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1 Optimization of the Water Alternating Gas Injection Compositional fluid flow simulation with Water Alternating Gas Injection optimization on the upscaled synthetic reservoir CERENA-I Fabusuyi, Oluwatosin John; Quintao, Maria Joao; Azevedo, Leonardo; Soares, Amílcar Email addresses: [email protected]; [email protected]; [email protected]; [email protected] Centre for Petroleum Reservoir Modelling Instituto Superior Técnico Avenida Rovisco Pais, 1 1049-001 Lisboa Abstract- This work focuses on the optimization of the production strategy on an up-scaled synthetic reservoir CERENA-I, which mimics some characteristics of a Brazilian Pre-Salt field. This reservoir has a saturated oil leg with a retrograde condensation gas cap, both with a high CO2 content. The production strategy involved the implementation of a simultaneous- water alternating gas injection scheme (SWAG). The objectives for this study were to increase the oil recovery while reducing the gas production and the parameters selected for optimization in this study were the bottom-hole pressure, the well position, the injection rate and WAG ratio. The effects of these variables were studied in order to achieve an optimal solution. Keywords: Reservoir simulation, compositional simulation, PVT analysis, Synthetic reservoir, Simultaneous WAG scheme, Particle Swarm Optimization, objective function. 1. Introduction This study is a continued interest in the CERENA-I reservoir created by Pedro Pinto [10]. from the Brazilian Pre-Salt play (figure 1), which has a very high content of CO2. This Brazilian Pre-salt reservoir poses great challenges in every aspect of its production, from reservoir modelling and management, to surface facilities. The reservoir covers an area of 567 km 2 about 300km offshore of Rio de Janeiro, in the Santos basin. Fig 1: The Brazilian Pre-Salt Play (Source: ANP) 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 18 o API and 55% (molar) of CO2 content. It also has a gas cap of retrograde condensation gas which contains approximately 60% (molar) of CO2. The idea to maximize the production of oil in the reservoir led to the development of a single well Simultaneous Water Alternating Gas (SWAG) injection scheme on the reservoir instead of the initial injection of water and gas produced in 2 different wells. SWAG is an enhanced oil recovery process in which gas is mixed with water outside the well 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
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Optimization of the Water Alternating Gas Injection...Simultaneous Water Alternating Gas (SWAG) injection scheme on the reservoir instead of the initial injection of water and gas

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Page 1: Optimization of the Water Alternating Gas Injection...Simultaneous Water Alternating Gas (SWAG) injection scheme on the reservoir instead of the initial injection of water and gas

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Optimization of the Water Alternating Gas Injection

Compositional fluid flow simulation with Water Alternating Gas Injection optimization on the upscaled synthetic reservoir CERENA-I

Fabusuyi, Oluwatosin John; Quintao, Maria Joao; Azevedo, Leonardo; Soares, Amílcar

Email addresses: [email protected]; [email protected];

[email protected]; [email protected]

Centre for Petroleum Reservoir Modelling

Instituto Superior Técnico

Avenida Rovisco Pais, 1

1049-001 Lisboa

Abstract- This work focuses on the optimization

of the production strategy on an up-scaled

synthetic reservoir CERENA-I, which mimics

some characteristics of a Brazilian Pre-Salt field.

This reservoir has a saturated oil leg with a

retrograde condensation gas cap, both with a

high CO2 content. The production strategy

involved the implementation of a simultaneous-

water alternating gas injection scheme (SWAG).

The objectives for this study were to increase the

oil recovery while reducing the gas production

and the parameters selected for optimization in

this study were the bottom-hole pressure, the

well position, the injection rate and WAG ratio.

The effects of these variables were studied in

order to achieve an optimal solution.

Keywords: Reservoir simulation, compositional

simulation, PVT analysis, Synthetic reservoir,

Simultaneous WAG scheme, Particle Swarm

Optimization, objective function.

1. Introduction

This study is a continued interest in the

CERENA-I reservoir created by Pedro Pinto [10].

from the Brazilian Pre-Salt play (figure 1), which

has a very high content of CO2. This Brazilian

Pre-salt reservoir poses great challenges in

every aspect of its production, from reservoir

modelling and management, to surface facilities.

The reservoir covers an area of 567 km2 about

300km offshore of Rio de Janeiro, in the Santos

basin.

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

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.

The idea to maximize the production of oil in the

reservoir led to the development of a single well

Simultaneous Water Alternating Gas (SWAG)

injection scheme on the reservoir instead of the

initial injection of water and gas produced in 2

different wells. SWAG is an enhanced oil

recovery process in which gas is mixed with

water outside the well 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

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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 [6].

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 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. The optimization technique used

in this study was the particle swarm optimization

which was used in the Raven software provided

by the Epistemy Company

The initial objective of this research work was to

find a production strategy to optimize oil

production and reduce the quantity of CO2 being

produced, and as the researched progressed,

different ideas were introduced. The different

parameters to be optimized were introduced and

discussed, these include parameters related to

the production wells and others related to the

injection wells. During the course of the thesis,

due to computational constraints for the

simulation and optimization procedure, the

reservoir CERENA-I was up-scaled, and the up-

scaled version was used henceforth.

