfactant which is relatively expensive. Therefore, the next step involves the

surfactant slug injection, and after comparing with continuous surfactant

injection, the better one in terms of economics will be selected.

52


6.1.2 Surfactant Slug Injection

The surfactant slug injection during 3 years involves injecting surfactant for one

month after every 3 months or 6 months interval followed by water. Two sub

cases were considered; injection at 3 months interval and injection at 6 months

interval.

Figure 6.3 shows that total oil production for surfactant slug injection at 3

months interval and 6 months interval is almost same. The total water

production for 3 months interval is matching with base case while for 6 months

interval it is less. This is because some of injected water occupying pore spaces

after oil displacement, resulting in less water production. It is apparent from

Figure 6.4 that surfactant requirement for 3 months interval is higher than 6

months interval.

Thus, it is obvious from above discussion that surfactant injection at 6 months

interval is more profitable than 3 months interval because less surfactant is

consumed and produced as well.

6.1.3 Continuous Surfactant Injection vs. Surfactant Slug Injection

Figure 6.5 and Figure 6.6 show comparison between continuous and slug

injection over a period of 3 years. It can be seen from Figure 6.5 that total oil

production for continuous and slug injection is more or less the same, whereas a

big difference is observed in surfactant consumption (Figure 6.6). The case of

continuous injection requires excess surfactant of 31.82 x 106 kg than 6 months

interval case for a same period of time. Thus surfactant slug injection at 6

months interval would be a right choice.

6.1.4 Appropriate Surfactant Concentration

In the previous cases, surfactant concentration of 10 kg/m3 was used. However,

it is not certain that this concentration would reduce residual oil saturation to

the possible minimum.

Four different surfactant concentrations such as 5 kg/m3, 10 kg/m3, 15 kg/m3, 30

kg/m3 were modeled to see which would return maximum cumulative oil

production.

Figure 6.7 and Figure 6.8 indicates that surfactant concentration of 5 kg/m3

would be better choice in terms of oil production and surfactant consumption.

53


Thus from above four cases, the option of surfactant slug injection for 3 years

period at a 6 months interval with 5 kg/m3 surfactant concentration is the most

appropriate choice and will be used for onward scenarios.

Figure 6.1: Total Oil production for continuous surfactant flooding for 3 years and 6 years

Figure 6.2: Total surfactant injection for continuous surfactant flooding for 3 years and 6 years

54


Figure 6.3: Total oil production for surfactant slug flooding for 3 months and 6 months intervals

Figure 6.4: Total surfactant injection for 3 months and 6 months intervals

Figure 6.5: Total oil production: Comparison between slug and continuous injection

55


Figure 6.6: Total surfactant injection: Comparison between slug and continuous injection

Figure 6.7: Total oil production for different surfactant concentrations

Figure 6.8: Total surfactant injection for different surfactant concentrations

56


6.1.5 Cumulative incremental oil production for surfactant flooding

From modeling in the previous cases it was discovered that surfactant flooding

pattern should be for 6 months interval over 3 years at a concentration of 5

kg/m3. The sequence of surfactant flood injection consists of: a preflush of fresh

water for 1 year starting from Jan 2005; surfactant slug injection at 6 months

interval for 3 years starting from Jan 2006; and finally, chase water starting from

Jan 2009 till the end of simulation.

Figure 6.9 depicts cumulative incremental oil production over water flooding.

Soon after the surfactant injection, fast improvements are observed and the

peak of cumulative incremental oil production is obtained two years after the

injection (Jan 2008) which then drops gradually. It took time to create an oil

bank, the curve increased again due to breakthrough of oil bank at the producer

and then declined slowly with time. The first rapid increase in oil production

occurred from Tofte formation, having two high permeability layers, while the

later increase was from other layers after the oil bank is made up.

Table G.2 in Appendix G shows year wise figures of incremental oil production,

surfactant consumption and incremental NPV. The incremental NPV for this

scenario is positive and the figure is +91.81 million USD. The expenses for

additional installations are not included in the economics calculation.

Thus, an EOR with surfactant injection seems profitable for Norne E-segment, but

we can obtain even better sweep efficiency and maximize profits with the

introduction of alkali and polymers. The next scenario discusses the effect of

alkali with the surfactant slug.

Figure 6.9: Scenario 1: Cumulative incremental oil production for surfactant flooding

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Scenario 1: Cummulative Incremental Oil Production (Sm3) vs Time

Surfactant Slug (5 kg/m3)

57


6.2 Scenario 2: Alkaline-Surfactant (AS) Flooding

The synergistic effect of alkaline and surfactant results in less surfactant required

to recover significant incremental oil. In the Alkaline Surfactant (AS) process, a

very low concentration of the surfactant is used to achieve ultra low interfacial

tension between the trapped oil and the injection fluid/formation water. The

ultra low interfacial tension also allows the alkali present in the injection fluid to

infiltrate deeply into the formation and contact the trapped oil globules. The

alkali then reacts with the acidic components in the crude oil to form additional

surfactant in-situ, thus, continuously providing ultra low interfacial tension and

freeing the trapped oil. The addition of alkali increases pH, alters rock wettability

(from oil-wet to water-wet) and lowers the surfactant adsorption so that very

low surfactants can be used to reduce cost (34).

In the previous scenario, the surfactant of 5kg/m3 concentration was used and

even with this concentration the incremental NPV was positive. The surfactant is

relatively expensive and to reduce its consumption, alkali was injected with low

concentration surfactant slug to produce in situ surfactant. The sequence of

Alkaline-surfactant flood injection consists of: a preflush of fresh water for 1 year

starting from Jan 2005; Alkaline-surfactant slug injection at 6 months interval for

3 years starting from Jan 2006; and finally, chase water starting from Jan 2009 till

the end of simulation.

Following three cases were simulated at a constant concentration of alkali (2

kg/m3) and with different concentrations of surfactant.

6.2.1 AS flooding at concentrations of 2 kg/m3 and 0.3 kg/m3 respectively

6.2.2 AS flooding at concentrations of 2 kg/m3 and 2 kg/m3 respectively

6.2.3 AS flooding at concentrations of 2 kg/m3 and 5 kg/m3 respectively

In Figure 6.10, the cumulative incremental oil production is almost same for all

surfactant concentrations (0.3 kg/m3, 2 kg/m3 & 5 kg/m3). The alkali is performing

well in reducing the residual oil saturation by generating additional in-situ

surfactant, thus reducing surfactant consumption.

Since, the incremental oil production at the 5 kg/m3 surfactant concentration is

same as with 0.3 kg/m3 concentration, so it is rather wasteful to inject 5 kg/m3

58


surfactant (Figure 6.11) and also this concentration yields high surfactant at the

producer (Figure 6.12). Figure 6.13 shows reservoir pressure of alkaline-

surfactant flooding which is maintained below the constrain (300 bara) and

above the bubble point (251 bara).

Table G.3, Table G.4, and Table G.5 shows year wise figures of incremental oil

production and incremental NPVs for different surfactant concentrations. The

incremental NPV of 0.3 kg/m3 surfactant concentration is +98.74 million USD

which is higher than the NPV of other concentrations (2 kg/m3 & 5 kg/m3) and

also scenario 1.

Thus in terms of incremental NPV, the surfactant of concentration 0.3 kg/m3 is

the right choice for this scenario and also alkali-surfactant flooding (scenario 2) is

yielding more net cash flows than surfactant flooding (scenario 1).

Figure 6.10: Scenario 2: Cumulative incremental oil production for alkaline-surfactant flooding

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ALK-SUR Slug (2kg/m3 & 2kg/m3)

ALK-SUR Slug (2kg/m3 & 5kg/m3)

59


Figure 6.11: Scenario 2: Total alkaline-surfactant injection for different surfactant concentrations

Figure 6.12: Scenario 2: Surfactant production rate for different concentrations

Figure 6.13: Scenario 2: Reservoir pressure for alkaline-surfactant flooding

60


6.3 Scenario 3: Polymer Flooding

Polymer flooding is one of the chemical EOR techniques and can yield a

significant increase in oil recovery compared to conventional water flooding

technique. Unlike surfactant, polymer does not reduce the interfacial tension

between water and oil but it improves volumetric sweep efficiency by increasing

water viscosity. This increase in viscosity lowers mobility ratio, decreases the

chances of fingering, and allows more oil to be contacted on a macroscopic scale.

Typical polymer injection sequence is described as: a preflush usually consisting

of a low- salinity brine or fresh water to condition the reservoir; the polymer

solution itself for mobility control to minimize channeling; and finally, chase or

drive water to move the polymer solution and resulting oil bank to the

production wells (22). The flow of polymer solution through the porous medium is

assumed to have no influence on the flow of the hydrocarbon phases.

