Optimization of a Water Alternating Gas Injection

Compositional fluid flow simulation with Water Alternating Gas Injection optimization on the upscaled synthetic

reservoir CERENA-I

Fabusuyi Oluwatosin John

Thesis to obtain the Master of Science Degree in

Petroleum Engineering

Supervisors

Professor Amílcar de Oliveira Soares

Doctor Maria João Costa Almeida Quintão Pereira Braga

Examination Committee

Chairperson: Professor Maria João Correia Colunas Pereira

Supervisor: Doctor Maria João Costa Almeida Quintão Pereira Braga Members of the Committee: Engineer Carlos Martins Andrade

Professor Leonardo Azevedo Guerra Raposo Pereira

October 2015

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“My formula for success?”...Rise early, Work late, Strike oil!!!!!!

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Acknowledgments

First and foremost, I express my deepest gratitude to God Almighty for given me the

opportunity and privilege to be in this position in my life. It has been Him from the first day of

my life. This work would have been a tough task without the help and support of these great

people below:

Maria Joao Quintao Braga, my supervisor, for the help and support throughout the period of

this thesis, you are greatly appreciated, we had some fun with the ant colony idea. I also

appreciate Manuel Ribeiro for your great help.

Dr. Leonardo Azevedo, my lecturer and advisor throughout the whole 2 years of the master’s

program, I really appreciate your effort, help and encouragement and most especially during

this past year.

Maria Joao Pereira, you were a great help in integrating me into the system after arriving late

for the master’s program, and I won’t forget your effort in making sure I was comfortable.

Professor Amilcar Soares, for helping to start off the idea for the thesis, always given us

access to your wealth of knowledge and experience and being a great leader to us all.

Pedro Pinto, for bringing me under your wings and constant help even sometimes at odd

hours. You have being a true “bro”.

I would also like to thank Dmitry Eydinov, from Rock Flow Dynamics, for providing the

tNavigator licence, Schlumberger for the donation of academic licences for Petrel and Eclipse

and the Epistemy group for the Raven licenses.

A big thank you to my colleagues and classmates who have been great friends and help in

the course of these 2 years, Carolina Mateus Moreira and Carolina Carvalho, you guys made

CERENA awesome all this past year and I will forever appreciate your friendship. Yolanda

Tati, for all the help at the beginning of this master’s program, Joao Brito, for always making

sure I had some fun with studying, Catarina Marques, for being an awesome help with raven

and also while I was away.

Finally, a huge thank you to the most amazing parent in the world, Mr. & Mrs. Fabusuyi, for

the help, finances, prayers, support, encouragement and belief that you had in me. I can

never repay you. And also to an awesome friend, Adeboye Oluwafeyikemi, you have been a

pillar of support in my life and I appreciate you every day.

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Abstract Optimization of a Water Alternating Gas Injection Compositional fluid flow simulation with Simultaneous Water Alternating Gas Injection optimization on the upscaled synthetic reservoir CERENA-I

The main objective of this work is to devise a production strategy, on a Brazilian pre-salt reservoir with a huge amount of oil reserves but with considerably huge amount of gas. Our aim is to recover as much oil as possible while also minimizing the gas produced by injecting a certain amount of this gas back in to the reservoir. The use of a compositional simulator was required and a Simultaneous Water Alternating Gas Injection scheme was selected to be implemented. In the Simultaneous WAG injection scheme, water and gas are injected simultaneously thereby improving the sweep efficiency in the depleted oil reservoirs. The first part of this thesis involved the upscaling of the reservoir model due to computational constraints and the two well injection system was also modified to a single well Simultaneous Water Alternating Gas Injection system. Fluid composition was derived from the Petrel library and PVT behaviour was adjusted to fit estimated parameters such as the bubble point. The second part of the thesis involves the optimization of the production and injection schemes with parameters such as the bottom-hole pressure, injection rates and WAG ratios as variables. The chosen optimization technique for this thesis is the Particle Swarm Optimization technique. The objective functions for this optimization were chosen in other to maximize the oil recovery while reducing the gas production. This thesis is a further development on a previous work carried out at the CERENA Research Center by Pedro Pinto.

Keywords: Reservoir simulation, compositional simulation, Synthetic reservoir, Simultaneous WAG scheme, Particle Swarm Optimization, objective function.

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Resumo Otimização de Injeção Alternada Água Gás Simulação de fluidos composicionais com Injeção alternada Água Gás no reservatório sintético “upscaled” CERENA-I. O principal objetivo deste trabalho é o planeamento de uma estratégia de produção, num reservatório pré-sal brasileiro com enormes reservas mas também com uma enorme quantidade de gás. Com esta estratégia, pretende-se maximizar a produção de óleo, minimizando o gás produzido, recorrendo em parte a injeção de uma certa quantidade de gás de volta ao reservatório. Foi necessária a utilização de um simulador composicional e foi escolhido um esquema de injeção WAG (agua/gás) simultânea para ser modelado. No esquema de injeção WAG simultânea, gás e água são injetados ao mesmo tempo no reservatório. A primeira parte desta tese consistiu no “upscaling” de um modelo de reservatório, devido a restrições computacionais A composição dos fluidos foi obtida a partir de um manual de fluidos em reservatórios, amostra do Petrel, e o comportamento PVT foi ajustado de modo a ajustar os parâmetros estimados como o “bubble point”. A segunda parte da tese consistiu na otimização de esquemas de produção e injeção com parâmetros como a pressão bottom-hole, as taxas de injeção e os rácios WAG como variáveis. A técnica de otimização escolhida nesta tese foi a técnica Particle Swarm. A função objetivo foi escolhida de modo a otimizar o óleo recuperado reduzindo a produção de gás. Esta tese e um desenvolvimento de um trabalho previamente desenvolvido no centro de investigação CERENA pelo meu colega Pedro Pinto.

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Table of Contents List of Figures ........................................................................................................................... XIII

List of Tables ........................................................................................................................... XIV

List of equations ....................................................................................................................... XV

Acronynms ............................................................................................................................... XVI

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

1.1 Motivation ...................................................................................................................... 1

1.2 Objectives ...................................................................................................................... 1

1.3 Structure of the thesis .................................................................................................... 2

1.4 Methodology .................................................................................................................. 2

2. State of the art and theoretical background .......................................................................... 4

2.1 Fluid flow simulators ...................................................................................................... 4

2.1.1 Black oil Vs. Compositional simulation .................................................................. 4

2.2 Water Alternating Gas Injection Scheme ...................................................................... 6

2.2.1 Background on primary, secondary and EOR ....................................................... 6

2.2.2 Types of WAG Injection ......................................................................................... 8

2.2.2.1 Simultaneous Water Alternating Gas (SWAG) .............................................. 9

2.2.3 Factors influencing WAG process design ............................................................. 9

2.2.3.1 Fluid properties and rock fluid interaction ...................................................... 9

2.2.3.2 Reservoir Heterogeneity and Stratification .................................................... 9

2.2.3.3 Availability and composition of injection gas ............................................... 10

2.2.3.4 WAG ratio .................................................................................................... 10

2.2.3.5 Injection pattern ........................................................................................... 11

2.2.3.6 Injection / production pressure and rates .................................................... 11

2.2.3.7 WAG cycle time ........................................................................................... 11

2.2.3.8 Time to Initiate WAG process ...................................................................... 12

2.2.4 Advantages and Disadvantages of the WAG techniques ................................... 12

2.3 Optimization ................................................................................................................. 12

2.3.1 Production Optimization ...................................................................................... 14

2.3.2 Particle Swarm Optimization ............................................................................... 14

3. Dynamic simulation on the CERENA-I reservoir: Synthetic application .............................. 17

3.1 CERENA-I: Dataset description .................................................................................. 17

3.2 Fluids system ............................................................................................................... 19

3.2.1 Fluid characterization .......................................................................................... 19

3.2.2 The Equation of State .......................................................................................... 21

3.3 Dynamic model ............................................................................................................ 22

3.3.1 Sectorial model .................................................................................................... 22

3.3.2 Upscaled Reservoir ............................................................................................. 24

XII

3.3.3 Model initialization ............................................................................................... 26

3.3.4 Production Schedule ........................................................................................... 27

3.3.5 Optimization Results ............................................................................................ 29

3.3.5.1 Bottom-Hole Pressure ................................................................................. 29

3.3.5.1.1 Same Bottom-Hole Pressure ................................................................... 29

3.3.5.1.2 Different Bottom-Hole Pressure ............................................................... 31

3.3.5.2 Injection rate and WAG ratio ....................................................................... 32

3.3.5.2.1 1570sm3/day ............................................................................................ 33

3.3.5.2.2 5570 sm3/day ........................................................................................... 34

3.3.5.2.3 7570 sm3/day ........................................................................................... 35

3.3.5.3 Well position ................................................................................................ 38

3.3.5.4 CO2 capture ................................................................................................. 40

3.3.5.4.1 Fluor process ........................................................................................... 41

4. Conclusions ......................................................................................................................... 43

5. Future Work ......................................................................................................................... 45

6. Bibliography ......................................................................................................................... 46

7. Appendices .......................................................................................................................... 49

7.1 Eclipse 300 data file .................................................................................................... 49

7.2 Include file "crude.PVO" .............................................................................................. 60

7.3 Include file "scal.inc" .................................................................................................... 71

7.4 Include file "wells2.inc" ................................................................................................ 72

7.5 Include file "injeccao.inc" ............................................................................................. 73

XIII

List of Figures Fig 1: The Brazilian Pre-Salt Play (Source: ANP) ......................................................................... 1

Fig 2: Schematic representation of the workflow used for the proposed methodology ................ 3

Fig 3: Flowchart of oil recovery methods (Doghaish, 2008) .......................................................... 7

Fig 4: Schematic representation of WAG injection (U.S Department of Energy, 2013) ................ 8

Fig 5: Particle Swarm Optimization Algorithm ............................................................................. 15

Fig 6: Stratigraphic units’ model (left) and geometries of the reservoir facies (right) .................. 17

Fig 7: Porosity model ................................................................................................................... 17

Fig 8: Histogram of porosity for both facies ................................................................................. 18

Fig 9: Joint distribution of porosity and permeability for both facies of the CERENA-I model .... 18

Fig 10: North view of the permeability model (left) and histogram of permeability (right) ........... 19

Fig 11: Molar percentages of the original oil sample ................................................................... 20

Fig 12: Molar percentages of the oil with grouped components ................................................. 21

Fig 13: Phase plots for the oil (left) and gas cap (right) .............................................................. 22

Fig 14: Sectorial model area ....................................................................................................... 23

Fig 15: Porosity model ................................................................................................................. 23

Fig 16: Permeability models (x ,y at the top, z below) ................................................................. 24

Fig 17: PermZ Histograms .......................................................................................................... 24

Fig 18: PermX Histograms .......................................................................................................... 25

Fig 19: PermY Histograms .......................................................................................................... 25

Fig 20: Porosity Histogram .......................................................................................................... 25

Fig 21: Upscaled perm x and y (left), original perm x and y (right) ............................................. 26

Fig 22: Upscaled perm z (left), original perm z (right) ................................................................. 26

Fig 23: Upscaled porosity (left), original porosity (right) .............................................................. 26

Fig 24: Initial fluids in equilibrium ................................................................................................ 27

Fig 25: Production scheme for the gas cap ................................................................................. 28

Fig 26: Well locations .................................................................................................................. 28

Fig 27: Same BHP Gas production opt ....................................................................................... 29

Fig 28: Same BHP Oil production opt ......................................................................................... 30

Fig 29: FGPT vs FOPT for same BHP ........................................................................................ 30

Fig 30: Different BHP Gas production opt ................................................................................... 31

Fig 31: Different BHP oil production opt ...................................................................................... 31

Fig 32: FOPT vs FGPT Different BHp ......................................................................................... 32

Fig 33: WAG ratios at 1570 ......................................................................................................... 33

Fig 34: 1570 FOPT results .......................................................................................................... 34

Fig 35: WAG ratios at 5,570 ........................................................................................................ 34