2. The synthetic reservoir: CERENA-I The CERENA-I model was created to replicate

some characteristics of the Brazilian Pre-salt

carbonate fields and it contains high-resolution

data sets of petro-physical and petro-elastic

properties. For the case study presented herein

only the sets of porosity and permeability were

used. The model is composed of two facies: a

reservoir facies, composed by microbiolites; and

a non-reservoir facies composed by mudstones,

on a corner-point grid with 161x161x300 cells,

with 25x25x1m spacing.

Fig 2: CERENA-I porosity model

Fig 3: 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 [4].

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3. Dynamic simulation

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's®

library, grouped to reduce computation time and

memory requirements, and with the molar

percentages re-adjusted to the known CO2

content of the analogue field (Table 1).

Table 1: 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

For this case study, the three parameter Peng-

Robinson equation of state was chosen, and

tuned to match the estimated PVT observations

(Table 2).

Table 2: Estimated saturation pressures

Bubble point (bar) Dew point (bar)

493 400 Due to the huge number of cells in the reservoir,

the choice was made to run the simulation on a

fine grid sectoral model (figure 4) which, despite

being considerably smaller, when compared to

the original model, reproduces the total variability

of the full field.

Fig 4: Sectorial model area

Despite doing this, the computational time and

memory needed for the number of iterations

needed during the optimization process was

really enormous, hence the sectorial reservoir

was up-scaled. The porosity and permeability

distribution in the up-scaled reservoir were made

to replicate the distribution in the original

sectorial model. The new up-scaled 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.

Fig 5: Up-scaled perm x and y (left), original perm x and y (right)

Fig 6: Up-scaled perm z (left), original perm z (right)

Fig 7: Up-scaled porosity (left), original porosity (right)

Figures 5 to 7 show the visual differences

between the up-scaled and original sectorial

models of the permeability and porosity models.

The trends, facies and distributions obtained in

the original sectorial model can also be observed

in the up-scaled model with variations. From this

point on, the link to the original full field model

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and the sectorial model is severed and the study

object is now the up-scaled sectorial model. For

this reason no boundary effects will be added to

the dynamic model, to account for the influence

of the remaining area.

Fig 8: Well Locations

The well pattern chosen for this study was a

traditional five-spot configuration with four vertical

producer wells in the corners and one vertical

injector well in the center (Figure 8).

Table 3: Fluids originally in place

The model was initialized and the fluids in place

(Table 3) were calculated for the equilibrium

conditions.

We first chose to produce the gas cap, to

access its liquid condensate fraction. The fluid

was condensed in surface separators and the

resulting dry gas was re-injected back into the

gas cap, to help keep reservoir pressure. The

gas cap was produced for one year, after which

the completions of the producer wells were

closed in this zone and opened in the oil leg.

4. Optimization results

The optimization technique selected for this study

was the particle swarm optimization technique.

This was implemented in a software called Raven

from the Epistemy Company. Four different

parameters were optimized during this study, the

bottom-hole pressure of the four production wells,

the injection rate, the WAG ratio and the well

position. The results obtained are shown below.

4.1 Production well Bottom-hole

pressure (bhp) Two strategies were considered for this

optimization, we considered having the same bhp

values for the 4 wells and we also considered

varying the bhp values for each well. This two

ideas were tested and optimized and the results

shown below.

Same Bottom-hole Pressure

These results were obtained after 255 iterations

of PSO-based algorithm optimization. It can be

observed that the suitable BHP for these wells

was obtained between 200 to 500 bars.

Fig 9: Same BHP Gas production opt

Reservoir volume of oil Reservoir volume of gas

1.5 x 107 sm3 1.46 x 1010sm3

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Fig 10: Same BHP Oil production opt

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 9

and 10.

Fig 11: FGPT vs FOPT for same BHP

The optimal bhp for these 4 wells to operate at

optimal condition would be at the peak pressure

of 454 bars. 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.

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 varied as

opposed to having the same value as shown

above. The bhp was 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 12: Different BHP Gas production opt

Fig 13: Different BHP oil production opt

The results obtained from this simulation did not

produce any useable results and took several

days and hours to obtain any recognizable

results. This can also be buttressed by the plot of

FOPT and FGPT shown below in figure 14.

4.2 Injection rate and WAG ratio When conducting a WAG scheme, one important

factor inter-twined with the injection rate is the

Water Alternating Gas ratio. This variable is

defined as the ratio of the volume of water

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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.

Fig 14: FGPT vs FOPT for same BHP

In this study, the WAG ratio can be explained by

the expression below,

WAG ratio = Volume of water injected: Volume of Gas

≡ Water Injection rate: Gas injection rate

≡ Water injection rate: (Water injection rate x 𝑘

𝑗)

≡ j: k

To start, a random WAG ratio was selected and

different injection rates were tested with this

WAG ratio, a certain trend was observed in all of

them with the best oil production being observed

when the WAG ratio favours a high injection of

gas over water but with a high volume of gas

produced along with it. The reverse is the case

when a high volume of water injection is favoured

over the gas injection: in this case, the oil

production decreases and the gas production

also decreases. The visible trend in the figure 15

shows the region that satisfies our objective

functions, where we are able to produce oil

maximally and gas minimally. A result from one

of the injection rates used is shown in figure 15,

where the injection rate selected was 7570

sm3/day. Some selected results from this

simulation are shown in figure 16.