The objective of this scenario is to know the incremental NPV based on

incremental oil production if this EOR technique is to be applied to Norne E-

segment. The sequence of polymer flood injection for this scenario consists of: a

preflush of fresh water for 1 year starting from Jan 2005; continuous polymer

injection for 3 years starting from Jan 2006; and finally, chase water starting from


The following three cases were simulated to identify which concentration of

polymer would generate high incremental NPV.

6.3.1 Polymer flooding at concentration of 0.5 kg/m3



Figure 6.14 proves the classical theory about the effect of polymer flooding on

water production rate. The trends from this figure are that the higher the

polymer concentration, the lower the water production rate, and faster the

water production increase. It can be seen from Figure 6.15 of Buckley Leverett

that the water saturation is high behind the trailing edge for a polymer flooding

than a water flooding. When this more even polymer flood front breaks through,

more water will be produced at the same time as in our case (Figure 6.14). It has

been clearly seen that the polymer concentration played an important role in

61


sweep efficiency; polymer of concentration 1.0 kg/m3 has less water production

and much better volumetric sweep efficiency than the water flooding case. The

water front moves faster through the reservoir in the water flooding case.

The cumulative incremental oil production for three different concentrations is

shown in Figure 6.16. The polymer of concentration 1.0 kg/m3 would have high

incremental oil production because of high viscosity. Although, high

concentration improves mobility ratio and displacement efficiency but also have

more chances of polymer adsorption, retention, and entrapment. The peak oil

production for all cases is achieved in 2010 and after 2013 the oil production

became lower than water flooding. This is because due to good volumetric

sweep efficiency, the more oil was produced before the breakthrough of front

and after breakthrough low oil production is due to less oil saturation left behind.

Figure 6.17 shows the reservoir pressure of polymer flooding which is maintained

above the bubble point (251 bara) and below the maximum constrain (300 bara).

Table G.6, Table G.7, and Table G.8 shows year wise figures of incremental oil

production and cumulative incremental NPVs for different polymer

concentrations. The incremental NPV of 1.0 kg/m3 polymer concentration is

+56.83 million USD which is higher than the NPV of other concentrations (0.5

kg/m3 & 0.7 kg/m3). However, the incremental NPV of this scenario is lower than

scenario 1 and scenario 2.

Thus, in terms of incremental NPV, the polymer of concentration 1.0 kg/m3 is

most favorable choice for this scenario.

Figure 6.14: Scenario 3: Water Production rate for polymer flooding

62


Figure 6.15: Buckley Leverett solution for polymer flooding compared to water flooding (35)

Figure 6.16: Scenario 3: Cumulative incremental oil production for polymer flooding

Figure 6.17: Scenario 3: Reservoir pressure for polymer flooding

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Polymer Concentration (0.5 kg/m3)Polymer Concentration (0.7 kg/m3)Polymer Concentration (1.0 kg/m3)

63


6.4 Scenario 4: Surfactant-Polymer (SP) Flooding

In the Surfactant Polymer (SP) Flood process, a very low concentration of low

adsorption surfactant is used to achieve ultra low interfacial tension between the

trapped oil and the injection fluid/formation water while polymer acts as

viscosity modifier and helps mobilize the oil. Often, the SP slug is followed by

polymer “push” solution to improve volumetric sweep efficiency, reduce

channeling and breakthrough and they can also provide mobility control at the

low IFT front. Otherwise, the front is not stable and will finger and dissipate.

Upon the completion of polymer injection, driving fluid (water) is injected to

move the chemicals and resulting oil bank towards production wells. Generally,

the reservoir is conditioned by preflush (with fresh water, alkali or polymer

depending on rock mineralogy) before the injection of SP slug into reservoir (30).

For this scenario, the sequence of SP flood injection consists of: a preflush of

fresh water for 1 year starting from Jan 2005; surfactant or SP slug injection for 3

years (6 months interval) starting from Jan 2006; and finally, chase water starting

from Jan 2009 till the end of simulation.

The following two cases were simulated to discover which method generates

higher incremental NPV.

6.4.1 Surfactant slug (5 kg/m3) followed by polymer (0.4 kg/m3)

6.4.2 SP slug (5 kg/m3 & 0.4 kg/m3) followed by polymer (0.4 kg/m3)

Figure 6.18 shows the cumulative incremental oil production for above two

cases. Soon after the surfactant/SP slug injection, the fast improvements were

observed and the first peak of cumulative oil production was obtained two years

after the injection (Jan 2008) which then drops gradually. Second highest peak

was observed in Jan 2012 which was due to breakthrough of oil bank at the

producer. The first peak of oil production occurred from the two high

permeability layers (Tofte formation), while second peak of oil production

resulted from other layers after the oil bank was made up.

The case 6.4.2 has higher incremental oil production than case 6.4.1 (Figure 6.18,

Table G.9 and Table G.10 ) and also comparatively low water production (Figure

6.19). This is because of the presence of polymer in the surfactant slug. As

discussed in scenario 3, the polymer increases the viscosity of the formulation,

64


resulting in favorable mobility ratio, less chance of fingering, and allows more oil

to be contacted on a macroscopic scale.

As can be seen in Table G.9 and Table G.10, the incremental NPV for case 6.4.2 is

higher than case 6.4.1. Also, this NPV (+117.49 Million USD) is higher than

scenario 1, scenario 2 and scenario 3.

Thus, in terms of incremental NPV, the case 6.4.2 is the optimal choice for this

scenario.

Figure 6.18: Scenario 4: Cumulative incremental oil production for surfactant-polymer flooding

Figure 6.19: Scenario 4: Water production total for surfactant-polymer flooding

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65


6.5 Scenario 5: Alkaline-Surfactant-Polymer (ASP) Flooding

The sequence of ASP flood injection for this scenario consists of: preflush of fresh

water for 1 year starting from Jan 2005; injection of ASP slug for 3 years (6

months interval) starting from Jan 2006; and finally, chase water starting from


The following two cases were simulated.

6.5.1 ASP slug (2 kg/m3, 0.3 kg/m3 & 0.4 kg/m3) followed by polymer (0.4

kg/m3)

6.5.2 Desorption / no desorption of surfactant and polymer

Figure 6.20 shows the cumulative incremental oil production for ASP flooding.

The incremental oil production trend of this scenario is almost matching (little

difference) with Figure 6.18 of scenario 4 (case 6.4.2). Compared to previous

scenario, the additional chemical in this scenario is Alkaline which is relatively

cheap and its addition can reduce the expenses of surfactant. The surfactant

concentration is reduced from 5 kg/m3 to 0.3 kg/m3 while polymer concentration

is same. The alkali is performing well in reducing the residual oil saturation by

generating additional in-situ surfactant. As shown in Figure 6.21, the reservoir

pressure profile is maintained above the bubble point pressure (251 bara) and

below the maximum constrain (300 bara).

The cumulative incremental oil production given in Table G.11 is 4530 Sm3 less

than scenario 4 (case 6.4.2) while the incremental NPV of ASP flooding (+123.50

million USD) is higher than SP flooding (case 6.4.2) and also all scenarios.

Figure 6.22 shows that maximum of 1.40 % oil recovery factor is achieved from

ASP flooding (55.70%) compared to water flooding (54.30 %).

In all scenarios, desorption of chemicals was prevented as per my assumption.

Figure 6.23 and Figure 6.24 shows the effect of desorption/no desorption of

surfactant and polymer respectively. The adsorption of chemicals on rock

surfaces is an instantaneous effect. Desorption refers to decrease of chemicals

adsorbed on rock surfaces with time. We can see in both figures that when

desorption model is active, the adsorbed chemicals in the block decreased as the

time passes, which is due to presence of alkali and also continued exposure to

66


water flood. If desorption is prevented then the adsorbed chemicals may not

decrease with time. Practically, desorption of chemicals may not be as efficient

as in this case.

Thus, in terms of high incremental NPV, less surfactant/polymer adsorption

effect, and less water production, ASP flooding can be the potential EOR method

for Norne E-segment.

Figure 6.20: Scenario 5: Cumulative incremental oil production for ASP flooding

Figure 6.21: Scenario 5: Reservoir pressure for ASP flooding

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67


Figure 6.22: Scenario 5: Oil recovery factor for ASP flooding

Figure 6.23: Scenario 5: Effect of desorption or no desorption of surfactant in block (7, 57, 9)

Figure 6.24: Scenario 5: Effect of desorption or no desorption of polymer in block (7, 57, 9)

68


6.6 Comparison between Incremental NPV for all Scenarios

As described in section 5.5, the Net Present Value (NPV) criterion was selected in

order to determine the most appropriate chemical EOR method for the Norne E-

segment. The NPV calculation is based on incremental oil production from

chemical flooding (alkaline, surfactant, and/or polymer) compared to water

flooding.