Fig 36: 5570 WAG ratio combinations ......................................................................................... 35

Fig 37: WAG ratios at 7570 ......................................................................................................... 36

Fig 38: 7570 FOPT results .......................................................................................................... 36

Fig 39 Optimal FOPT results ....................................................................................................... 37

Fig 40: Optimal FGPT results ...................................................................................................... 38

Fig 41: 4 Reservoir Quadrants .................................................................................................... 38

Fig 42: Well placement vs FOPT optimization ............................................................................ 39

Fig 43: Well optimization regions ................................................................................................ 39

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List of Tables Table 1: Molar percentages of the original oil sample ................................................................. 19

Table 2: Molar percentages of the oil with grouped components ............................................... 20

Table 3: Estimated observations ................................................................................................. 21

Table 4: Calculated observations ................................................................................................ 21

Table 5: Tuned critical properties for the oil components ........................................................... 22

Table 6: Contact depths .............................................................................................................. 27

Table 7: Fluids originally in place ............................................................................................... 27

Table 8: Overall comparisons of CO2 removal processes .......................................................... 41

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List of equations Eqn. 1: Differences between black oil and compositional simulators (adapted from

Schlumberger, 2005) ..................................................................................................................... 4

Eqn. 2: Rachford-Rice equation .................................................................................................... 5

Eqn. 3: Ideal Gas Law ................................................................................................................... 5

Eqn. 4: Real Gas Law.................................................................................................................... 5

Eqn. 5: Peng-Robinson Equation of State..................................................................................... 5

Eqn. 6: Mathematical Optimization Notation ............................................................................... 13

Eqn. 7: WAG ratio ........................................................................................................................ 33

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Acronynms

CERENA - Centre for Natural Resources and the Environment

WAG - Water Alternating Gas

SWAG - Simultaneous Water Alternating Gas

PVT - Pressure Vapour and Temperature

EOR - Enhanced oil Recovery

CO2 - Carbondioxide

ANP - Agência Nacional do Petróleo

PermX - Permeability X direction

PermY - Permeability Y direction

PermZ - Permeability Z direction

BHP - Bottomhole Pressure

Opt - Optimization

FOPT - Field Oil Production Total

FGPT - Field Gas Production Total

Ups - Upscaled

PSO - Particle Swarm Optimization

API - American Petroleum Institute

IFT - Interfacial tension

WASP - Water Alternating Steam Process

FAWAG - Foam Assisted Water Alternating Gas

HCPV - Hydroccarbon Pore volume

gBest - Global best

pBest - personal best

sm3 - cubic meters

sm3 / day - cubic meters per day

H2S - Hydrogen Sulphide

SPE - Society of Petroleum Enigneer

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1. Introduction

1.1 Motivation

This thesis is a continued progress on the work done during my internship as a reservoir

engineer at the Centre for Petroleum Reservoir Modelling, of Instituto Superior Técnico, in

which an already made synthetic reservoir model built to replicate some features of the

Brazilian Pre-Salt Reservoirs, had some of its features re-modelled and tested in terms of its

production and eventually optimized. With the opportunity to have learnt basic optimization and

modelling skills in static and dynamic reservoir modelling with, this thesis has been an

opportunity to combine all these acquired skills.

1.2 Objectives The main purpose of this work came from a continued interest in a reservoir in the Brazilian

Pre-Salt play with a very high content of CO2. A synthetic model of this reservoir was modelled

previously and further work was carried out on the reservoir. This Brazilian Pre-salt reservoir

poses great challenges in every aspect of its production, from reservoir modelling and

management, to surface facilities. Figure 1 shows the Pre-Salt play where Jupiter, the real

analogue for this study, can be found:

Fig 1: The Brazilian Pre-Salt Play (Source: ANP)

The reservoir covers an area of 567 km2, about 300km offshore of Rio de Janeiro, in the Santos

basin. It is situated in water depths of around 2000m, with the top of the reservoir situated at

approximately 5200m. It has a 90m thick heavy oil leg with 18o API and 55% (molar) of CO2

content. It also has a gas cap of retrograde condensation gas which contains approximately

60% (molar) of CO2.

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The initial objective of this research work was to find a production strategy to optimize the oil

production and reduce the quantity of CO2 being produced, but as the researched progressed,

different ideas were introduced. One of the ideas was to remodel the crude oil in the same

reservoir to see its performance with an entirely different composition, then further the

application of different production strategies, and finally the optimization of the production of the

crude oil in the reservoir. During the course of the thesis, due to computational constraints for

the simulation and optimization procedure, the reservoir CERENA-I was upscaled, and the

upscaled version was used henceforth.

1.3 Structure of the thesis This work reflects an in-depth utilization of reservoir engineering knowledge, which is important

in the dynamic simulation modelling with applications in Water Alternating Gas injection scheme

modelling. A further study of different optimization techniques was necessary to be able to find

optimal values for optimal production of oil or reduction of gas production as the case maybe.

This thesis is divided into three major parts: the introduction and theory, where the problem is

introduced and the key theoretical concepts necessary to approach it are presented; the

simulation and optimization, where the problem is addressed and results are shown as they are

produced. Finally, some conclusions are presented, summing up an overall view of the work

and its results.

1.4 Methodology The methodology for this thesis can be basically divided into two major parts: the first part is the

creation of the models which involves the up-scaled reservoir, the oil composition model and the

water alternating gas injection scheme; the second part involves the optimization process and

simulation on the models.

The dynamic model was built recurring to the CERENA-I static model as a starting point.

Reservoir conditions were still borrowed from a neighboring field from the Brazilian Pre-Salt

play. Due to the lack of real data, fluid composition was obtained from another sample in the

Schlumberger's Petrel® fluids library and PVT behavior, in order to match the estimated bubble

point of the oil, and was modelled through a tuned equation of state in Schlumberger's PVTi

package.

The dynamic flow simulation was run in Schlumberger's Eclipse 300® and tNavigator® by Rock

Flow Dynamics and the optimization process was done using Raven by Epistemy. The

simulation code used for this work is available in section 7. A simplified schematic

representation of the workflow previously described is presented in Figure 2.

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Fig 2: Schematic representation of the workflow used for the proposed methodology

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2. State of the art and theoretical background In this section, a brief summary of the concepts and ideas used in this thesis is discussed, such

as reservoir compositional fluid flow simulation (Fanchi, 2006), Water Alternating Gas Injection

schemes (Helena Nangacovié, 2012) and the Optimization method used.

2.1 Fluid flow simulators Fluid flow simulators are software programs that allow the simulation of fluid flow in porous

media. In reservoir engineering, these software programs are used to predict reservoir

performance, study and optimize different scenarios and plan reservoir management. Mostly,

Black Oil or Compositional simulators are used for simulating reservoirs. Compositional fluid flow simulators are substantially more complex than Black Oil simulators

because they involve the calculation of fluid properties for the flow equations at every time step,

whereas a Black Oil simulator simply reads them from input Tables. Hence, more resources and

time are needed for compositional fluid flow simulators which could also translate into significant

computational requirement. Despite this complexity, the next section presents a simple

comparison between both methods.

2.1.1 Black oil Vs. Compositional simulation A generally practical comparison is discussed in this section because a detailed description of

each simulation methods would extend beyond the required span of this thesis. The

compositional simulator was used for this thesis and the advantages of using this particular

simulator are discussed at the end of this section.

In a black oil simulator, the conservation of mass applies to phases, whereas in compositional

simulation it applies to components. Therefore, in the later, the mass of the various phases may

vary in a time step but the total mass of components is maintained. This can easily be

understood by imagining the liberation of gas by an oil phase: oil loses mass as gas is liberated

but the overall mass of, for instance, methane is maintained, as the sum of masses of this

component in the gas and liquid phases (Pinto, 2014). The two simulators, black and

compositional are required to solve equations for fluids in more than one phase and dimension

depending on pressure. For oil, gas and water systems, their flows are determined by defining

the fluxes and concentrations of the conservation equations for each of the three components in

each of the three phases. A flux can be written as the density of the fluid multiplied by its

velocity in a specified direction. The reading of the pressure dependent properties for the flow equation differentiates this two

simulators, the black simulator has to be fed directly with these pressure dependent properties

(such as density) through an already prepared input table, while in a compositional simulator

they are calculated for every single time step as a function of pressure and composition, as can

be seen in equations below.

Black oil Compositional Eqn. 1: Differences between black oil and compositional simulators (adapted from Schlumberger, 2005)

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A compositional model, by knowing the components that are present in the fluid, will calculate

the phases present at the given pressure and temperature available for the reservoir, afterwards

the compositions at each phase is then calculated. Given these compositions we then have to

calculate the physical properties of each phase independently, for example the oil viscosity

(Pinto, 2014). The calculation that helps in deciding how many phases are present, and their compositions, is

called a “Flash Calculation”. The flash calculation is based

on the Rachford-Rice equation:

Eqn. 2: Rachford-Rice equation

Where Zi is the total number of moles of the i component, Ki is the K-value for the i component

and V is the unknown vapour molar fraction.

A huge amount of CPU time is spent at this stage of the process, because this process is an

iterative one performed at each time step. (Schlumberger, 2005). Knowing the liquid and vapour

molar compositions, it is possible to calculate fluid properties using an Equation of State and the

Lohrenz-Bray-Clark correlation (Lohrenz, Bray, & Clark, 1964).

An Equation of State is an analytic expression that relates volume to pressure and temperature.

The simplest Equation of State is the Ideal Gas Law, in which the product of pressure and

volume changes linearly with temperature:

Eqn. 3: Ideal Gas Law

For real gases, the deviation from ideal behaviour can be accounted for by the addition of the Z-

factor, or compressibility factor:

Eqn. 4: Real Gas Law

The most commonly used Equation of State for compositional problems is the Peng-Robinson equation: Eqn. 5: Peng-Robinson Equation of State

Where a and b are parameters inherited from the Van der Waals equation, to respectively

account for the attractive force between molecules and the finite volume of molecules. Despite

the significantly greater complexity and computational requirements when compared to a Black

Oil simulator, compositional simulation provides several clear advantages that the black oil

simply cannot match: phase behaviour; multi-contact miscibility; immiscible or near-miscible

displacement behaviour in compositionally dependent mechanisms such as vapourization,

condensation, and oil swelling; composition-dependent phase properties such as viscosity and

density on miscible sweep-out; interfacial Tension (IFT) especially the effect of IFT on residual

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oil saturation (Schlumberger, 2005). It also allows us a great deal of opportunity to study the

crude oil at a component (molar fraction) level compared to the black simulator in simulations

where decisions would have to be made based on some components in the crude oil like the

Sulphur content, nitrogen contents, carbon dioxide in this case.

2.2 Water Alternating Gas Injection Scheme

2.2.1 Background on primary, secondary and EOR Hydrocarbon is produced from underground through generally 3 methods: primary, secondary

and tertiary (enhanced recovery, EOR) methods. The primary recovery refers to the recovery of

the oil by depending solely on the natural energy of the reservoir (Archer & Wall, 1986). The oil

is moved from the pore spaces into the wellbore through the natural reservoir pressure or

gravity drive, and combined with artificial lift techniques (such as pumps) which bring the oil to

the surface. The natural driving mechanisms that provide the energy for recovery from the oil

reservoirs are rock and fluid expansion drive, depletion drive, gas cap drive, water drive, gravity

drainage drive and combination drive. When the natural energy of these reservoirs is no longer

enough to sustain the production of oil at our desired rates, other artificial means of injecting

energy into the reservoir are then implemented. Secondary recovery are recovery techniques

used to help the natural energy of the reservoir, this is done by artificially injecting fluid (gas or

water) into the reservoir to force the oil to flow into the wellbore and to the surface (Speight,

2009). Generally, the main objective of a secondary recovery program is to increase the sweep

effect of the oil towards the production wells thereby increasing the oil productivity. Secondary

recovery is also one of the ways used for restoring and maintain the reservoir pressure, which

would normally decline during the primary recovery phase. Due to its capital intensive nature,

secondary recovery should only be employed when primary recovery is no longer economically

viable to recover the oil (Latil, 1980). Secondary recovery involves water and gas injection

mainly. During water injection, water is injected into the reservoir in order to create a sweeping

effect which moves the oil towards production wells, and it is also used to maintain the pressure

in reservoirs where the caps cannot maintain the pressure anymore. Gas injection involves

injecting gas into the reservoir for the same reasons in water injection, the injected gas goes

into the oil and expands and this expansion forces the oil to flow from the pores into the

wellbore and to the surface (Zerón,

2012).Tertiary recovery which is also known as Enhanced oil recovery involves different

advanced form of oil recovery. These are sophisticated recovery techniques that are applied to

increase or boost the flow of fluid within the reservoir. Fluids different from conventional water

and gas are injected into the reservoir in order to effectively increase oil production (Zerón,

2012). In tertiary recovery, reducing the viscosity of the fluid and increasing the mobility of the

oil are some of the aims in other to increase productivity. Tertiary recovery is normally applied to

recover more of the residual oil remaining in the reservoir after both primary and secondary

recoveries have reached their economic limit. The methods include: thermal, chemical, gas, and

microbial (Speight, 2009). A flowchart of the three recovery methods is shown in Figure 3.