The tables in Appendix G for all scenarios present the NPV calculation based on

incremental oil production. Figure 6.25 shows the incremental NPV for all

scenarios. The incremental NPV of ASP flooding (Scenario 5) is higher than all of

the scenarios. Both Scenario 2 (AS flood) and Scenario 5 (ASP flood) has same

alkaline (2 kg/m3) and surfactant (0.3 kg/m3) concentrations; the additional

product in Scenario 5 is polymer. The polymer characteristics to improve

volumetric sweep efficiency by decreasing the mobility ratio resulted in increase

in cumulative incremental oil production; ultimately net present value became

high.

The surfactant concentration (5 kg/m3) in both Scenario 4 (SP flood) and Scenario

1 (Surfactant flood) is equal; the improvement in incremental NPV for Scenario 4

is due to polymer in the slug and also mobility control behind of the slug.

The incremental oil as well NPV for Scenario 3 (polymer flood) are lower than all

of the scenarios. This is because, unlike surfactant, polymer does not release the

capillary trapped oil but it improves volumetric sweep efficiency and reduces

mobility ratio by increasing water viscosity. Ultimately, our goal to reduce

residual oil saturation may not be achieved. Polymer enhances the efficiency of

the AS flood when pumped with and behind the slug as observed in scenario 5.

Keeping in view the above and previous discussion in scenarios, it is concluded

that ASP flooding (scenario 5) can be applied to Norne E-segment to release

capillary trapped oil from Ile and Tofte formations. The maximum of 1.40 %

incremental recovery factor and incremental NPV of +123.53 million USD were

achieved with ASP flooding.

69


Figure 6.25: Incremental NPV for all scenarios

-20

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Incr

em

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Comparision between Incremental NPV for all Scenarios

Scenario 1 (Surfactant Flooding)

Scenario 2 (Alkali-Surfactant Flooding)

Scenario 3 (Polymer Flooding)

Scenario 4 (SP Flooding)

Scenario 5 (ASP Flooding)

70


6.7 Single Parameter Sensitivity Analysis (Spider Plot) for ASP

Flooding

Sensitivity analysis measures the impact on project outcomes of changing one or

more key input values about which there is uncertainty. Spider plot is one of the

standard tools used in risk and uncertainty analysis. Spider diagram is a graph

that compares the potential impact, taking one input at a time, of several

uncertain input variables on project outcomes.

The uncertain parameters for this project are oil price, discount rate, and

chemical prices (alkaline, surfactant and polymer). The sensitivity analysis is done

on the basis of base case sheet by varying a single parameter while keeping all

the base case parameters constant (Table G.12 in appendix G). The NPVs (based

on discount rate and prices of oil, alkaline, surfactant, and polymer) for high case,

base case and low case extracted from Table G.12 are presented in Table 6.1,

where sensitivity is done in terms of % change.

From Figure 6.26, it can be seen that in case of increase in oil price (+55.56%), %

change NPV is +58.26%; whereas for low case, the % change NPV is -58.31% with

% change oil price (-55.56%). This means that change in oil price has huge effect

on NPV.

Equally rise of prices of alkali, polymer and surfactant (50%), % change NPV with

% change polymer price is +1.15% comparison with % change surfactant price

and alkali price is +0.20% and 0.68% respectively. It means that change in

polymer price has slight high effect on NPV than change in surfactant and alkali

price.

Percentage change in discount rate (+25% and -25%) has also great effect on NPV

(+9.29% & -8.12%) respectively but less than oil price.

From above analysis it is obvious that change in oil price has substantial effect on

NPV compared to other parameters while surfactant price is the least sensitive

parameter i.e very low affect on NPV for high/low case.

71


Table 6.1: Single parameter sensitivity analysis

Low Base High

Oil Price (U$/bbl) 40 90 140 % Change -55.56% 0% 55.56% NPV (Million NOK) 51.50 123.53 195.50

% Change -58.31% 0% 58.26%

Alkaline Price (kg/m3) 0.75 1.50 2.25

% Change -50.00% 0% 50.00% NPV (Million NOK) 122.69 123.53 124.37 % Change -0.68% 0% 0.68%

Polymer Price (kg/m3) 2.00 4.00 6.00 % Change -50.00% 0% 50.00%

NPV (Million NOK) 122.11 123.53 124.95

% Change -1.15% 0% 1.15%

Surfactant Price (kg/m3) 1.75 3.50 5.25

% Change -50.0% 0% 50.0% NPV (Million NOK) 123.28 123.53 123.78 % Change -0.20% 0% 0.20%

Discount Rate 0.10 0.08 0.06 % Change 25.0% 0% -25.0% NPV (Million NOK) 113.50 123.53 135.00

% Change -8.12% 0% 9.29%

Figure 6.26: Single parameter sensitivity analysis (Spider Plot) for Scenario 5

-50%-40%-30%-20%-10%

0%

10%20%

30%40%

50%

-50% -30% -10% 10% 30% 50% 70%

% C

han

ge N

PV

% Change Variable

Scenario 5 (ASP Flooding): SPIDER (Sensitivity Diagram)

Oil PriceAlkaline PricePolymer PriceSurfactant PriceDiscount Rate

72

Discussion and Summary

7 Discussion and Summary

The EOR screening criteria suggested by Taber et al. (1) was applied to Norne E-

segment in order to come up with right EOR method that would reduce residual

oil saturation. Five EOR scenarios such as surfactant flooding, alkaline-surfactant

flooding, polymer flooding, surfactant-polymer and ASP were simulated for the

Norne E-segment. After comparative study, ASP flooding was concluded as a

possible EOR method for the Norne E-segment.

Before simulating these scenarios, the injector and producer for EOR study were

selected. The surfactant injection in well F3-H gave comparatively little higher

recovery factor with less surfactant consumption than injection in F-1H or F-1H &

F-3H. This is because F-1H is located in water zone; the injected surfactant will

spread out instead of attacking the residual oil. Also, surfactant has to travel for

long before it can start to mobilize capillary trapped oil and a lot of surfactant is

required due to adsorption/retention. The well E-2H was selected as a producer

because of its location in our main target formations.

The scenario 1 was about surfactant flooding. A series of simulation cases were

done in order to know the right quantity of surfactant that would yield maximum

recovery of residual trapped oil. An attempt was made to compare continuous

injection with slug injection. The injection of surfactant in slug form at 6 months

interval (for 3 years) instead of continuous injection (for 3 or 6 years) was

chosen. The incremental oil recovery for 3 or 6 years continuous injection

compared to 3 years slug injection was not encouraging and it was rather

wasteful to inject surfactant in former case. Also, slug injection has less effects of

surfactant adsorption than continuous. The surfactant slug injection during 3

years involves injecting surfactant for one month after every 6 months interval

followed by water. Injecting surfactant of higher concentration (10 kg/m3, 15

kg/m3 and 30 kg/m3) did not prove to be profitable than 5 kg/m3 concentration,

because of no significant difference in cumulative oil production.

Having established the most effective surfactant flooding method, one case was

simulated to calculate the incremental NPV for scenario 1. The drop in

cumulative incremental oil production after some years with production can be

explained by a poorer sweep efficiency achieved with the use of surfactant. Due

to heterogeneous nature of the reservoir, the breakthrough of the surfactant will

first occur in the layers of high permeability causing the decrease of residual oil

73


saturation in theses layers and there is expected that relative permeability to

water has increased, simply because the water is trapped by less oil. Thus, low

permeability layers will be attacked less by the surfactant. Many researchers

have validated these findings through their experiments. For our case, the first

rapid increase in oil production occurred from Tofte formation, having two high

permeability layers, while the later increase was from other layers of Ile and

Tofte after the oil bank was made up.

For scenario 2, the addition of alkali (2 kg/m3) in low concentration surfactant

slug resulted in almost similar cumulative incremental oil production but higher

incremental NPV than scenario 1. Alkali performed well in reducing the residual

oil saturation by generating additional in-situ surfactant, resulting in less

surfactant required. Surfactant concentration of 0.3 kg/m3 for this scenario

generated higher NPV than other concentrations (2 kg/m3 and 5 kg/m3). The

trend of incremental oil production curve is similar to scenario 1.

The scenario 3 is about polymer flooding. Unlike surfactant, polymer is not able

to reduce the interfacial tension between water and oil but it improves

volumetric sweep efficiency by increasing water viscosity. This classical theory

was proved in our case, where high incremental oil production with less water

production was obtained compared to water flooding. The peak of oil production

was achieved in 2010 and after 2013 the oil production became lower than water

flooding. This is because due to good volumetric sweep efficiency (piston like),

the more oil was produced before the breakthrough of front and after

breakthrough low oil production was due to less oil saturation left behind. This

behavior is matching with Figure 6.15 of Buckley Leverett solution. The polymer

of concentration 1.0 kg/m3 had high incremental oil production because of high

viscosity as well high incremental NPV than other concentrations (0.5 kg/m3 and

0.7 kg/m3). The low NPV of this scenario compared to scenario 1 and scenario 2 is

because polymer injection alone cannot release capillary trapped oil. Instead,

polymer with alkaline and/or surfactant would yield high recovery.