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Fig 3: Flowchart of oil recovery methods (Doghaish, 2008)

Water alternating gas injection (WAG), is one of the numerous enhanced recovery process.

WAG injection involves drainage (D) and imbibition (I) taking place simultaneously or in cyclic

alternation in the reservoir (Nezhad et al., 2006). It was initially proposed as a method to

improve macroscopic sweep efficiency during gas injection. WAG injection is now applied widely

to improve oil recovery from matured fields by re-injecting produced gas into water injection

wells (Touray, 2013). Due to their low viscosities, gases have high mobility which results in poor

macroscopic sweep efficiency (Hustad & Holt, 1992). WAG recovery techniques combine the

benefits of both water and gas injection, i.e., improved macroscopic sweep efficiency of water

flooding with the high displacement efficiency of gas injection in order to increase incremental oil

production (Kulkarni & Rao, 2005). WAG can further improve oil recovery through compositional

exchanges between the injected fluid and the reservoir oil (Stenby et al., 2001). The

compositional exchange leads to oil swelling and oil viscosity reduction, thus making the oil

more mobile. The reduction in residual oil saturation due to three phase and hyteresis effects

8

and the reduction in interfacial tension (IFT) are also mechanisms through which additional oil

recovery is obtain during immiscible WAG injection (Righi et al., 2004). The low interfacial

tension of the gas-oil system compared to the water-oil system enables the gas to dispel oil

from the small pore spaces that are not accessible by water alone. The injection of water in the

presence of the gas phase leads to trapping of part of the gas. This can cause mobilization of

the oil at low saturations and an effective reduction in the three phase residual oil saturation.

Since its first application in Canada in 1957, the WAG recovery techniques have been applied

widely and have been effective in improving the recovery of oil. It has been reported that 80% of

WAG projects in USA have been profitable (Sanchez, 1999). In a review of 59 fields, it was

reported that WAG injection results in an increased average oil recovery of 5% to 10% OOIP

(Stenby et al., 2001). According to the same review, the average improved oil recovery from

miscible WAG injection and immiscible WAG injection is calculated to be 9.7% and 6.4%

respectively. The use of CO2 gas results in higher improved recovery than hydrocarbon gas.

Fig. 4 shows the process of WAG (CO2) injection.

Fig 4: Schematic representation of WAG injection (U.S Department of Energy, 2013)

2.2.2 Types of WAG Injection

WAG injection can be classified into different forms depending on how the fluids are injected.

Generally, WAG injection is classified as either miscible or immiscible. Miscible or immiscible

injections are function of the properties of the displaced oil and injected gas as well as the

pressure and temperature of the reservoir (Lyons & Plisga, 2005). Hybrid WAG injection,

simultaneous WAG injection (SWAG), Water Alternating Steam Process (WASP) and foam

assisted WAG injection (FAWAG) are other different WAG injection classification. For the

purpose of this thesis, the simultaneous WAG injection (SWAG) scheme was used and it is

discussed below.

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2.2.2.1 Simultaneous Water Alternating Gas (SWAG)

SWAG is an enhanced oil recovery process in which gas is mixed with water outside and the

mixture is then injected as a two phase mixture in the well or, alternatively, both gas and water

are injected at the same time into the well to get better oil recovery. Water and gas injection are

the best solution to cope with the problems such as early breakthrough which occur only when

gas is injected individually due to unfavorable oil-gas mobility ratio. Hence, simultaneous

injection of gas and water would be of greater importance to improve the sweep efficiency by

improving the displacement front (Meshal et al., 2007). SWAG combines the benefits of

microscopic sweep efficiency obtained from miscible gas injection with better economics and

frontal stability obtained from water flooding. Water and gas can be injected alternatively in

slugs or simultaneously. The experience of using SWAG is less but the experiments in different

fields have suggested that use of SWAG as EOR process can be very crucial as it has been

seen, less well injectivity and decrease in associated problems have occurred (Tunio, Chandio

and Memon, 2012)

2.2.3 Factors influencing WAG process design

The main design issues for WAG injection techniques are fluid properties, rock-fluid interaction,

availability and composition of injection gas, WAG ratio, heterogeneous permeability, injection

pattern, cycling time, WAG ratio, injection/production pressure and rate, three-phase relative

permeability effects and flow dispersion and finally time to initiate the WAG (Christensen et al.,

2001; Heeremans et al., 2006; Zahoor, 2011;).

2.2.3.1 Fluid properties and rock fluid interaction

The fluid properties are related to the viscosity of the crude oil in the reservoir. These properties

are tested and determined in a laboratory environment. This can be difficult sometimes because

the conditions differ from the actual reservoir and also changes as the fluids travel from the sites

to the laboratories due to some undergoing processes and unexpected reactions which could

affect the tests. Variations in rock-fluid interaction with changing conditions in a reservoir result

in wettability variations, which in turn affect flow parameters such as capillary pressure and

relative permeability (Josephina et al., 2006; Zahoor, 2011).

In terms of reservoir simulation, the rock fluid properties like adhesion, spreading and wettability

are normally analyzed as one parameter, "relative permeability". Thus, this parameter is very

important when predictions are realized in the reservoir simulation (Rogers, 2000). There has

been much improvement in the preservation of the samples from the sites to the laboratories

and some possibly some important tests could be carried out at the sites to make the results

more reliable.

2.2.3.2 Reservoir Heterogeneity and Stratification

The degree of interconnection between the pores of an oil reservoir, are usually not evenly

10

distributed due to non-uniformity of pore size, which gives rise to disordered and complex

reservoir fluid flow behaviour. Geologically speaking, this is known as phenomenon of

heterogeneous permeability that can manifest different individual layers, forming different

homogeneous layers within the oil reservoir with different permeability’s. The effects of

stratification and heterogeneity can be distinct in different reservoirs, affecting various

parameters such as capillary pressure, relative permeability, and mobility ratios (Ahmad et al.,

2009; Farshid et al., 2010). The presence of different permeability’s and heterogeneity in a

reservoir, affects the displacement of the native fluids by the injected fluid. Channeling of the

solvent through high permeability regions reduces the storage and displacement efficiency of

the displacing solvent (X. WU, 2004). However, it strongly affects the efficiency in the WAG

process design, since this phenomenon controls the injection and sweep patterns in the flood.

This phenomenon can cause large variations in the vertical and horizontal permeability of the

reservoir. Vertical permeability is influenced by cross flow, Viscous, capillary, gravity and

dispersive forces (Madhav, 2003). However, high recoveries result from low vertical to

horizontal permeability ratio because the gravity segregation does not dominate the fluid flow

behaviour (Zahoor, 2011; John and Reid, 2000).

2.2.3.3 Availability and composition of injection gas

The availability of gas in WAG process design affects greatly the economic viable choice.

Usually, the gas produced with oil from a reservoir is separated and re-injected during the WAG

process, promoting less expense. This was one of the main ideas studied in this thesis. The

availability of the gas injection is very important to choose the right WAG ratio. Gas composition,

in particular, is crucial in the design WAG process, a decision parameter that determines

whether the process will be miscible or immiscible under the prevailing conditions of pressure

and temperature within the oil reservoir (Zahoor, 2011).

2.2.3.4 WAG ratio

In WAG process, gas and water slugs are alternately or simultaneously injected in a fixed ratio

called the WAG ratio. According to WU (2004), WAG ratio can also be defined as the ratio of the

volume of water injected within the reservoir compared to the volume of injected gas.

WAG ratio represents, one important parameter to optimize during WAG process. According to

Chen (2010), WAG ratio plays an important role in obtaining the optimum value of the recovery

factor corresponding to an optimal value of the WAG ratio. This optimal WAG ratio is differing

from one reservoir to another because the performance of any WAG scheme depends strongly

on the distribution of permeability as well as factors that determine the impact of gravity

segregation (fluid densities, viscosities, and reservoir flow rates) (X. WU, 2004). Studies also

shows that the WAG ratio strongly depends on reservoir’s wettability and availability of the gas

to be injected.

When the WAG ratio is high, this may lead to oil trapping by water blocking or at best may not

allow sufficient solvent-oil contact, causing the production performance to behave like a water

flood and if the WAG ratio is very small, the gas may channel and the production performance

11

may tend to behave as a gas flood, with the pressure declining rapidly, leading to early gas

breakthrough and high declination on production rate (X. WU, 2004).To find the optimal WAG

ratio, it is necessary to perform a sensitivity analysis, proposing different relations of WAG ratio

to study the effect on oil recovery. Normally, it is preferable to inject higher gas volumes as

compared to water in oil-wet reservoirs. The amount of volumes to be injected at the desired

pressures strongly affects the cost of surface facilities, like compressors and pumps, which in

turn strongly influences the WAG ratios due to economic constraints (Zahoor, 2011).

2.2.3.5 Injection pattern

The choice of the wells spacing, in WAG process design, is also very important because the

sweep efficiency of the oil is strongly affected by the distance between the injector and the

producer well (Christensen et al., 1998).

In many cases, a Five-spot injection pattern is very popular, as it can provide better control on

frontal displacement (Zahoor, 2011). However, the results of a recent study made by

Mohammad et al. (2010) in an Iranian fractured reservoir shows that a 4-spot pattern (4

producers with 2 injectors) gives a higher recovery than a 5-spot pattern (6 producers with 2

injectors). It means that the best injection patterns have to be chosen after simulation studies

analysis, because it varies from reservoir to reservoir. Chase and Tood (1984) reported about

wells’ orientation and their opinion is that the combination of vertical producers with horizontal

injectors can conduce to a better recovery. An analysis of different scenarios would go a long

way to help determine an optimal pattern.

2.2.3.6 Injection / production pressure and rates

Producer bottom-hole pressure is one of the most important factors that affect the production

performance. To study the effect of the producer bottom-hole pressure on oil recovery, earlier

simulation studies made by WU (2004) on heterogeneous reservoir showed that the producer

bottom-hole pressure should be a little less than the bubble point pressure, and at this pressure,

the oil recovery is maximum. For instance, if the producer bottom hole pressure is much lower

than the bubble point pressure, the gas breakthrough occurs very early, which leads the oil

production to decline. The injectivities of water and gas in low and high permeability layers can

be controlled by the water-gas ratio and injection rates (Surguchev, 1992).

2.2.3.7 WAG cycle time

Other variable that can be considered in optimizing WAG scheme includes the timing of the

switch from gas to water in schemes where an alternating pattern is applied. Furthermore, the

sequencing of gas, water and WAG injection across a large field can offer significant

opportunities for increases gas storage (X. WU, 2004). Previous WAG cycle design procedures

used steady state methodology and accepted industry rules of thumb. The use of a simulator

enables a more rigorous analysis to optimize WAG cycle parameters such as cycle time

(Pritchard, 1992).

12

2.2.3.8 Time to Initiate WAG process

One important factor to consider in designing the WAG process is when to initiate the WAG

process. According with WU (2004) studies made by using two approaches include starting

WAG process at the very beginning of the reservoir development (Initial WAG) as we have done

in this study, or after obvious miscible inject ant breakthrough (Post Breakthrough WAG). WU

recommends initiating the WAG injection as early as possible in the reservoir development

cycle, to maintain the average reservoir pressure and achieve a high oil recovery, but the

decision should be based on proper simulation studies.

2.2.4 Advantages and Disadvantages of the WAG techniques

This injection technique has been used in several fields. The efficiencies of these techniques

typically result in considerable amounts of oil recovery depending on the characteristics of the

reservoir and how the technique is designed. The efficiency is due to the advantages offered by

this technique, including:

Controls mobility (reduces Gas processing)

Improves operation (less gas cycling)

Improve residual oil recovery

Maintains average pressure

However, as every process, WAG technique also has disadvantages. The common

disadvantages that have been reported from different studies are the difficulticulty to control gas

breakthrough as flood matures, optimum slug size may be 50% HCPV inj., loss of injectivity

(water), which can be as high as 70%. When WAG process is well optimized, the following

desired results can be achieved in a field wide scale: improve overall recovery (sweep efficiency

and oil recovery) and improve financial performance (net cash flow).

2.3 Optimization Finding an optimal depletion strategy for hydrocarbon production has always been a key subject

in reservoir management. The underlying problem to be solved is generally the maximization of

a key quantity such as oil production, net present value (NPV), etc. In the past, optimal settings

of the optimization parameters were almost exclusively determined manually. This is generally a

quite time consuming procedure with a high likelihood of obtaining suboptimal results. While

manual approaches are still predominant strategies in the reservoir management practice, due

to the maturity of most existing major oilfields and gradual decrease in large oil discoveries,

research for more systematic optimization approaches has been initiated. Not surprisingly, most

advances have so far been made in systematic optimization, at least in theory, of water-flooding

recovery processes. The spectrum of the proposed (water flooding) optimization techniques is

already quite broad. In general, optimization consists finding the maximum or minimum value of

a function. The main elements of an optimization problem are an objective function, variables

and constraints defined as follows:

13

Objective (or cost) function: This is a quantitative measure of the performance of the system

under study, for instance FOPT, FGPT, as in this thesis, or least square error function, as in

history matching problems.

Variables: These are free/manipulative parameters that the objective function is dependent on.

Some other equal terms are optimization variables, parameters, control parameters and

decision variables. In this thesis the variables considered are well bottom-hole pressures,

injection rates, WAG ratio, etc.

Constraints: These are restrictions on variables. Considering the above definitions, more

specifically, optimization is defined as maximizing/minimizing (optimizing) the objective function

by manipulating the variables subject to the constraints. In mathematical notation, an

optimization problem is:

min 𝑓(𝑥) 𝑥 ∈ 𝑅𝑛 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜

Eqn. 6: Mathematical Optimization Notation

where 𝑓 is the objective function, 𝑥 represents the variables, and 𝑐(𝑥) denotes the equality, 𝐸,

and inequality, 𝐼, constraints. Mathematical optimization is usually defined as a minimization

problem and the maximization problem of 𝑓 is solved by minimizing −𝑓.

Optimization algorithms are generally iterative processes starting from an initial point until they

eventually come to an end, hopefully at an optimal point or stopping after a given number of

interactions. The way the different optimization techniques move from the current iteration to the

next helps to differentiate the techniques from each other. Mathematically, optimization is a

mature field and many techniques have been developed based on different kinds of problems.

The optimization algorithms can be globally classified in deterministic and stochastic search

methods.

Two good properties of an optimization method are (Nocedal and Wright, 2006):

Robustness: it should perform well on a wide variety of problems in its class, for all reasonable

values of the starting point;

Efficiency: it should not require excessive computer time or storage.

The deterministic search methods always converge to the same optimal value or point if they

are started from the same initial point (also known as initial solution, initial guess or starting

point) with the same settings (e.g. perturbation size), i.e. they are initial guess dependent. For

this type of methods, initialization of the algorithm from different initial points can be useful in an

attempt to reach a better solution. The deterministic optimization techniques can be classified as

evaluation-only and gradient based methods. Evaluation-only methods are simpler and do not

require gradient calculations. Gradient based methods use the derivatives of the objective

function to find their search direction. The gradient information is calculated by means of finite

differences approximation or in an alternative approach using an adjoint equation.

The stochastic search methods are exploring a wider solution span and every optimization

parameter combination has a nonzero chance of occurrence. The initial guesses are generated

randomly from different points and if the procedure runs long enough the global optimum is

(𝑥) = 0, 𝑖 ∈ 𝐸

(𝑥) ≤ 0, 𝑖 ∈ 𝐼

14

visited eventually. Depending on the type of the problem, some of the optimization techniques

perform better than the others, in terms of efficiency and robustness. There is generally a trade-

off between the efficiency and robustness in an optimization algorithm. Stochastic algorithms

typically require a large number of function evaluations and do not guaranty a monotonic and

continuous improvement of the objective function.

2.3.1 Production Optimization Production/reservoir optimization is performed using a reservoir model to simulate, predict and

optimize the production performance for maximizing and minimization of parameters based on

the selected objective function. An integrated production optimization requires models from the

reservoir to sales point for the field development plan including:

• Reservoir models;

• Well models;

• Production and pipeline network models;

• Process facility models;

• Economic models.

A project optimizing the asset value utilizing all above mentioned models is called integrated

optimization. A project addressing the optimization of a recovery process using only subsurface

reservoir models is referred to reservoir optimization. Nevertheless, “production optimization” is

used as a substitution for “reservoir optimization”, since it is a more general term. Using the

subsurface reservoir models, the optimization techniques might be utilized for finding the

optimal well locations (Wang et al., 2007; Sarma and Chen, 2008b) or optimizing the

production/injection strategies of a producing field. A number of studies have used the reservoir

models to maximize the field recovery without integration of the subsurface models with surface

facility models. In this thesis, the reservoir optimization techniques are evaluated using a

subsurface reservoir model in a given well configuration.

2.3.2 Particle Swarm Optimization Inspired by the flocking and schooling patterns of birds and fish, Particle Swarm Optimization

(PSO) was invented by Russell Eberhart and James Kennedy in 1995. Particle Swarm

Optimization (PSO) algorithm is a co-operative, population-based global search swarm

intelligence metaheuristics. Originally, Eberhart and Kennedy started developing computer

software simulations of birds flocking around food sources, but afterward, they realized how well

their algorithms worked on optimization problems. Over a number of iterations, a group of

variables have their values adjusted closer to the member whose value is closest to the target at

any given moment. If we imagine a flock of birds circling over an area where they can smell a

hidden source of food, the one who is closest to the food chirps the loudest and the other birds

swing around in his direction. If any of the other circling birds comes closer to the target than the

first, it chirps louder and the others veer over toward him. This tightening pattern continues until

one of the birds stumbles upon the food. It's an algorithm that is simple and easy to implement.

(Onwunalu and Durlofsky, 2011) PSO algorithm has been successfully used as a highly efficient

optimizer in numerous area. More specifically in petroleum engineering field, PSO have been

15

utilized to perform assisted history matching, optimization in several of recover processes

(Mohamed, et.al., 2010). The algorithm keeps track of three global variables:

Target value or condition

Global best (gBest) value indicating which particle's data is currently closest to the

Target

Stopping value indicating when the algorithm should stop if the Target isn't found

Each particle consists of:

Data representing a possible solution

A Velocity value indicating how much the Data can be changed

A personal best (pBest) value indicating the closest the particle's Data has ever come to

the Target

The particles' data could be anything. In the flocking birds example above, the data would be

the X, Y, Z coordinates of each bird. The individual coordinates of each bird would try to move

closer to the coordinates of the bird which is closer to the food's coordinates (gBest). If the data

is a pattern or sequence, then individual pieces of the data would be manipulated until the

pattern matches the target pattern.

The velocity value is calculated according to how far an individual's data is from the target. The

further it is, the larger the velocity value. In the bird’s example, the individuals furthest from the

food would make an effort to keep up with the others by flying faster toward the gBest bird. If the

data is a pattern or sequence, the velocity would describe how different the pattern is from the

target, and thus, how much it needs to be changed to match the target.

Fig 5: Particle Swarm Optimization Algorithm:

16

Each particle's pBest value only indicates the closest the data has ever come to the target since

the algorithm started.

The gBest value only changes when any particle's pBest value comes closer to the target than

gBest. Through each iteration of the algorithm, gBest gradually moves closer and closer to the

target until one of the particles reaches the target. The Particle Swarm Optimization technique

was chosen for this thesis because of its ease in implementation, through the utilization of

software called the Raven, made available by Epistemy, with different modified versions of the

PSO algorithm. The Raven software has been applied to many optimization and history

matching problems and it was a key factor on the results obtained in this thesis.

17

3. Dynamic simulation on the CERENA-I reservoir: Synthetic

application

3.1 CERENA-I: Dataset description The CERENA-I model was created to replicate some key characteristics of the Brazilian Pre-salt

carbonate fields. This model contains high-resolution data sets of petro-physical and petro-

elastic properties. It is based on a corner-point grid with 161x161x300 cells, with 25x25x1m

spacing. For this thesis the petro-physical parameters used were the porosity and permeability.

These models help us to show the reservoir based on a geological facies model which tries to

translate the evolution of sedimentary environments on the early stages of a carbonate basin.

The model is composed of two facies: a reservoir facies, composed by microbiolites; and a non-

reservoir facies composed by mudstones. These are located in three distinct stratigraphic units

of approximately 100 metres thickness, as shown below.

Fig 6: Stratigraphic units’ model (left) and geometries of the reservoir facies (right)

Through the use of the sequential simulation (stochastic), a porosity model was generated from

the facies model above and the resulting porosity distribution in the reservoir is shown below.

Fig 7: Porosity model

18

From the porosity figure above, the distribution of the porosity in the reservoir can be easily

understood. The low porosity values have high spatial continuity while the high porosity values

have less spatial continuity. This can also be seen in the porosity distribution plot below in figure

8, which shows the high porosity part representing the reservoir facies and the low porosity part

as the non-reservoir facies.

Fig 8: Histogram of porosity for both facies

Permeability was modelled recurring to the porosity model and it exhibits a dependence that

was derived from real analogues (Kansas Geological Survey, 2004). The joint distribution

between both properties can be seen in Figure 9.

Fig 9: Joint distribution of porosity and permeability for both facies of the CERENA-I model

The permeability model reflects the behaviour already interpreted for the porosity. It can be

seen that both facies exhibit very distinct characteristics, with the mudstones facies having a

19

very tight behaviour, and the reservoir facies showing a very heterogeneous and exponential-

type relation typical of carbonate reservoirs (Mavko, Mukerji, & Dvorkin, 2009). The histogram of

permeability for the complete model is shown in Figure 10. As expected, there is a clear

dominance of low permeability values due to the high homogeneity of the mudstones (non-

reservoir) facies.

Fig 10: North view of the permeability model (left) and histogram of permeability (right)

3.2 Fluids system To run a compositional fluid flow simulation, the understanding of the fluid system and

properties is essential. This understanding will help us in tuning the PVT behaviour of the fluid

through a selected equation of state to be able to reproduce or imitate as much as possible how

the real fluid would respond in the reservoir, during its production and how changes in reservoir

pressure affects it. A description of the chosen fluid’s composition, the tuning of the equation of

state for the fluid are discussed below, and some other experiments too are explained.

3.2.1 Fluid characterization Due to the lack of real data from analogue fields the oil composition for this study was obtained

from a generic sample of oil from Petrel® library. Table 1 presents the fluid composition.

Table 1: Molar percentages of the original oil sample

Component Molar

%

Mol.

weight

N2 0.16 28.013

CO2 0.91 44.01

C1 36.47 16.043

C2 9.67 30.07

C3 6.95 44.097

NC4 3.93 58.124

IC4 1.44 58.124

NC5 1.41 72.151

IC5 1.44 72.151

C6 4.33 84

C7+ 33.29 218

20

The components distribution according to molecular weight is shown in Figure 11.