The scenario 4 is about surfactant-polymer flooding. The addition of mobility

control (polymer) in Scenario 1 (surfactant slug) resulted in high incremental

recovery due to good volumetric sweep efficiency. ‘SP slug followed by polymer’

gave more incremental oil production as well NPV than ‘surfactant slug followed

by polymer’. This is because polymer in the surfactant slug due to its high

viscosity makes favorable mobility ratio and also reduces the chances of

74


fingering, resulting in high incremental oil production. The NPV of this scenario is

comparatively higher than scenario 1, scenario 2 and scenario 3.

In scenario 5, the synergistic effect of alkaline, surfactant and polymer (ASP)

resulted in less surfactant required to recover significant incremental oil. As

mentioned in scenario 2 and scenario 4 that alkali makes the project profitable

by creating the natural soap in-situ (if total acid number (TAN) is high), reducing

the expenses of surfactant while polymer acts as viscosity modifier and helps

mobilize the oil. Having above qualities, the highest NPV was achieved for ASP

flooding (scenario 5) than other scenarios. The 1.40% incremental recovery

factor by ASP flooding would generate a net present value of +123.53 million

USD.

The desorption effect was restricted in all scenarios. When a case of ASP was run

with active desorption model, the adsorbed surfactant and polymer in the block

decreased with time. This is due to presence of alkali and also continued

exposure to water flood. If desorption is prevented then the adsorbed chemicals

may not decrease with time.

In the end, single price sensitivity (Spider plot) at different oil prices, chemicals

prices, and discount rate for low case, base case, and high case was also

performed. It was found that change in oil price has substantial effect on NPV

compared to other parameters while surfactant price is the least sensitive

parameter i.e very low affect on NPV for high/low case.

Thus, in terms of high incremental NPV among all scenarios, the ASP flooding

seems an attractive EOR method for Norne E-segment especially when the Ile

and Tofte formations are targeted. The success of this method depends on the

identification of the proper alkali, surfactant, and polymer and on the way they

are combined to produce compatible formulation that yields good crude oil

emulsification / mobilization, low chemical losses and good mobility control (29).

The operational and additional facilities costs; and oil and chemicals prices also

play important roles in the success of an EOR project which are not included in

the economics calculation. In addition, the chemical properties (alkali, surfactant

and polymer) used in this study are not actually related to Norne E-segment and

practically may or may not be compatible with Norne reservoir and fluid

characteristics; because no laboratory research regarding this chemical EOR

method has been performed yet.

75

Conclusion and Recommendation

8 Conclusion and Recommendation

8.1 Conclusion

The ASP flooding is a good candidate for an EOR project at Norne E-segment

and should be injected earlier.

The ASP flooding can increase the incremental recovery factor of 1.40% in the

target formations (Ile & Tofte)

The injector F-3H will be more viable candidate for ASP flooding than F-1H.

The producer E-2H will be better candidate for ASP flooding because of its

location in our main target formations.

The injection of S/AS/SP/ASP in slug form at 6 months interval (for 3 years)

was economically viable than continuous injection (for 3 or 6 years).

Surfactant concentration of 5 kg/m3 gave same oil production with less

surfactant consumption compared to 10 kg/m3, 15 kg/m3 and 30 kg/m3.

Polymer concentration of 1 kg/m3 gave higher cumulative incremental NPV

compared to 0.5 kg/m3 and 0.7 kg/m3.

It was found from single parameter sensitivity analysis (Spider plot) that

change in oil price (for low case, base case, and high case) has considerable

effect on NPV compared to discount rate, alkali price, surfactant price and

polymer price.

Surfactant price is the least sensitive parameter i.e very low affect on NPV for

high/low case.

8.2 Recommendation

It is recommended that right alkali, surfactant and polymer structure that would

be compatible with fluid and rock properties of Norne field E-segment, be

developed in the laboratory. It is also important that up-scaling the appropriate

laboratory identified chemicals to a field-scale usage be done correctly. The

timing of ASP injection into Norne E-segment is also recommended to be early in

the life of the field because injection of ASP at a later time might not lead to best

possible oil recovery. The addition of co-surfactants and co-solvents in the ASP

solution could also be tried out to see if it would make the flooding more

effective.

76

<Bibliography

Bibliography

1. Taber, J.J, Martin, F.D and Seright, R.S. EOR Screening Criteria Revisited-Part1:

Introduction to Screening Criteria and Enhanced Recovery Field Projects, SPE

Reservoir Engineering. August 1997. 12(3):189-198.

2. Sheng, James J. Modern Chemical Enhanced Oil Recovery (Theory and

Practice), Elsevier Inc, USA. 2011.

3. Donnelly, John. Global Demand Surge, JPT Editor. August 2011.

4. Awan, A.R and Reigland, R. and Kleppe, J. A Survey of North Sea Enhanced-Oil-

Recovery Projects Initiated during the Years 1975 to 2005, SPE 99546. April 2006.

5. Norwegian Petroleum Directorate (NPD). Improved Recovery without Side

Effects (http://www.npd.no/en/Topics/Improved-

Recovery/Temaartikler/Improved-recovery-without-side-effects/) accessed on

August 8, 2011.

6. Cheng, Nan. Principal Engineer, Reservoir Technology at Statoil, Rotvoll

Trondheim Norway (contacted on August 11, 2011).

7. Emegwalu, Chinenye Clara. Thesis-Enhanced Oil Recovery for Norne Field's E-

Segment using Surfactant Flooding. 2010.

8. Kalnaes, Per Einar. Thesis-An EOR Evaluation of Surfactant Flooding in the

Norne E-Segment based on Applied Reservoir Simulation. June 2010.

9. Awolola, Kazeem, Bjornar, Mariann and Asphaug, Engeset and Sarkar,

Summe. The Potential for Surfactant Flooding in the Norne E-segment, Experts in

Team (Norne Village). March 2011.

10. Sundt, Tine, et al. Comparative Study of Different EOR Methods for Norne E-

segment, Experts in Team (Norne Village). May, 2011.

11. Chierici, Gian Luigi. Principle of Petroleum Reservoir Engineering, volume 2,

Springer-Verlag. 1995.

12. Schmidt, R.L. Thermal Enhanced Oil Recovery—Current Status and Future

Needs. January 1990.

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13. Ali, S.M.Farouq and Thomas, S. A Realistic Look at Enhanced Oil Recovery,

Scientia Iranica, Vol. 1, No. 3, Sharif University of Technology. October 1994.

14. Statoil (2004). Annual Reservoir Development Plan, Norne Field.

15. IO-Center (2011). Welcome to IO Center-Norne Benchmark Case, Introduction

to Norne Field,

(http://www.ipt.ntnu.no/~norne/wiki/data/media/english/gfi/introduction-to-

the-norne-field.pdf), accessed on July 7, 2011.

16. Statoil (2001). PL 128 Norne, Reservoir Management Plan.

17. NPD (2010). The NPD's fact-pages. [Online]

(http://www.npd.no/engelsk/cwi/pbl/en/field/all/43778.htm), accessed on July

8, 2011.

18. Statoil (1994). PDO-Reservoir Geology, Support Documentation.

19. Statoil (2006). Annual Reservoir Development Plan, Norne and Urd Field.

20. Statoil (1994). PL 128 Plan for Development and Operation Support

Document-Reservoir Engineering. June 1994.

21. Green, D.W and Willhite, G.P. Enhanced Oil Recovery-SPE Text-book Series

vol 6, Society of Petroleum Engineers, Richardson Texas. 1998.

22. Lake, Larry W. Enhanced Oil Recovery, University of Texas at Austin. 1989.

23. Akstinat, M.H. Surfactants for EOR Process in High-Salinity System: Product

Selection and Evaluation, F.J Fayers(ed.) New York, Elsevier. 1981.

24. Kleppe, Jon and Skjaeveland, Svein. M. SPOR Monograph-Recent Advances

in Improved Oil Recovery Methods for North Sea Sandstone Reservoirs, NPD

Stavanger. 1992.

25. Liu, Shunhua. Alkaline Surfactant Polymer Enhanced Oil Recovery Process,

Phd Thesis, Rice University Houstan Texas. Januray, 2008.

26. FLOPAAM™. Polymers for Enhanced Oil Recovery from design to injection

(http://www.snf-group.com/IMG/pdf/Oil_EOR-FLOPAAM-Desert.pdf) accessed

on July 22, 2011 . [Online]

78

27. Petroleum Development Oman. Polymer Flooding

(http://www.pdo.co.om/pdoweb/tabid/278/Default.aspx), accesed on July 22,

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28. S.Majidaie, Tan, Isa M. and Demiral, Birol M.R. Review of ASP Flooding,

Universitti Teknologi PETRONAS, Malaysia.