Fig 11: Molar percentages of the original oil sample

The fingerprint plot analysis of the sample above shows the molecular weight of the different

molecules in the sample against their percentage composition in the sample. It can be seen that

the distribution of components by molecular weight has an increasing trend. For a proper

analysis, we would require a better sample due to the large continual increasing trend, it does

not provide a good description of a fluid and can lead to significant differences between real and

simulated fluid behaviour but for this case, with no real data to honor, it is considered sufficient.

The components were grouped to reduce computation time and memory requirements, and the

molar percentages were re-adjusted to the known CO2 content of the analogue field (Table 2).

Table 2: Molar percentages of the oil with grouped components

Component Molar %

Mol.

weight

CO2 55.00 44.01

C1 16.56 16.043

C2 4.46 30.037

C3 3.15 44.097 C4-6 5.69 70.237

C7+ 15.11 218

The values attributed to the C7+ fraction represents an average weight for components larger

than C7. Mostly, this part is split into smaller percentages to help improve the quality of the

results at the heavier components ends of the simulation. Due to computer time and memory

usage, this was not done. After the grouping, a new fingerprint plot of the sample is presented in

the figure below.

21

Fig 12: Molar percentages of the oil with grouped components

3.2.2 The Equation of State The equation of state is a mathematical expression that relates variables such as pressure and

volume of a given substance through several constants inherent to the substance

(Schlumberger, 2005). When in a mixture, individual components influence each other and

behave differently from when in pure substance. For that reason, their constants should be

tailored so that the equation reproduces real observed fluid behavior (Pinto, 2014). This process

is called tuning the equation of state, and it's one of the most important and hardest steps in the

creation of a compositional model. The process of tuning the Equation of state becomes harder

with increase in the number of behavior variables to be reproduced. For this case, the Equation

of State is intended to honor an estimated bubble point and dew point (Table 3).

Table 3: Estimated observations

Bubble point (bar) Dew point (bar)

493 400

Generally, when there is a gas phase present within a reservoir, the bubble point of the oil

corresponds to the pressure at the gas-oil contact. The bubble point was obtained from a

neighboring Pre-Salt analogue. The dew point for the gas was assumed as being 400bar (a) to

ensure that the entire gas cap was in supercritical conditions when the model was initialized.

The three parameter Peng-Robinson equation of state was chosen as it is the most commonly

used in the oil industry, and provides a more accurate calculation of phase density (Pinto,

2014). The regression was performed, adjusting critical pressures and temperatures for all

components (Table 4).

Table 4: Calculated observations

Bubble point (bar) Dew point (bar)

492.9964 399.9967 The regression is considered validated since there are only small differences between the

calculated and estimated observations. Table 5 contains the tuned critical temperature and

pressure for each of the oil components.

22

Table 5: Tuned critical properties for the oil components

With this we have a better understanding of the sample and a possibility of calculating the

phase behavior as a function of pressure and temperature as can be seen from the figures

below. At any chosen temperature and pressure, it is possible to predict the phase at which the

sample would be.

Fig 13: Phase plots for the oil (left) and gas cap (right)

3.3 Dynamic model

3.3.1 Sectorial model The initial simulations were being ran on a fine grid sectorial model which, despite being

considerably smaller, reproduced the total variability of the full field, but due to the

computational time needed to run this model an upscaled model was made and used for this

thesis. This option was taken so that the dynamic model could be run for the optimization model

with several iterations since less emphasis were placed on the seismic. From this point on, the

link to the original full field model is severed and the study object is now the sectorial model and

the upscaled version. For this reason, no boundary effects will be added to the dynamic model,

to account for the influence of the remaining area.

From the 16km2 and 7million cell full field model, a 1km2 and 280,000 active cells model was

created, containing active cells only for the reservoir facies (Figure 18). This was chosen due to

computational constraints. The resulting petro-physical sectorial models are shown in Figure 19.

Component

Critical pressure

(bar) Critical temperature

(oC)

CO2 119.19 -22.239

C1 74.296 -116.2

C2 78.417 -24.04

C3 68.835 31.369

C4-6 56.823 107.47

C7+ 27.546 340.27

23

Fig 14: Sectorial model area

The figure below shows the porosity model for the sectorial model, the high porosity regions are

the reservoir facies while the low porosity facies are the non-reservoir facies.

Fig 15: Porosity model

The permeability was assumed to be isotropic in the horizontal directions while the vertical

permeability was computed as 10% of the total horizontal permeability.

24

Fig 16: Permeability models (x ,y at the top, z below)

3.3.2 Upscaled Reservoir The sectorial reservoir model takes a considerable amount of computational time for the

dynamic simulation and would take extremely more time for the optimization due to the

considerable amount of iterations that is required for a full optimization. Hence, the sectorial

model was also upscaled to reduce computational time and possible memory size needed for

the optimization. The new upscaled sectorial model contains a combination of grid cells of about

22 x 22 x 154 cells compared to the 45 x 42 x 300 cells in the original sectorial model. It was

ensured that the upscaled model was able to replicate the trend of the distribution of the main

petro-physical properties of the reservoir, i.e. porosity and permeability in the 3 different

directions. A histogram comparison of the properties is presented below.

Fig 17: PermZ Histograms

25

Fig 18: PermX Histograms

Fig 19: PermY Histograms

Fig 20: Porosity Histogram

26

It can be observed that the properties in the upscaled model were made to follow the distribution

trend of the original sectorial model.

Fig 21: Upscaled perm x and y (left), original perm x and y (right)

Fig 22: Upscaled perm z (left), original perm z (right)

Fig 23: Upscaled porosity (left), original porosity (right)

From the images above, the trends, facies and distributions obtained in the original sectorial

model can also be observed in the upscaled model too with variations.

3.3.3 Model initialization The compositional model was initialized providing the simulator with the pressure and depth of

the gas-oil contact, the depth of the oil-water contact and the oil composition versus depth. The

information regarding the model constraints is synthesized in Table 6.

27

Table 6: Contact depths

Oil-Water contact

depth(m) Gas-oil contact

depth(m) Gas-oil contact

pressure(bar)

300 210 492.9964 The saturation functions used in this simulation were borrowed from a reservoir engineering

classroom exercise in and can be found in the appendix section (keywords SGFN, SOF3 and

SWFN).

Figure 21 represents reservoir fluids in equilibrium at initial conditions:

Fig 24: Initial fluids in equilibrium

After the model initialization, the fluids in place were calculated (Table 7).

Table 7: Fluids originally in place

Reservoir volume of oil Reservoir volume of gas

1.5 x 107 rm3 1.46 x 1010rm3

3.3.4 Production Schedule This section describes the production strategy employed for the oil recovery in this field. The

aim is to find the best possible strategy to maximize the amount of oil to be recovered. The

production design differs from the one designed by my Pedro Pinto, a single injection well,

planned to simultaneously inject water and gas, was used instead of the two different injection

28

wells that was used previously. We first chose to approach the reservoir by producing the gas

cap, to access the liquid condensate fraction of this volume. The fluid is condensed in surface

separators and the resulting dry gas is rejected by re-injecting it back into the gas cap, to help

keep reservoir pressure. The following Figure presents a simplified diagram of this production

concept:

Fig 25: Production scheme for the gas cap

The traditional five spot well pattern configuration was used in this production with 4 production

wells at the 4 corners and a single injection well, all vertical wells.

Fig 26: Well locations

The production wells were modelled to produce based on their bottom-hole pressure while for

29

the injection well, its injection rate and WAG ratio are the important parameters for its modeling.

These parameters were optimized for optimal recovery of oil. The details of the well parameters

can be seen in the appendix section below.

3.3.5 Optimization Results

3.3.5.1 Bottom-Hole Pressure

The bottom-hole pressure (BHP) is one of the more important parameters for a well. This well

property helps to ensure that the well flows with the desired fluid and the attempt to optimize this

property is for us to ensure the production well can produce at an optimal pressure for maximum

oil recovery. For this optimization, since we have 4 different production wells, two scenarios

were tested. The first scenario was the possibility of having all the 4 production wells operating

with the same bhp and the second scenario was trying to find a different suitable bhp for each of

4 wells and see which scenario increases the oil production. The 2 scenarios and their results

are presented in the next sections.

3.3.5.1.1 Same Bottom-Hole Pressure

The objective functions were chosen to maximize the oil recovery and also minimize the gas

production which will indirectly minimize the CO2 produced too. The bottom-hole pressure was

assigned the same unknown letter “j”, and the PSO optimization algorithm was used. The

algorithm is expected to assign the same value to the 4 wells, randomly according to a uniform

distribution between the ranges of 200 to 1200 bars, until a meaningful trend can be observed.

The observed optimization obtained is shown below. Figure 27 and 28 represents the

optimization trend for the total gas production and total oil production respectively against the

parameter. This results were obtained after 255 iterations of PSO-based algorithm optimization.

It can be observed that the suitable BHP for these wells were obtained between 200 to 500

bars.

Fig 27: Same BHP Gas production opt

30

A closer look at the plot indicates that as the BHP increases from 200 until it peaks at about 454

bars, the Field Oil Production Total increases gradually while the reverse happens for the Field

Gas Production Total as evidenced by figures 27 and 28. The optimal bhp for these 4 wells to

operate at optimal condition would be at the peak pressure of 454 bars.

Fig 28: Same BHP Oil production opt

The plot helps us to understand how the FOPT and FGPT are inter-related in terms of the BHP,

thus we can infer from this study the importance of the BHP on the productions of oil and gas.

Fig 29: FGPT vs FOPT for same BHP

31

3.3.5.1.2 Different Bottom-Hole Pressure

With the same objective functions in mind, the proposed task was to conduct the optimization

simulations by observing the production runs when the bhp of the 4 production wells is varied as

opposed to having the same value as shown above. The bhp were assigned the letters j, k, l, m

respectively and the optimization simulation was conducted within the same range. After several

days and over 300 iterations, the results obtained from this optimization are presented below.

Fig 30: Different BHP Gas production opt

Fig 31: Different BHP oil production opt

32

As it can be observed in both figures 30 and 31, after several iterations in comparison to the

previous scenario, the attempt to vary the bhp does not yield meaningful results. The series of

combinations obtained from the variation shows the oil production of about 4 million to about 6

million. From figure 32 below, there is no convincing relation between the oil and gas

production total as observed in the scenario above. Therefore, for us to improve our oil

production this reservoir and also be able to create a good management for this reservoir in the

long term, maintaining the same bhp for all the 4 wells would be good. The best result obtained

from this run was the run 62, which is the closest to what we are aiming for. The bhps obtained

from these run were 511 bars for j, 411 bars for k, 437 bars for l and 837 bars for m.

Fig 32: FOPT vs FGPT Different BHp

3.3.5.2 Injection rate and WAG ratio

Another very important variable in the production of a reservoir is the injection rate of the

injection well and how it affects our objective functions by increasing the oil production and

reducing the gas production. When conducting a WAG scheme, one important factor that is

inter-twined with the injection rate is the Water Alternating Gas ratio. This variable is defined as

the ratio of the volume of water injected to the volume of gas injected. In this scheme, a single

injection well is used and both water and gas are injected together without mixing at the top. To

model the SWAG injection well in eclipse, the injection well is assigned an initial fluid to inject

either water or gas and in this case it was water, hence the injection rate assigned initially to this

well is for the water injection. The model then allows the injection of the gas fluid simultaneously

by creating a multiple of the injection rate by the ratio to obtain the injection rate for the gas.

This ratio corresponds to the WAG ratio. This is explained further by the equations below.

33

j, and k are fractions for portions of water and gas injected respectively which could be from

0.01 to 0.99. WAG ratio = Volume of water injected: Volume of Gas

≡ Water Injection rate: Gas injection rate

≡ Water injection rate: (Water injection rate x𝑘

𝑗)

≡ j: k

Eqn. 7: WAG ratio

Initially, a random WAG ratio was used and the optimization of the injection rate was carried out

but we realized that the WAG ratio changes when the injection rate also changes. Therefore, we

decided to go the other way round by initially starting with an injection rate then trying to find the

best possible WAG ratios at that particular injection rate. The initial injection rate started with a

rate of 1570sm3/day, then we gradually increased the injection rate onwards, with the optimal

bhp of 454 bars being used.

3.3.5.2.1 1570sm3/day

With the bhp at an optimal 454 bars, the total oil and gas production shows a linear correlation.

The WAG ratio however shows an inverse correlation that leads to the production of oil and gas.

Fig 33: WAG ratios at 1570

At this injection rate, the plots obtained indicate that a combination of various WAG ratio would

yield to oil and gas production but to obtain maximum oil recovery a huge amount of gas would

have to be injected into the reservoir with a smaller portion of water also injected and to reduce

the produced gas (which will invariably reduce the oil produced), a WAG ratio with an extremely

high portion of water is needed. Some samples were obtained and the results shown below.

34

Fig 34: 1570 FOPT results

The injection rate was increased for further optimization and the results are discussed below.

3.3.5.2.2 5570 sm3/day

At this injection rate, with the production wells at bhp of 454 bars, a WAG ratio optimization test

was carried out. The total oil and gas produced in corresponding to the ratios are shown below.

Fig 35: WAG ratios at 5,570

35

The figure above shows a better trend and also a better understanding of what was observed in

the previous section. The oil recovered also increased with an increase in the injection rate, but

the WAG ratio pattern still follows the earlier trend in a more clear way. From the image, the

different values possible from 0.8 to 0.1 would produce relatively the same amount of oil with

controllable amount of gas too. To substantially increase the volume of oil produced, a larger

amount of k which corresponds to the gas injection would be needed with little amount of water

i.e. high WAG ratios of about 1:20, 1:30, but this would also increase the amount of gas

produced. Since our prior objective functions was to help increase the oil production and reduce

gas produce, then it makes sense to work within the trend line that is observed. Sample results

for different WAG combinations are showed in figure 36 below.

Fig 36: 5570 WAG ratio combinations

The injection rate was further increased to 7570sm3/day and the results are discussed below.

3.3.5.2.3 7570 sm3/day

A new and higher injection rate of 7570sm3/day was further used and as can be observed from

the images below the same trend also continued albeit more pronounced.

36

Fig 37: WAG ratios at 7570

As expected due to the increase in the injection rate, the oil production also increased. As

observed the same trend still continues with a huge amount of oil was produced when the ratio

of gas injected to the water injected is considerably high. Some sample results are shown

below.

Fig 38: 7570 FOPT results

37

The FOPT with the WAG ratio of about 88:1 represents a huge amount of water injection

compared to the gas injection in the WAG injection scheme. This leads to both a reduction in

the oil and gas production compared to the other ratio. This ratio was the lowest FOPT & FGPT

result as observed in figure 38 above. The WAG ratio 1:66 shows the extreme side of the scale

with a huge amount of gas injected compared to the water injection. We recover more oil but

also more gas is produced. The other 3 WAG ratios, 1:2, 2:3, 1:5 are examples of optimum

WAG ratios that could be use at this injection rate.

At this point with a common trend observed in the injection rates WAG studies, a random WAG

ratio within the WAG ratios that was observed to be in the optimum trend line was selected and

optimal injection rate studies were then carried out. The optimal WAG ratio selected was the

2:3, and the injection rate optimization was done with this fixed parameter. The results obtained

from the study are shown in the figures below. The results obtained below also show the

correlations between the oil and gas produced. If we carefully study the FOPT results, we can

observe 3 different sections, the first section corresponds to the region where the oil production

increases as the injection rates also increase, this continues until an injection rate of about

32,000sm3/day. Afterwards, an increase in the injection rate causes a significant jump of

production in about 100,000sm3. This total oil production is the same despite the continual

increase of the injection rate until a new oil production total is obtained observed at an injection

rate of 40,214 sm3/day. And from this point no further difference is observed. If the

corresponding gas production total is observed. All through this first 2 stages observed in the oil

production plot there was still an increase in the gas being produced up until an injection rate of

about 46,000 sm3/day. Since our initial aim was to increase oil recovery and reduce gas

production, it was necessary to pick the injection rate that best satisfy this aim. The injection

rate with the most oil recovered and less gas produced is the 40,214sm3/day, which is the

optimal injection rate for the parameters and conditions that were used for this final simulation.

Fig 39 Optimal FOPT results

38

Fig 40: Optimal FGPT results

3.3.5.3 Well position

The aim of this optimization simulation is to help us make better decision in the placement of our

wells in the reservoir to enhance better oil recovery. The optimization was carried out by dividing

the reservoir into 4 quadrants while each well was sited in each quadrant and the assumption is

that each well will be sited in an optimal cell in their respective quadrant.

Fig 41: 4 Reservoir Quadrants

The simulation was conducted with the optimal parameters observed in the previous simulations

and maintained for the whole optimization process. In the optimization, each well is only allowed

to move and be optimized within its own quadrant, and the field oil production total for each

39

simulation is used in understanding the optimization results. The results obtained from the

optimization results are presented in a layer map of a reservoir.

Fig 42: Well placement vs FOPT optimization

The image above shows the 4 different quadrants that the reservoir was divided to, A, B, C, and

D. It also shows the different locations that were tested during the optimization process. The aim

of the optimization is to indicate the regions with the most likely chance of better oil recovery in

the reservoir. As it can be seen in the image above, in the first quadrant, it would be advisable

to place the first well around the edge of the reservoir, the north-west region, as shown in the

figure 43a below.

Fig 43: Well optimization regions

In the second quadrant, B, the best region for the well placement for better oil recovery would

be the upper North east region of the reservoir as seen in figure 43b above. In the third

quadrant, C, the best region for the placement of the well is the south-south region of the

40

reservoir as shown in figure 43c. The best region for well placement in the fourth quadrant is the

south-east region of the reservoir. The conclusions made from these results were also

observable in the simulation with the best result of about 10.2 million sm3. This was obtained

with the iteration 95 with coordinates (1, 18) in quadrant A, (21, 21) in quadrant B, (9, 1) in

quadrant C, and (21, 4) in quadrant D. Studying the entire reservoir as a whole, these

conclusions can be better explained also with the porosity and permeability images. The regions

around the edges with the best porosity and permeability values also coincides with the regions

good for well placements as shown in figure 15 and 16. This would imply that there is ease of

flow of the oil and also larger storage of oil in those regions.

3.3.5.4 CO2 capture

One of the task of this thesis was also to help take care of the CO2 produced. In the work

previously done by Pedro Pinto (2014), all the gas produced was reinjected all the gas produced

into the reservoir thereby avoiding the separation of this CO2 from the gas being produced

because of the percentage composition which is about 80 percent molar content. I know there

are several processes available for the removal of this CO2 irrespective of the high molar

content that is observed in this field. The major processes available can be grouped as follows

(Maddox, 1982);

Absorption Processes (Chemical and Physical absorption)

Adsorption Process (Solid Surface)

Physical Separation (Membrane, Cryogenic Separation)

Hybrid Solution (Mixed Physical and Chemical Solvent)

The selection of a suitable process will depend on several factors. Each of the processes has its

own advantages relative to the others, therefore in selection of a suitable process, the following

factors should be put into consideration:

Type and concentration of impurities in the feed gas

The concentration of each contaminants and degree of removal required.

Hydrocarbon composition of the gas

Final specification

Capital and operating cost

Volume of gas to be processed

Selectivity required for acid gas removal

Conditions at which the feed gas is available for processing.

Delving into this area would make us digress from the core scope of this work, but the table

below showing the advantages and disadvantages of each process will help us have an idea on

which process could be used for this situation without really taking into consideration all the

possible factors.

41

Table 8: Overall comparisons of CO2 removal processes

Process Advantages Disadvantages

Absorption Widely used technology for

efficient (50-100) % removal of

acid gases (CO2 and H2S).

Not economical as high partial pressure is

needed while using physical solvents.

Long time requirement for purifying acid gas as

low partial pressure is needed while using

chemical solvents

Adsorption High purity of products can be

achieved.

Ease of adsorbent relocation to

remote fields when equipment

size becomes a concern.

Recovery of products is lower

Relatively single pure product

Membrane Simplicity, versatility, low capital

investment and operation.

Stability at high pressure

High recovery of products

Good weight and space

efficiency

Less environmental impact

Recompression of permeate

Moderate purity

Cryogenic Relatively higher recovery

compared to other process

Relatively high purity products

Highly energy intensive for regeneration

Not economical to scale down to very small

size.

Unease of operation under different

feed stream as it consists of highly

integrated, enclosed system

3.3.5.4.1 Fluor process

For our peculiar case, a highly recommended method for the removal of the CO2 with this high

amount is the Fluor process which is an example of the physical absorption process, helped by

the high partial pressure observed. The Fluor solvent process is one of the most attractive

processes for gas treating when the feed gas CO2 partial pressure is high (> 60 psia), or where

the sour feed gas is primarily CO2. The process is based on the physical solvent propylene

carbonate (FLUORTM) for the removal of CO2. Propylene carbonate (C4H6O3), is a polar solvent

with high affinity for CO2 and αij values of C1 or C2 to CO2 are high, therefore hydrocarbon

pickup in the rich solvent and subsequent hydrocarbon losses in the CO2 vent stream are

minimal. Earlier the FLUOR solvent process were configured to treat very narrow range of feed

gas compositions. Recently new configurations have been developed for treating high CO2

content sour gas. (Salako, 2005).

The feed gas pressure in this case varies from 400 – 1200 psig with the CO2 content varying

from 30-80 % and more. High CO2 content in the feed gas increases the amount of refrigeration

42

produced by the flash regeneration of the rich solvent. At very high CO2 partial pressures, the

cooling effect from flash regeneration will exceed the cooling required for CO2 absorption. Also

the viscosity and surface tension of propylene carbonate increases dramatically and the

absorber mass transfer rate drops drastically. This negatively impact the process, therefore

overcooling of the solvent should be avoided. The excess refrigeration is harnessed in this

application by lowering the absorption column temperature with refrigeration generated from

flashing the rich solvent from high to medium pressure. This allows the absorber to operate at a

lower temperature and increases the solvent loading. The flashed gasses are compressed and

recycled to reduce hydrocarbon losses in the CO2 vent. Excess refrigeration generated by

flashing of the rich solvent flowing to the first stage flash drum is used to cool and condense the

CO2 vent stream from the atmospheric and vacuum flashes. The condensed CO2 can be used

for EOR or disposed of by injecting the liquid into an underground formation. (Salako, 2005)

ADVANTAGES OF FLUOR PROCESS

FLOUR process requires no stringent need for solvent regeneration.

The FLUOR solvent has high CO2 solubility and enhances CO2 loading.

It requires no makeup water.

The operation is simple and a dry gas as output product.

Since propylene carbonate freezes at -57o F, winterization modification is minimal.

Modification for increasing CO2 in the feed is low.

DISADVANTAGES OF FLUOR PROCESS

Solvent circulation for the FLUOR solvent process is high.

The FLUOR solvent is very expensive ( SPE 14057)

The solvent have high affinity to heavy hydrocarbon which will be removed with CO2

and essentially results to hydrocarbon losses

43

4. Conclusions

The aim of this work is to optimize the oil recovery of the reservoir under study, through a

multidisciplinary approach that includes not only reservoir modelling, reservoir engineering but

also a glimpse of chemical engineering. An original reservoir from the Brazilian pre-salt was

modelled to form the CERENA-I static model, to test the reservoir performance and production

strategies. Reservoir conditions were borrowed from a real analogue pre/salt field close-by and

the equation of state was tuned to match an estimated bubble point and dew point.