29. Al-Hashim, H.S., et al. Alkaline Surfactant Polymer Formulation for Carbonate

Reservoirs, Petroleum Science and Technology, 23:723-746,2005. May 5, 2004.

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SP_cutsheet.pdf), accessed on July 26, 2011.

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(www.unitedenergy.com/pdf/2008_03_EOR_Enhanced_Oil_Recovery.pdf)

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32. Schlumberger. Eclipse Technical Description Manual . 2009.2.

33. Investopedia. Net Present Value

(http://www.investopedia.com/terms/n/npv.asp) accessed on July 27, 2011.

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35. Nguyen, Long Hai. Master's Thesis, Reservoir Simulation for IOR with Polymer

Flooding in Norwegian Field, NTNU. June 2008.

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on July 10, 2011.

79

APPENDICES

80

APPENDICES

A. The Surfactant Model in Eclipse

A.1 Calculation of the Capillary Number

The capillary number is a dimensionless ratio of viscous forces to capillary forces (32).

(A. 1)

where

K = permeability

P = potential

ST = interfacial tension

Cunit = conversion factor depending on the unit used

is calculated as

(A. 2)

where for cell i

(A. 3)

and similarly for the y and z directions.

A.2 Relative Permeability Model

The Relative Permeability model is very important in Eclipse and is a transition

from immiscible relative permeability curves at low capillary number to miscible

relative permeability curves at high capillary number. The user supplies a table

that describes the transition as a function of log10 (capillary number).

The procedure to calculate oil to water relative permeability curve is illustrated in

Figure A. 1. Firstly the end points of the curve are interpolated (point A) and both

the immiscible and the miscible curves are scaled between A and B. The relative

permeability values are looked up on both curves, and the final relative

permeability is taken as an interpolation between these two values (32).

81

APPENDICES

Figure A. 1 Calculation of relative permeability (32)

A.3 Capillary Pressure

Capillary pressure is the difference in pressure across the interface between two

immiscible fluids and is defined as

(A. 4)

The water oil capillary pressure will reduce as the concentration of surfactant

increases and actually it is the reduction in the water oil capillary pressure that

reduces the residual oil saturation (32). The oil water capillary pressure is given as:

(A. 5)

where

= surface tension at the present surfactant concentration

= surface tension at zero concentration

= capillary pressure from the immiscible curves initially scaled to the interpolated end points calculated in the relative permeability model.

A.4 Water PVT Properties

The surfactant modifies the viscosity of the pure or salted water input using the

PVTW or PVTWSALT keyword respectively. The SURFVISC keyword is used to

represent the surfactant viscosity as a function of surfactant concentration and is

82

APPENDICES

used to calculate the water-surfactant solution viscosity as shown in equation A.6 (32).

(A. 6)

where

= surfactant viscosity

= water viscosity

= viscosity of the water-surfactant mixture.

= reference pressure in the PVTW or PVTWSALT keywords.

= reference salt concentration

A.5 Adsorption

The adsorption of surfactant is assumed to be immediate, and the quantity

adsorbed is a function of the surrounding surfactant concentration and is given

by (32):

(A. 7)

Where

= Pore volume of the cell

MD = mass density of the rock

= adsorption isotherm as a function of local surfactant concentration

in solution

A.6 Keywords for Surfactant Flood Model in Eclipse 100

The model is activated by specifying the keyword SURFACT in the RUNSPEC

section. In the PROPS section three keywords (SURFVISC, SURFST and SURFCAPD)

and one in the Schedule section (WSURFACT) are obligatory while two optional

keywords could be used for cases in which adsorption takes place. Table A.1

gives a summary of the surfactant keywords and their descriptions (32).

83

APPENDICES

Table A.1: Surfactant Model Keywords (32)

Keyword Description Notes

SURFACT Activates Surfactant model and keyword has no associated data

Obligatory

SURFST Water-oil Surface tension in the presence of surfactant

Obligatory

SURFVISC Modified water viscosity Obligatory

SURFCAPD Capillary de-saturation data Obligatory

SURFROCK Rock properties and adsorption model indicator

If SURFADS is present

SURFNUM For specifying miscible relative permeability curves

SURFADS Adsorption isotherm Optional

WSURFACT Injected surfactant concentration of water injector

Obligatory

84

APPENDICES

B The Polymer Flood Model in Eclipse

B.1 Material Balance for Polymer Flooding

The standard black-oil equations used to describe the hydrocarbon phases in the

model are as follows:

Water:

(B. 1)

Polymer:

(B. 2)

Brine:

(B. 3)

(B. 4)

where

= dead pore space within each grid cell

= polymer adsorption concentration

= mass density of the rock formation

= relative permeability reduction factor for the aqueous phase due to polymer retention

= polymer and salt concentrations respectively in the aqueous phase

= effective viscosity of the water (a=w), polymer (a=p) and salt (a=s).

= cell center depth.

= transmissibility

= block pore volume

= water production rate

The model assumes that the density and formation volume factor of the aqueous

phase are independent of the polymer and salt concentrations. The polymer

solution, reservoir brine and the injected water are represented in the model as

miscible components in the aqueous phase, where the degree of mixing is

specified through the viscosity terms in the conservation equations (32).

85

APPENDICES

B.2 Treatment of Fluid Viscosities

The viscosity terms used in the fluid flow equations contain the effects of a

change in the viscosity of the aqueous phase due to the presence of polymer and

salt in the solution.

To get the effective polymer viscosity, it is required to enter the viscosity of a

fully mixed polymer solution as an increasing function of the polymer

concentration in solution . The effective polymer viscosity is calculated

as follows:

(B. 5)

where

= Todd-Longstaff mixing parameter

The mixing parameter is useful in modeling the degree of segregation between

the water and the injected polymer solution. If ω = 1 then the polymer solution

and water are fully mixed in each block. If ω = 0 the polymer solution is

completely segregated from the water (32).

B.3 Treatment of Polymer Adsorption

Adsorption is treated as an instantaneous effect in the model. The effect of

polymer adsorption is to create a stripped water bank at the leading edge of the

slug. Desorption effects may occur as the slug passes.

There are two adsorption models, either can be selected. The first model ensures

that each grid block retraces the adsorption isotherm as the alkaline

concentration rises and falls in the cell. The second model assumes that if

desorption is prevented then the adsorbed polymer concentration may not

decrease with time, and hence does not allow for any desorption (32).

B.4 Treatment of Permeability Reductions and Dead Pore Volume

The adsorption process causes a reduction in the permeability of the rock to the

passage of the aqueous phase and is directly correlated to the adsorbed polymer

concentration. In order to compute the reduction in rock permeability, it is

required to specify the residual resistance factor (RRF) for each rock type. The

actual resistance factor can be calculated as:

86

APPENDICES

(B. 6)

The value of the maximum adsorbed concentration, , depends on the rock

type and needs to be specified by the user. This value must be non zero. The user

should also specify the dead pore volume for each rock type. It represents the

amount of total pore volume in each grid cell that is inaccessible to the polymer

solution (32).

B.5 Treatment of Shear Thinning Effect

The shear thinning of polymer has the effect of reducing the polymer viscosity at

higher flow rates. Eclipse assumes that shear rate is proportional to the flow

viscosity. This assumption is not valid in general, as for example, a given flow in a

low permeability rock will have to pass through smaller pore throats than the

same flow in a high permeability rock, and consequently the shear rate will be

higher in the low permeability rock. However for a single reservoir this

assumption is probably reasonable (32).

The reduction in the polymer viscosity is assumed to be reversible as a function

of the water velocity and is calculated as:

(B. 7)

where

= shear viscosity of the polymer solution (water + polymer)

= effective water viscosity

= viscosity multiplier assuming no shear effect

= shear thinning multiplier

= 1 represents no shear thinning, whereas = 0 shows maximum shear thinning

B.6 Keywords for Polymer Flood Model in Eclipse 100

The model is activated by the keyword POLYMER in the RUNSPEC section. The

mixing parameter data is obligatory and should be defined using the keyword

TLMIXPAR. The maximum number of mixing parameter regions is set using the

parameter NTMISC in the keyword MISCIBLE. The maximum polymer and salt

concentrations to be used in calculating the effective fluid component viscosities

87

APPENDICES

are entered under the keyword PLYMAX. The viscosity of a fully mixed polymer

solution is defined by the keyword PLYVISC.

The polymer adsorption data should be entered using the keyword PLYADS in the

PROPS section. Other polymer-rock parameters such as the rock mass density

used in the adsorption calculation, the dead pore volume and the residual

resistance factor are input using the keyword PLYROCK. The shear thinning

model is activated if the PLYSHEAR keyword is present in the PROPS section. The

shear thinning data consists of tables of viscosity reduction as a function of local

velocity. The concentration of the injected polymer in water injector is specified

using the WPOLYMER keyword in the SCHEDULE section (32).