Due to lack of computational memory, a sectorial model was carved out of the original reservoir

and also due to amount of iterations that would be needed for the optimization process in terms

of the computational time, the sectorial model was further up-scaled. This was an attempt to

produce a sectorial model with a smaller amount of cells but retaining the same variability in

terms of its porosity, permeability and also faces distribution. From this point onwards, the

upscaled sectorial model was used for the optimization process.

From the results obtained we can see the effect of the bottom-hole pressure in determining the

inter-relationship between the oil and gas produced. It can be concluded that for maximum oil

recovery, it would be advisable to maintain the same bottom-hole pressure for the 4 production

well rather than varying them. We could also observe that the oil production increases as the

bhp increases till it got to about 454 bars and consequently decreases despite the increase in

the bhp. A trend walk towards the optimal bhp shows a gradual decrease in the amount of gas

being produced, which shows the importance of maintaining the bhp for as long as possible to

improve oil recovery before the oil converts to the gaseous phase.

The other part of this work was the optimization of the injection rate in order to maintain the

reservoir pressure for as long as possible for better oil recovery. This was carried out by the

simultaneous injection of gas and water. The injection rate and the WAG ratio were optimized

for this work. Initially, we attempted to optimize the WAG ratio for each injection rate that was

tested, but we discovered a similar trend as we increased the injection rate and since this kept

leading to a further increase in oil production, we reversed the idea by actually selecting a

common WAG ratio to all the injection rate we have tested and then tried to optimize the

injection rate. The optimal WAG ratio selected from the available result was the ratio 2:3, then

an optimization of the injection rate at this WAG ratio was conducted. The result as shown on

the plot obtained indicates that the ideal injection rate would be the injection of water at about

40,000 sm3/day while the gas would be at about 60,000 sm3/day. At this rate we were able to

recover about 10,200,000 sm3 of oil from a possible 15 million sm3 of oil in place in the

reservoir, which is about 68% oil recovery.

We also considered how the placement of the wells affects the oil production of oil. There were

better oil recovery observed around the north-east and north-west of the upper region and the

south-south and south-east of the reservoir. This regions are characterized by much high

44

porosity than the other part of the reservoir which indicates possible oil storage in this region

and also high permeability values which indicates ease of movement of the oil into the wellhead.

The final part was a discussion on the possible processes available to remove the CO2 in the

gas produced so that it would be possible to sell as LNG due to the high amount of gas being

produced. Based on the available information we have, due to the high partial pressure and high

CO2 content in the gas, the physical absorption process named the FLOUR process, would be

advisable. This process was initially designed for low CO2 feeds but a high feed solution has

been developed to cater for feeds with high CO2 content like ours. The only limitation would be

the willingness to invest in this process depending on the cost estimates compared to the profits

that would be made compared to just reinjecting all the gas produced back into the reservoir like

it was done by Pedro Pinto (Pinto, 2014).

For the application of this idea into the real reservoir production, a number of both engineering

and financial decisions has to be made. Further studies, estimations and proper planning has to

be carried out for this concept to be implemented.

45

5. Future Work

One of the ideas that we have learnt in the past few years is the fact that the models we

generate are never perfect, so as long as the models can be improved upon, we can also

improve on the work that has to be done. The upscaled model would definitely need to be

improved upon and it would be interesting to see the possibility of optimizing the production of

the reservoir without necessarily upscaling the reservoir.

One of the interesting aspect of this work is the water-alternating gas injection scheme carried

out. The study was based on macro-study of the effect, but it would be interesting to have a

micro-study of the injection scheme, its direction in the reservoir, and the possibility of adding

some surfactants into the water being injected which could also improve the oil recovery. It

would also be interesting testing other form of water alternating gas injection scheme like the

Hybrid Water Alternating Gas injection scheme or the Foam assisted Water Alternating Gas

injection scheme.

The fluid composition studies can also be better done if more reliable data about the fluid can be

obtained to better understand how the fluids interact in the reservoir and could be used in their

separation and also in possible surfactants introduction which I believe could also help.

A further work on the actual separation of the CO2 from the gas through the physical sorption

method, Fluor Process, could be fully studied in collaboration with the chemical engineering

department.

Other optimization studies can be carried out on this work such as the optimization of the well

bores, the timing of the injections, and even the cost optimization. Other optimization technique

could also be used to compare the effectiveness of the techniques in obtaining the best optimal

results.

46

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47

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49

7. Appendices 7.1 Eclipse 300 data file

--********************************************************

RUNSPEC

--project name

TITLE

CERENA 1

COMPS

6 /

REGDIMS

1 1 0 1 0 5 5 /

--grid dimension

DIMENS

22 22 154 /

--Phases

OIL

WATER

GAS

--CO2SOL

METRIC

WELLDIMS

-- #wells #connects #groups #in_grp Stages per separator #Well streams #Mixtures

#separators #items in mixture

5 1* 2 4 5 1 1 1 1 /

/

HWELLS

--starting date of the project

START

-- DAY MONTH YEAR

1 JAN 2014 /

--grid definition

50

GRID

INCLUDE

'actnum.GRDECL' /

INCLUDE

'grid.GRDECL'/

INCLUDE

'poro2.GRDECL' /

INCLUDE

'permx.GRDECL' /

INCLUDE

'permy.GRDECL'/

INCLUDE

'permz.GRDECL' /

RPTGRID

'ALLNNC' 'DX' 'DY' 'DZ' 'PERMX' 'PERMY' 'PERMZ' 'PORO' /

GRIDFILE

0 1 /

--*******************************************

INIT

PROPS

INCLUDE

'ZMFVD.PVO' /

INCLUDE

'CRUDE.PVO' /

INCLUDE

'scal.inc' /

DENSITY

--

-- Fluid Densities at Surface Conditions

--

1* 1040.0000 1*/

51

SOLUTION

-- DATUM DATUM OWC OWC GOC GOC

-- DEPTH PRESS DEPTH PCOW DEPTH PCOG

EQUIL

-- 1 2 3 4 5 6 9 10 11

209.999815687411 492.9964 300.00 0.00 209.999815687411 0.00 1* 1* 0 3 1/

-- SWITCH ON OUTPUT OF INITIAL SOLUTION

RPTSOL

FIP=1 PRES SOIL SWAT /

RPTRST

SGAS

SOIL

SWAT

PRES

BO

BOIL

DENG

DENO

PSAT

RS

XMF

YMF

TOTCOMP

/

--*************************************************************************

SUMMARY

--*************************************************************************

FPRP

FOVPR

FOVPT

52

--Oil

FOPR

FOPT

FOMR

FOMT

FODN

--Gas

FGPR

FGMR

FGMT

FGDN

FGIR

FGIT

FGPT

--Wells

WPI

/

WBHP

/

WWCT

/

WOPR

/

WWPR

/

WGPR

/

FGPRB

FGOR

53

FWIR

FWIT

FWPT

FWCT

FXMF

1 /

FXMF

2 /

FXMF

3 /

FXMF

4 /

FXMF

5 /

FXMF

6 /

FYMF

1 /

FYMF

2 /

FYMF

3 /

FYMF

4 /

FYMF

5 /

FYMF

6 /

FCMPR

54

1 /

FCMPR

2 /

FCMPR

3 /

FCMPR

4 /

FCMPR

5 /

FCMPR

6 /

FCMPT

1 /

FCMPT

2 /

FCMPT

3 /

FCMPT

4 /

FCMPT

5 /

FCMPT

6 /

FCMIR

1 /

FCMIR

55

2 /

FCMIR

3 /

FCMIR

4 /

FCMIR

5 /

FCMIR

6 /

FCMIT

1 /

FCMIT

2 /

FCMIT

3 /

FCMIT

4 /

FCMIT

5 /

FCMIT

6 /

FCHMR

1 /

FCHMR

2 /

FCHMR

3 /

FCHMR

4 /

56

FCHMR

5 /

FCHMR

6 /

FCHMT

1 /

FCHMT

2 /

FCHMT

3 /

FCHMT

4 /

FCHMT

5 /

FCHMT

6 /

FHMPR

FHMPT

FOPRA

FOPRB

FOPTA

FOPTB

FOVPR

FOVPT

FGVPR

FGVPT

57

FOIP

RUNSUM

EXCEL

--**********************************************************

-- THE SCHEDULE SECTION DEFINES THE OPERATIONS TO BE SIMULATED

--**********************************************************

SCHEDULE

TUNING

/

/

--24 1 50 7* /

2* 80 /

INCLUDE

'wells2.inc' /

WCONPROD

PROD1 OPEN BHP 5* 454.28076424967645 /

PROD2 OPEN BHP 5* 454.28076424967645 /

PROD3 OPEN BHP 5* 454.28076424967645 /

PROD4 OPEN BHP 5* 454.28076424967645 /

/

INCLUDE

'injeccao.inc' /

DATES

1 FEB 2014 /

--1 MAR 2014 /

1 APR 2014 /

58

--1 MAY 2014 /

1 JUN 2014 /

--1 JLY 2014 /

1 AUG 2014 /

--1 SEP 2014 /

1 OCT 2014 /

--1 NOV 2014 /

1 JAN 2015 /

/

WELSPECS

PROD1 PROD 4 4 85 OIL 1* STD /

PROD2 PROD 4 19 85 OIL 1* STD/

PROD3 PROD 17 4 85 OIL 1* STD/

PROD4 PROD 17 19 85 OIL 1* STD/

INJ1 I 10 10 120 WATER 1* STD /

/

COMPDAT

--Nome I J Kup Klow Open/shut 2* Well bore 3* Direcção do poço

PROD1 4 4 35 70 SHUT 2* 0.2 3* Z /

PROD1 4 4 80 150 OPEN 2* 0.2 3* Z /

PROD2 4 19 35 70 SHUT 2* 0.2 3* Z /

PROD2 4 19 80 150 OPEN 2* 0.2 3* Z /

PROD3 17 4 35 70 SHUT 2* 0.2 3* Z /

PROD3 17 4 80 150 OPEN 2* 0.2 3* Z /

PROD4 17 19 35 70 SHUT 2* 0.2 3* Z /

PROD4 17 19 80 150 OPEN 2* 0.2 3* Z /

59

INJ1 10 10 35 70 OPEN 2* 0.2 3* Z /

INJ1 10 10 120 150 OPEN 2* 0.2 3* Z /

/

WCONINJE

--Nome Tipo OPEN/SHUT Controlo

INJ1 MULTI OPEN RATE 40214.23029019803 7* 0.30239965572528305

0.45519459870107587 /

/

CECON

PROD1 4* 0.9 2* CON /

PROD2 4* 0.9 2* CON /

PROD3 4* 0.9 2* CON /

PROD4 4* 0.9 2* CON /

/

DATES

1 FEB 2015 /

1 MAR 2015 /

1 APR 2015 /

1 MAY 2015 /

1 JUN 2015 /

1 JLY 2015 /

1 AUG 2015 /

1 DEC 2015 /

1 FEV 2016 /

1 MAR 2016 /

1 APR 2016 /

1 MAY 2016 /

1 DEC 2016 /

/

DATES

1 MAY 2017 /

60

1 DEC 2017 /

1 MAY 2018 /

1 DEC 2018 /

1 MAY 2019 /

1 DEC 2019 /

1 MAY 2020 /

1 DEC 2020 /

1 MAY 2021 /

1 DEC 2021 /

1 MAY 2022 /

1 DEC 2022 /

1 MAY 2023 /

1 DEC 2023 /

1 MAY 2024 /

1 DEC 2024 /

1 MAY 2025 /

1 DEC 2025 /

1 MAY 2026 /

1 DEC 2026 /

1 MAY 2027 /

1 DEC 2027 /

1 MAY 2028 /

1 DEC 2028 /

1 MAY 2029 /

1 DEC 2030 /

/

END

7.2 Include file "crude.PVO"

ECHO

-- Units: C

61

RTEMP

--

-- Constant Reservoir Temperature

--

100

/

EOS

--

-- Equation of State (Reservoir EoS)

--

PR3

/

NCOMPS

--

-- Number of Components

--

6

/

PRCORR

--

-- Modified Peng-Robinson EoS

--

CNAMES

--

-- Component Names

--

'CO2'