88

APPENDICES

C The Alkaline Flood Model in Eclipse

C.1 Alkaline Conservation Equation

The alkaline is assumed to exist only in the water phase a concentration in a

water injector. The distribution of the injected alkaline is modeled by solving a

conservation equation:

(C. 1)

where

= alkaline adsorption concentration

= mass density of the rock formation

= alkaline concentrations

= effective viscosity of salt

= cell center depth.

= transmissibility

= block pore volume

= water production rate

The alkaline concentrations are updated at the end of a time-step after the inter-

block phase flows have been determined (32).

C.2 Treatment of Adsorption

The adsorption of alkaline is assumed to be instantaneous. The isotherm

adsorption is specified as a look-up table of adsorbed alkaline as a function of

alkaline concentration using the ALKADS keyword.

If desorption is prevented then the adsorbed alkaline concentration may not

decrease with time. If desorption is allowed then each grid block retraces the

adsorption isotherm as the alkaline concentration falls in the cell (32).

C.3 Alkaline Effect on Water-Oil Surface Tension

Alkaline effect on the water-oil surface tension is modeled as a combined effect

with surfactant by modifying the water-oil surface tension as follows (32):

(C. 2)

89

APPENDICES

= surface tension at surfactant concentration and zero alkaline

concentration

= surface tension multiplier at alkaline concentration

C.4 Alkaline Effect on Surfactant/Polymer Adsorption

The alkaline can reduce the adsorption of both surfactant and polymer on the

rock surface. This is modeled by modifying the mass of adsorbed surfactant or

polymer as follows (32):

(C. 3)

where

= pore volume of the cell

= surfactant/polymer adsorbed concentration

= adsorption multiplier at alkaline concentration

C.5 Keywords for Polymer Flood Model in Eclipse 100

This model is activated by specifying the ALKALINE keyword in the RUNSPEC

section. The Polymer Flood Model or the Surfactant Flood Model should be

active as well. Table C.1 gives a summary of the alkaline model keywords and

their descriptions (32).

Table C.1: Alkaline Model Keywords

Keyword Description Notes

ALKALINE Activates Alkaline model and keyword has no associated data

Obligatory

ALSURFST Table of oil/water surface tension as a function of alkaline concentration

Obligatory if the Surfactant Flood Model is active

ALSURFAD Table of surfactant adsorption as a function of alkaline concentration

Obligatory if the SURFADS keyword is used

ALPOLADS Table of Polymer adsorption as a function of alkaline concentration

Obligatory if the Polymer Flood Model is active

ALKADS Table of adsorption functions Optional

ALKROCK Specifies alkaline-rock adsorption/desorption properties

Obligatory if ALKADS is used

WSURFACT Specifies the concentration of the injected alkaline in a water injector

Obligatory

90

APPENDICES

D Alkaline Input File

ALSURFST --Water/oil surface tension multipliers as a function of alkaline concentration --Alkaline Water/oil Surface --concentration Tension Multiplier --kg/m3 0.0 1.0 6.0 0.5 15.0 0.3 20.0 0.1 30.0 0.0 / ALPOLADS --Alkaline multipliers for polymer adsorption --Alkaline conc. Adsorption --Kg/m3 Multiplier 0.0 1.0 3.0 0.7 6.0 0.5 9.0 0.3 / ALSURFAD --Alkaline multipliers for surfactant adsorption --Alkaline Adsorption --concentration Multiplier --Kg/m3 0.0 1.0 3.0 0.7 6.0 0.5 9.0 0.0 / ALKADS --Alkaline adsorption --Alkaline Alkaline Adsorbed --concentration on rock --Kg/m3 (kg/kg) 0.0 0.000000 3.0 0.000005 6.0 0.000007 9.0 0.000008 10.0 0.000009 / ALKROCK -- No desorption 1 /

91

APPENDICES

E Surfactant Input File

SURFST -- Surfactant Water/oil Surface --conc., kg/m3 Tension, N/m 0 30.0E-03 0.1 10.0E-03 0.25 1.60E-03 0.5 0.40E-03 1.0 0.07E-03 3.0 0.006E-03 5.0 0.004E-03 10.0 0.006E-03 20.0 0.01E-03 /

SURFVISC --Surf conc Water Viscosity --Kg/m3 Centipoise 0.0 0.42 5.0 0.449 10.0 0.503 15.0 0.540 20.0 0.630 /

SURFADS --Surfactant Adsorption by rock --Surf conc Adsorbed mass --Kg/m3 (kg/kg) = kg surf /kg rock 0.0 0.00000 1.0 0.00017 5.0 0.00017 10.0 0.00017 /

SURFCAPD --Capillary De-saturation curve --Log10 (capillary Miscibility --number) function 0 = immiscible, 1= miscible -8 0.0 -7 0.0 -6 0.0 -5.0 0.0 -2.5 1.0 0 1.0 5 1.0 10 1.0/ SURFROCK -- No desorption 1 2650/

92

APPENDICES

F Polymer Input File

PLYSHEAR --Polymer shear thinning data -- Wat. Velocity Visc reduction -- m/day CP 0.0 1.0 2.0 1.0 / PLYVISC -- Polymer solution Viscosity Function -- Ply conc. Wat. Visc. mult. -- kg/m3 0.0 1.0 0.1 1.55 0.3 2.55 0.5 5.125 0.7 8.125 1.0 21.2 / PLYADS -- Polymer Adsorption Function -- Ply conc. Ply conc. Adsorbed -- kg/m3 by rock, Kg/kg 0.0 0.0 0.5 0.0000017 1.0 0.0000017 / TLMIXPAR -- Todd-Long staff Mixing Parameters 1 1* / PLYMAX -- Polymer-Salt concentration for mixing maximum polymer and salt concentration -- Ply conc. Salt conc. -- kg/m3 kg/m3 1.0 0.0 / PLYROCK --Polymer-Rock Properties --dead pore residual resistance mass Ads. max. Polymer -- space factor density Index adsorption 0.16 1.0 2650 1 0.000017 /

93

APPENDICES

Discount Factor 0.08

Oil Price USD/bbl 90

Alkaline Price USD/kg 1.50 Polymer Price USD/kg 4.00 Surfactant Price USD/kg 3.50 Color code Red: Input

Table G.2: Scenario 1: Incremental NPV for Surfactant Slug (5kg/m3) Injection for 3 years (6 months interval)

Time

Annual Oil Production

Annual Annual Present

Value (PV) NPV

Base Case

With Surfactant

Oil Increment

Surfactant Consumption

Year Year Sm3 Sm3 Sm3 bbl kg Million USD Million USD

2005 0 701,610 701,610 0 0 0 0.00 0.00

2006 1 565,280 597,110 31,830 200,204 1,085,000 13.17 13.17

2007 2 465,850 499,440 33,590 211,274 1,085,000 13.05 26.21

2008 3 388,310 415,270 26,960 169,573 1,085,000 9.10 35.31

2009 4 332,590 356,830 24,240 152,465 0 10.09 45.40

2010 5 291,440 318,040 26,600 167,309 0 10.25 55.65

2011 6 254,430 279,490 25,060 157,622 0 8.94 64.59

2012 7 232,890 256,730 23,840 149,949 0 7.87 72.46

2013 8 211,040 235,440 24,400 153,471 0 7.46 79.92

2014 9 191,500 215,090 23,590 148,376 0 6.68 86.61

2015 10 160,900 180,760 19,860 124,915 0 5.21 91.81

The NPV for Scenario 1 91.81

94

APPENDICES

Table G.3: Scenario 2 (6.2.1): Incremental NPV for Alkaline-Surfactant Slug (2kg/m3 & 0.3kg/m3) Injection for 3 years (6 months interval)

Time


Annual Annual Annual

Present Value (PV)

NPV Base Case

With Alkaline-

Surfactant OilIncrement

Alkaline Consumption


Year Year Sm3 Sm3 Sm3 bbl kg kg Million USD Million

USD

2005 0 701,610 701,610 0 0 0 0 0.00 0.00

2006 1 565,280 597,080 31,800 200,016 434,000 65,100 15.85 15.85

2007 2 465,850 499,160 33,310 209,513 434,000 65,100 15.41 31.27

2008 3 388,310 414,930 26,620 167,434 434,000 65,100 11.26 42.53

2009 4 332,590 356,490 23,900 150,326 0 0 9.94 52.48

2010 5 291,440 317,980 26,540 166,931 0 0 10.22 62.70

2011 6 254,430 279,390 24,960 156,993 0 0 8.90 71.60

2012 7 232,890 256,580 23,690 149,005 0 0 7.82 79.43

2013 8 211,040 235,290 24,250 152,528 0 0 7.42 86.85

2014 9 191,500 215,070 23,570 148,251 0 0 6.67 93.52

2015 10 160,900 180,820 19,920 125,293 0 0 5.22 98.74

The NPV for Scenario 2 (6.2.1) 98.74

95

APPENDICES

Table G.4: Scenario 2 (6.2.2): Incremental NPV for Alkaline-Surfactant Slug (2kg/m3 & 2kg/m3) Injection for 3 years (6 months interval)