'C1'

'C2'

'C3'

'C4-6'

62

'C7+'

/

MW

--

-- Molecular Weights (Reservoir EoS)

--

44.01

16.043

30.03651882

44.097

70.2371498

218

/

OMEGAA

--

-- EoS Omega-a Coefficient (Reservoir EoS)

--

0.457235529

0.457235529

0.457235529

0.457235529

0.457235529

0.457235529

/

OMEGAB

--

-- EoS Omega-b Coefficient (Reservoir EoS)

--

0.077796074

0.077796074

0.077796074

63

0.077796074

0.077796074

0.077796074

/

-- Units: K

TCRIT

--

-- Critical Temperatures (Reservoir EoS)

--

250.91111609

156.9532596

249.1099632

304.51896827

380.6181688

613.4226548

/

-- Units: bar

PCRIT

--

-- Critical Pressures (Reservoir EoS)

--

119.1938758

74.29588135

78.41736061

68.83487205

56.82317979

27.54568139

/

-- Units: m3 /kg-mole

VCRIT

64

--

-- Critical Volumes (Reservoir EoS)

--

0.09400075621

0.09800017929

0.1470534926

0.1999979608

0.3014148438

0.8499119437

/

ZCRIT

--

-- Critical Z-Factors (Reservoir EoS)

--

0.537080892975933

0.557950389352133

0.55676353303698

0.543743455312942

0.541221410764183

0.459030936952775

/

SSHIFT

--

-- EoS Volume Shift (Reservoir EoS)

--

-0.0453788736841816

-0.182901686008519

-0.126011095016238

-0.105436919693872

0.347695418004011

-0.754778251842114

65

/

ACF

--

-- Acentric Factors (Reservoir EoS)

--

0.225

0.013

0.09764618515

0.1524

0.224434502

0.70397

/

BIC

--

-- Binary Interaction Coefficients (Reservoir EoS)

--

0.1

0.09817700916 0.003701359105

0.1 0.006214 0.002738293998

0.1 0.01482207968 0.007750716921 0.002218453386

0.1 0.047496 0.03439155849 0.023801 0.0131823004

/

PARACHOR

--

-- Component Parachors

--

78

77

106.9094608

150.3

66

225.6184829

564.40006

/

-- Units: m3 /kg-mole

VCRITVIS

--

-- Critical Volumes for Viscosity Calc (Reservoir EoS)

--

0.09400075621

0.09800017929

0.1470534926

0.1999979608

0.3014148438

0.8499119437

/

ZCRITVIS

--

-- Critical Z-Factors for Viscosity Calculation (Reservoir EoS)

--

0.537080892975933

0.557950389352133

0.55676353303698

0.543743455312942

0.541221410764183

0.459030936952775

/

LBCCOEF

--

-- Lorentz-Bray-Clark Viscosity Correlation Coefficients

67

--

0.1023 0.023364 0.058533 -0.040758 0.0093324

/

--PVTi--Please do not alter these lines

--PVTi--as PVTi can use them to re-create the fluid model

--PVTiMODSPEC

========================================================

--PVTiTITLE

--PVTiModified System: From Automatically created during keyword export

--PVTiVERSION

--PVTi 2006.1 /

--PVTiNCOMPS

--PVTi 6 /

--PVTiEOS

--PVTi PR3 /

--PVTiPRCORR

--PVTiLBC

--PVTiOPTIONS

--PVTi 0 0 1 2 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0

--PVTi/

--PVTiECHO

--PVTiMODSYS ========================================================

--PVTiUNITS

--PVTi METRIC ABSOL PERCENT /

--PVTiDEGREES

--PVTi Centigrade /

--PVTiSTCOND

--PVTi 15.5556 1.0132 /

--PVTiCNAMES

--PVTi CO2

--PVTi C1

68

--PVTi X1+

--PVTi C3

--PVTi 4

--PVTi C7+

--PVTi /

--PVTiTCRIT

--PVTi -2.223888391E+01 -1.161967404E+02 -2.404003680E+01 3.136896827E+01

--PVTi 1.074681688E+02 3.402726548E+02 /

--PVTiPCRIT

--PVTi 1.191938758E+02 7.429588135E+01 7.841736061E+01 6.883487205E+01

--PVTi 5.682317979E+01 2.754568139E+01 /

--PVTiVCRIT

--PVTi 9.400075621E-02 9.800017929E-02 1.470534926E-01 1.999979608E-01

--PVTi 3.014148438E-01 8.499119437E-01 /

--PVTiZCRIT

--PVTi 5.370808930E-01 5.579503894E-01 5.567635330E-01 5.437434553E-01

--PVTi 5.412214108E-01 4.590309370E-01 /

--PVTiVCRITVIS

--PVTi 9.400075621E-02 9.800017929E-02 1.470534926E-01 1.999979608E-01

--PVTi 3.014148438E-01 8.499119437E-01 /

--PVTiZCRITVIS

--PVTi 5.370808930E-01 5.579503894E-01 5.567635330E-01 5.437434553E-01

--PVTi 5.412214108E-01 4.590309370E-01 /

--PVTiSSHIFT

--PVTi -4.273033674E-02 -1.442656189E-01 -1.037251741E-01 -7.750138148E-02

--PVTi -4.300117637E-02 -6.797330077E-01 /

--PVTiACF

--PVTi 2.250000000E-01 1.300000000E-02 9.764618515E-02 1.524000000E-01

--PVTi 2.244345020E-01 7.039700000E-01 /

--PVTiMW

--PVTi 4.401000000E+01 1.604300000E+01 3.003651882E+01 4.409700000E+01

69

--PVTi 7.023714980E+01 2.180000000E+02 /

--PVTiZI

--PVTi 5.497015024E+01 1.657320235E+01 4.467084702E+00 3.158315227E+00

--PVTi 5.703144762E+00 1.512810272E+01 /

--PVTiTBOIL

--PVTi -7.845000040E+01 -1.614500026E+02 -9.039323562E+01 -4.204999388E+01

--PVTi 2.777470515E+01 2.937459706E+02 /

--PVTiTREF

--PVTi 1.985000221E+01 -1.614500026E+02 -9.185742700E+01 -4.214999944E+01

--PVTi 1.846992249E+01 1.555556876E+01 /

--PVTiDREF

--PVTi 7.769999569E+02 4.250000441E+02 5.521668752E+02 5.820000234E+02

--PVTi 6.251027808E+02 8.515000568E+02 /

--PVTiPARACHOR

--PVTi 7.800000000E+01 7.700000000E+01 1.069094608E+02 1.503000000E+02

--PVTi 2.256184829E+02 5.644000600E+02 /

--PVTiHYDRO

--PVTi N H H H H H

--PVTi /

--PVTiTHERMX

--PVTi 0.0005000 /

--PVTiACHEUH

--PVTi 0.1500 /

--PVTiBIC

--PVTi 1.000000000E-01

--PVTi 9.817700916E-02 3.701359105E-03

--PVTi 1.000000000E-01 6.214000000E-03 2.738293998E-03

--PVTi 1.000000000E-01 1.482207968E-02 7.750716921E-03 2.218453386E-03

--PVTi 1.000000000E-01 4.749600000E-02 3.439155849E-02 2.380100000E-02

--PVTi 1.318230040E-02

--PVTi /

70

--PVTiSAMPLES

--PVTiGAS

--PVTi 6.504084843E+01 2.202573113E+01 4.654985432E+00 2.738703724E+00

--PVTi 3.663585736E+00 1.876165208E+00 /

--PVTi /

--PVTiSAMTITLE

--PVTi GAS 'Split-Off Vapour of Experiment 1 : BUBBLE' /

--PVTi /

--PVTiSPECHA

--PVTi 1.979999924E+01 0.000000000E+00 5.070193224E-01 1.925000000E+01

--PVTi -2.688967445E+00 4.936753433E+00 /

--PVTiSPECHB

--PVTi 7.344000041E-02 0.000000000E+00 -2.208748796E-04 5.212000012E-02

--PVTi 4.104582117E-01 6.789752385E-01 /

--PVTiSPECHC

--PVTi -5.601999874E-05 0.000000000E+00 4.362156719E-07 1.196999983E-05

--PVTi -2.049610167E-04 -1.517181433E-04 /

--PVTiSPECHD

--PVTi 1.715000053E-08 0.000000000E+00 -1.901119036E-10 -1.132000005E-08

--PVTi 3.701855583E-08 0.000000000E+00 /

--PVTiHEATVAPS

--PVTi 0.000000000E+00 0.000000000E+00 0.000000000E+00 0.000000000E+00

--PVTi 0.000000000E+00 7.166216411E+04 /

--PVTiCALVAL

--PVTi 0.000000000E+00 3.749000000E+03 4.411015259E+03 8.130000000E+02

--PVTi 2.799151394E+03 8.811000000E+03 /

--PVTi--End of PVTi generated section--

ZI

--

-- Overall Composition

--

71

0.5497015024

0.1657320235

0.04467084702

0.03158315227

0.05703144762

0.1512810272

/

7.3 Include file "scal.inc"

-- RELATIVE PERMEABILITY AND CAPPILARY PRESSURE CURVES

SWFN

0.2 0.0 0.0

0.3 0.00024 0.0

0.4 0.0039 0.0

0.5 0.02 0.0

0.6 0.062 0.0

0.7 0.152 0.0

0.8 0.316 0.0

0.9 0.585 0.0

1.0 1.0 0.0

/

SOF3

--So Kro (oil water regions) Kro (oil, gas and connate water)

0.1 0.0 0.0

0.2 0.018 0.0

0.3 0.073 0.025

0.4 0.165 0.1

0.5 0.294 0.225

0.6 0.459 0.4

72

0.7 0.661 0.625

0.8 0.9 0.9

/

SGFN

--Sg Krg Pcog (Gas-Oil Capillary pressure)

0.0 0.0 0.0

0.1 0.0 0.0

0.2 0.018 0.0

0.3 0.073 0.0

0.4 0.165 0.0

0.5 0.294 0.0

0.6 0.459 0.0

0.7 0.661 0.0

0.8 0.9 0.0

/

PVTW

234.46 1.0042 5.43E-05 0.5 1.11E-04 /

-- ROCK COMPRESSIBILITY

--

-- REF. PRES COMPRESSIBILITY

ROCK

235 0.00045 /

-- SWITCH OFF OUTPUT OF ALL PROPS DATA

STONE1

7.4 Include file "wells2.inc"

WELSPECS

73

PROD1 PROD 4 4 35 OIL 1* STD/

PROD2 PROD 4 19 35 OIL 1* STD/

PROD3 PROD 17 4 35 OIL 1* STD/

PROD4 PROD 17 19 35 OIL 1* STD/

INJ1 I 10 10 120 GAS 1* STD/

/

COMPORD

-- Nome Método de ordenação

PROD1 INPUT /

PROD2 INPUT /

PROD3 INPUT /

PROD4 INPUT /

INJ1 INPUT /

/

COMPDAT

--Nome I J Kup Klow Open/shut 2* Well bore 3* Direcção do poço

PROD1 4 4 35 70 OPEN 2* 0.2 3* Z /

PROD2 4 19 35 70 OPEN 2* 0.2 3* Z /

PROD3 17 4 35 70 OPEN 2* 0.2 3* Z /

PROD4 17 19 35 70 OPEN 2* 0.2 3* Z /

INJ1 17 19 35 70 OPEN 2* 0.2 3* Z /

INJ1 10 10 120 150 OPEN 2* 0.2 3* Z /

/

7.5 Include file "injeccao.inc"

WCONINJE

--Nome Tipo OPEN/SHUT Controlo

INJ1 GAS OPEN RATE 20000 /

/

74

--WELLSTRE

--Nome da stream %.1 %2 %3 %4 %5 %6 %7 %8 %9 %10 %11

-- ‘CO2’ 1 0 0 0 0 0 0 0 0 0 0 /

--/

WINJGAS

--Nome Tipo de fluido injectado Nome do grupo

INJ1 GV FIELD /

/