Time



Present Value (PV)

NPV Base Case

With Alkaline-

Surfactant

Oil Increment




USD

2005 0 701,610 701,610 0 0 0 0 0.00 0.00

2006 1 565,280 597,090 31,810 200,079 434,000 434,000 14.66 14.66

2007 2 465,850 499,160 33,310 209,513 434,000 434,000 14.31 28.97

2008 3 388,310 415,000 26,690 167,875 434,000 434,000 10.27 39.24

2009 4 332,590 356,590 24,000 150,955 0 0 9.99 49.23

2010 5 291,440 317,950 26,510 166,743 0 0 10.21 59.44

2011 6 254,430 279,400 24,970 157,056 0 0 8.91 68.35

2012 7 232,890 256,620 23,730 149,257 0 0 7.84 76.19

2013 8 211,040 235,360 24,320 152,968 0 0 7.44 83.62

2014 9 191,500 215,070 23,570 148,251 0 0 6.67 90.30

2015 10 160,900 180,770 19,870 124,978 0 0 5.21 95.51


96

APPENDICES

Table G.5: Scenario 2 (6.2.3): Incremental NPV for Alkaline-Surfactant Slug (2kg/m3 & 5kg/m3) Injection for 3 years (6 months interval)

Time



Present Value (PV)

NPV Base Case

With Alkaline-

Surfactant

Oil Increment




USD

2005 0 701,610 701,610 0 0 0 0 0.00 0.00

2006 1 565,280 597,150 31,870 200,456 434,000 1,085,000 12.59 12.59

2007 2 465,850 499,470 33,620 211,463 434,000 1,085,000 12.50 25.09

2008 3 388,310 415,300 26,990 169,762 434,000 1,085,000 8.60 33.69

2009 4 332,590 356,850 24,260 152,591 0 0 10.09 43.78

2010 5 291,440 318,030 26,590 167,246 0 0 10.24 54.02

2011 6 254,430 279,490 25,060 157,622 0 0 8.94 62.96

2012 7 232,890 256,700 23,810 149,760 0 0 7.86 70.83

2013 8 211,040 235,420 24,380 153,345 0 0 7.46 78.28

2014 9 191,500 215,080 23,580 148,313 0 0 6.68 84.96

2015 10 160,900 180,760 19,860 124,915 0 0 5.21 90.17


97

APPENDICES

Table G.6: Scenario 3 (6.3.1): Incremental NPV for Continuous Polymer Injection (0.5kg/m3)

Time



Value (PV) NPV

Base Case

With Polymer

Oil Increment

Polymer Consumption


2005 0 701,610 701,610 0 0 0 0.00 0.00

2006 1 565,280 569,600 4,320 27,172 638,750 -0.10 -0.10

2007 2 465,850 491,290 25,440 160,013 638,750 10.16 10.05

2008 3 388,310 418,830 30,520 191,965 640,500 11.68 21.74

2009 4 332,590 366,820 34,230 215,300 0 14.24 35.98

2010 5 291,440 307,550 16,110 101,329 0 6.21 42.19

2011 6 254,430 259,280 4,850 30,506 0 1.73 43.92

2012 7 232,890 232,310 -580 -3,648 0 -0.19 43.72

2013 8 211,040 207,400 -3,640 -22,895 0 -1.11 42.61

2014 9 191,500 186,160 -5,340 -33,588 0 -1.51 41.10

2015 10 160,900 155,190 -5,710 -35,915 0 -1.50 39.60


98

APPENDICES

Table G.7: Scenario 3 (6.3.2): Incremental NPV for Continuous Polymer Injection (0.7kg/m3)

Time



Value (PV) NPV

Base Case

With Polymer

Oil Increment

Polymer Consumption


2005 0 701,610 701,610 0 0 0 0.00 0.00

2006 1 565,280 568,810 3,530 22,203 894,250 -1.46 -1.46

2007 2 465,850 495,630 29,780 187,310 894,250 11.39 9.92

2008 3 388,310 425,790 37,480 235,742 896,700 14.00 23.92

2009 4 332,590 376,240 43,650 274,550 0 18.16 42.08

2010 5 291,440 312,210 20,770 130,639 0 8.00 50.08

2011 6 254,430 261,750 7,320 46,041 0 2.61 52.70

2012 7 232,890 232,630 -260 -1,635 0 -0.09 52.61

2013 8 211,040 207,050 -3,990 -25,096 0 -1.22 51.39

2014 9 191,500 185,460 -6,040 -37,990 0 -1.71 49.68

2015 10 160,900 154,300 -6,600 -41,513 0 -1.73 47.95


99

APPENDICES

Table G.8: Scenario 3 (6.3.3): Incremental NPV for Continuous Polymer Injection (1.0 kg/m3)

Time



Value (PV) NPV

Base Case

With Polymer

Oil Increment

Polymer Consumption


2005 0 701,610 701,610 0 0 0 0.00 0.00

2006 1 565,280 566,390 1,110 6,982 1,277,500 -4.15 -4.15

2007 2 465,850 498,260 32,410 203,852 1,277,500 11.35 7.20

2008 3 388,310 432,810 44,500 279,896 1,281,000 15.93 23.13

2009 4 332,590 390,370 57,780 363,425 0 24.04 47.17

2010 5 291,440 320,670 29,230 183,851 0 11.26 58.43

2011 6 254,430 265,190 10,760 67,678 0 3.84 62.27

2012 7 232,890 232,830 -60 -377 0 -0.02 62.25

2013 8 211,040 206,190 -4,850 -30,506 0 -1.48 60.77

2014 9 191,500 184,530 -6,970 -43,840 0 -1.97 58.79

2015 10 160,900 153,400 -7,500 -47,174 0 -1.97 56.83


100

APPENDICES

Table G.9: Scenario 4 (6.4.1): Incremental NPV for Surfactant Slug (5kg/m3) Injection for 3 years (6 months interval) followed by Polymer ( 0.40 kg/m3) for 2 years and chase water

Time



Present Value (PV)

NPV Base Case

With Polymer-

Surfactant

Oil Increment

Polymer Consumption



USD

2005 0 701,610 701,610 0 0 0 0 0.00 0.00

2006 1 565,280 597,190 31,910 200,708 0 1,085,000 13.21 13.21

2007 2 465,850 499,480 33,630 211,526 0 1,085,000 13.07 26.28

2008 3 388,310 415,270 26,960 169,573 0 1,085,000 9.10 35.38

2009 4 332,590 357,920 25,330 159,321 511,000 0 9.04 44.41

2010 5 291,440 334,490 43,050 270,776 511,000 0 15.19 59.61

2011 6 254,430 303,840 49,410 310,779 0 0 17.63 77.23

2012 7 232,890 270,250 37,360 234,987 0 0 12.34 89.57

2013 8 211,040 242,680 31,640 199,009 0 0 9.68 99.25

2014 9 191,500 217,340 25,840 162,528 0 0 7.32 106.57

2015 10 160,900 179,710 18,810 118,311 0 0 4.93 111.50


101

APPENDICES

Table G.10: Scenario 4 (6.4.2): Incremental NPV for Surfactant-Polymer Slug (5kg/m3) Injection for 3 years (6 months interval) followed by Polymer ( 0.40 kg/m3) for 2 years and chase water

Time



Present Value (PV)

NPV Base Case

With Polymer-

Surfactant

Oil Increment

Polymer Consumption



USD

2005 0 701,610 701,610 0 0 0 0 0.00 0.00

2006 1 565,280 599,430 34,150 214,797 86,800 1,085,000 14.06 14.06

2007 2 465,850 503,330 37,480 235,742 86,800 1,085,000 14.64 28.70

2008 3 388,310 419,820 31,510 198,192 86,800 1,085,000 10.87 39.57

2009 4 332,590 362,430 29,840 187,688 511,000 0 10.91 50.48

2010 5 291,440 336,940 45,500 286,186 511,000 0 16.14 66.62

2011 6 254,430 304,320 49,890 313,798 0 0 17.80 84.42

2012 7 232,890 269,800 36,910 232,157 0 0 12.19 96.61

2013 8 211,040 241,540 30,500 191,839 0 0 9.33 105.94

2014 9 191,500 215,970 24,470 153,911 0 0 6.93 112.87

2015 10 160,900 178,530 17,630 110,889 0 0 4.62 117.49


102

APPENDICES

Table G.11: Scenario 5 (6.5.1): Incremental NPV for ASP (2kg/m3, 0.3kg/m3 & 0.4kg/m3) Injection for 3 years (6 months interval) followed by Polymer (0.40kg/m3) injection for 2 years and Chase Water

Time


Annual Annual Annual Annual Present Value (PV)

NPV Base Case

With ASP Oil

Increment Alkaline

Consumption Surfactant

Consumption Polymer

Consumption

Year Year Sm3 Sm3 Sm3 bbl kg kg kg Million

USD Million

USD

2005 0 701,610 701,610 0 0 0 0 0 0.00 0.00

2006 1 565,280 599,390 34,110 214,545 434,000 65,100 86,800 16.74 16.74

2007 2 465,850 503,220 37,370 235,050 434,000 65,100 86,800 17.09 33.83

2008 3 388,310 419,590 31,280 196,745 434,000 65,100 86,800 13.08 46.91

2009 4 332,590 362,110 29,520 185,675 0 0 511,000 10.78 57.69

2010 5 291,440 336,720 45,280 284,802 0 0 511,000 16.05 73.75

2011 6 254,430 303,890 49,460 311,094 0 0 0 17.64 91.39

2012 7 232,890 269,150 36,260 228,068 0 0 0 11.98 103.37

2013 8 211,040 240,680 29,640 186,430 0 0 0 9.06 112.43

2014 9 191,500 215,130 23,630 148,628 0 0 0 6.69 119.12

2015 10 160,900 177,700 16,800 105,669 0 0 0 4.41 123.53


103

APPENDICES

Table G.12: Single parameter sensitivity analysis for low case, base case, and high case at different oil prices, chemicals prices, and discount rate

Change in discount rate

Change in oil price Change in alkaline

price Change in polymer

price Change in Surfactant

price

Low Base High Low Base High Low Base High Low Base High Low Base High

Discount Factor 0.10 0.08 0.06 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08

Oil Price USD/bbl 90 90 90 40 90 140 90 90 90 90 90 90 90 90 90

Alk. Price USD/kg 1.50 1.50 1.50 1.50 1.50 1.50 2.25 1.50 0.75 1.50 1.50 1.50 1.50 1.50 1.50

Poly. Price USD/kg 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 5.50 4.00 2.50 4.00 4.00 4.00

Surf. Price USD/kg 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 3.50 5.00 3.50 2.00

Year Year Present Value (PV), Million USD

2005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2006 1 16.4 16.7 17.1 6.8 16.7 26.7 16.4 16.7 17.0 16.6 16.7 16.9 16.7 16.7 16.8

2007 2 16.5 17.1 17.7 7.0 17.1 27.2 16.8 17.1 17.4 17.0 17.1 17.2 17.0 17.1 17.2

2008 3 12.4 13.1 13.8 5.3 13.1 20.9 12.8 13.1 13.3 13.0 13.1 13.2 13.0 13.1 13.2

2009 4 10.0 10.8 11.6 4.0 10.8 17.6 10.8 10.8 10.8 10.2 10.8 11.3 10.8 10.8 10.8

2010 5 14.6 16.1 17.6 6.4 16.1 25.7 16.1 16.1 16.1 15.5 16.1 16.6 16.1 16.1 16.1

2011 6 15.8 17.6 19.7 7.8 17.6 27.4 17.6 17.6 17.6 17.6 17.6 17.6 17.6 17.6 17.6

2012 7 10.5 12.0 13.7 5.3 12.0 18.6 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0

2013 8 7.8 9.1 10.5 4.0 9.1 14.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1 9.1

2014 9 5.7 6.7 7.9 3.0 6.7 10.4 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7

2015 10 3.7 4.4 5.3 2.0 4.4 6.9 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4

NPV, Million USD 113.5 123.5 135.0 51.5 123.5 195.5 122.7 123.5 124.4 122.1 123.5 124.9 123.3 123.5 123.8

104

Prediction Input File

Prediction Input File DATES 1 'JAN' 2005 / / COMPDAT -- WELL I J K1 K2 Sat. CF DIAM KH SKIN ND DIR Ro 'F-3H' 7 57 5 5 'OPEN' 1* 3.427 0.216 328.264 2* 'Z' 18.296 / 'F-3H' 7 57 6 6 'OPEN' 1* 3.391 0.216 325.229 2* 'Z' 18.420 / 'F-3H' 7 57 7 7 'OPEN' 1* 4.284 0.216 410.644 2* 'Z' 18.352 / 'F-3H' 7 57 8 8 'OPEN' 1* 4.241 0.216 407.082 2* 'Z' 18.498 / 'F-3H' 7 57 9 9 'OPEN' 1* 9.387 0.216 902.750 2* 'Z' 18.667 / 'F-3H' 7 57 10 10 'OPEN' 1* 101.082 0.216 9741.795 2* 'Z' 18.876 / 'F-3H' 7 57 11 11 'OPEN' 1* 4.716 0.216 454.519 2* 'Z' 18.880 / 'F-3H' 7 57 12 12 'OPEN' 1* 1.659 0.216 160.153 2* 'Z' 19.011 / 'F-3H' 7 57 13 13 'OPEN' 1* 123.532 0.216 11909.055 2* 'Z' 18.905 / 'F-3H' 7 57 14 14 'OPEN' 1* 23.785 0.216 2299.001 2* 'Z' 19.163 / 'F-3H' 7 57 15 15 'OPEN' 1* 9.266 0.216 897.482 2* 'Z' 19.375 / 'F-3H' 7 56 15 15 'OPEN' 1* 2.873 0.216 278.581 2* 'Z' 19.470 / 'F-3H' 7 56 16 16 'OPEN' 1* 213.422 0.216 20730.869 2* 'Z' 19.660 / 'F-3H' 7 56 17 17 'OPEN' 1* 22.442 0.216 2174.958 2* 'Z' 19.427 / 'F-3H' 7 56 18 18 'OPEN' 1* 22.435 0.216 2194.387 2* 'Z' 20.384 / 'F-3H' 7 56 19 19 'OPEN' 1* 16.226 0.216 1590.031 2* 'Z' 20.581 / 'F-3H' 7 56 20 20 'OPEN' 1* 85.791 0.216 8439.256 2* 'Z' 21.005 / 'F-3H' 7 56 21 21 'OPEN' 1* 42.255 0.216 4130.474 2* 'Z' 20.320 / 'F-3H' 7 56 22 22 'OPEN' 1* 300.194 0.216 29777.980 2* 'Z' 21.955 / / WCONPROD 'E-2H' 'OPEN' 'BHP' 5* 235 / / WSURFACT 'F-3H' 0.0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / WCONINJE 'C-1H' 'WATER' 1* 'RATE' 10121.652 1* 450 3* / 'C-2H' 'WATER' 1* 'RATE' 9064.145 1* 450 3* / 'C-3H' 'GAS' 'SHUT' 'RATE' 1627788.907 1* 600 3* / 'C-4AH' 'WATER' 'OPEN' 'RATE' 4303.281 1* 450 3* / --F-1H injection rate tuned down from 10,000 m3/d to 7000 m3/d 'F-1H' 'WATER' 'OPEN' 'RATE' 7000 1* 450 3* / 'F-2H' 'WATER' 1* 'RATE' 5846.826 1* 450 3* / 'F-3H' 'WATER' 'OPEN' 'RATE' 3500 1* 450 3* / 'F-4H' 'WATER' 'OPEN' 'RATE' 1555.877 1* 450 3* /

105


/ RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2006 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'FEB' 2006 / / WSURFACT 'F-3H' 0.0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'AUG' 2006 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / -------------------------------------------------------

106


DATES 1 'SEP' 2006 / / WSURFACT 'F-3H' 0.0 / / WPOLYMER 'F-3H' 0.00 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / DATES 1 'MAR' 2007 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'APR' 2007 / / WSURFACT 'F-3H' 0.0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'OCT' 2007 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER

107


'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'NOV' 2007 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'MAY' 2008 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JUN' 2008 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED

108


'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'DEC' 2008 / / WSURFACT 'F-3H' 0.3 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 5 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2009 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 0 / / --F-1H injection rate tuned down from 7,000 m3/d to 5500 m3/d WCONINJE 'F-1H' 'WATER' 'OPEN' 'RATE' 5500 1* 450 3* / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2010 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.40 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / -------------------------------------------------------

109


DATES 1 'JAN' 2011 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2012 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / --F-1H injection rate tuned up from 5500 m3/d to 7000 m3/d WCONINJE 'F-1H' 'WATER' 'OPEN' 'RATE' 7000 1* 450 3* / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2013 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / -------------------------------------------------------

110


DATES 1 'JAN' 2014 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- DATES 1 'JAN' 2015 / / WSURFACT 'F-3H' 0 / / WPOLYMER 'F-3H' 0.0 0.0 / / WALKALIN 'F-3H' 0 / / DATES 1 'DEC' 2015 / / RPTSCHED 'FIP=2' 'WELLS=2' 'SUMMARY=2' ALKALINE/ 'SURFBLK' 'SURFADS' 'PLYADS' 'PBLK' / ------------------------------------------------------- -- END OF SIMULATION

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