Fig 15: WAG ratios at 7570

Fig 16: 7570 FOPT results

The different WAG ratios along the trend lines

were able to fully satisfy our objective functions

and one of them was selected and maintained for

the rest of this study. The WAG ratio that was

selected was the ratio 2:3. With this WAG ratio,

an optimization simulation was conducted to

determine the optimal injection rate for the

injection well in this production, and the results

obtained are shown in figure 17 and 18. We can

observe 3 different sections. The first section

corresponds to the region where the oil

production increases as the injection rates also

increase, 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

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production total is obtained observed at an

injection rate of 40,214 sm3/day. From this point

forward, no further difference is observed. If the

corresponding gas production total is observed

and compared to the oil production total. 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 46,000sm3/day. Since our initial aim was to

increase oil recovery and reduce gas production,

it was necessary to pick the injection rate that

best satisfies 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 17: Optimal FOPT results

Fig 18: Optimal FGPT results

4.3 Well’s positions The aim of this optimization simulation is to help

us make a better decision in the placement of our

wells in the reservoir to enhance better oil

recovery.

Fig 19: Reservoir Quadrants

The optimization was carried out by dividing the

reservoir into 4 quadrants with a well sited in

each quadrant and the assumption is that each

well will be sited in optimal locations in its

respective quadrant in order to maximize the total

oil recovery. The simulation was conducted with

the optimal parameters observed in the previous

simulations, 454 bars for the production bhp and

40,214sm3/day for the injection rate, 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

simulation is used in understanding the

optimization results. The results obtained from

the optimization results are presented in a layer

map of a reservoir. 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 better oil recovery

in the reservoir. As it can be seen in figure 20, 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 40a

below. 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 40b above. In the third

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quadrant, C, the best region for the placement of

the well is the south-south region of the reservoir

as shown in figure 40c. The best region for well

placement in the fourth quadrant is the south-

east region of the reservoir. The regions around

the edges with the best porosity and permeability

values also coincide with the regions good for

well placements as shown in figure 21 and 22.

This would imply that there is ease of flow and

also larger storage of oil in those regions.

The conclusions taken from these results were

also observable in the simulation with the best

result of about 10.2 million sm3.

Fig 20: Well placement vs FOPT optimization

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.

Fig 21: Well optimization regions

Fig 22: Suitable regions and porosity models

5. CO2 Capture One of the tasks 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 into the reservoir

thereby avoiding the separation of this CO2 from

the gas being produced because of the

percentage composition of the gas which is about

60 percent molar content of the CO2. There are

several processes available for the removal of

this CO2 irrespective of the high molar content

that is observed in this field. The best method for

this peculiar case in this study is the Fluor solvent

process. 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 was configured to treat very narrow

range of feed gas compositions. Recently new

configurations have been developed for treating

high CO2 content sour gas [11]. 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 produced

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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

impacts 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.

6. 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 up-

scaled 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 until it got to a

threshold of 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. Another 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 simultaneous injection of gas and water.

The injection rate and the WAG ratio were

optimized for this case being considered. 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

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rates we tested and then tried to optimize the

injection rate. The optimal WAG ratio selected

from the available result was the ratio 2:3. An

optimization of the injection rate at this WAG ratio

was conducted. The result, as shown on the plot

obtained (fig 17), indicates that the ideal injection

rate would be the injection of water at about

40,214sm3/day while the gas would be at about

60,000sm3/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 total oil production. We can observe

better oil recovery in the well locations with

corresponding configurations around the north-

east and north-west of the upper region and the

south-south and south-east of the reservoir.

These regions are characterized by much higher

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.

7. References

[1] Archer, J. S., & Wall, C. G. (1986). Petroleum

engineering: principles and practice. London:

Graham and Trotman Ltd.

[2] Christensen, J. R.; Stenby, E. H., Skauge,

(2001) A. Review of WAG field experience. SPE

Reservoir Evaluation & Engineering.

[3] Doghaish, N. M. (2008). Analysis of Enhanced

Oil Recovery-A Literature Review. Dalhousie

University. Halifax: unpublished work.

[4] Horta, A., & Soares, A. (2010). Direct

Sequential Co-Simulation with Joint Probability

Distributions. Mathematical Geosciences.

[5] Kansas Geological Survey (2004).

Sedimentologic and Diagenetic Characteristics of

the Arbuckle Group.

[6] Meshal, A., G. Rida and M. Adel, 2007. A

parametric Investigations of SWAG injection

technique. SPE paper # 105071 prepared to be

presented in 15th SPE Oil and Gas Show,

Bahrain 11-14th March

[7] Nezhad, S., Mojarad, M., Paitakhti, S.,

Moghadas, J., & Farahmand, D. (2006).

Experimental Study on Applicability of

Water.Alternating-CO2 injection in the Secondary

and Tertiary Recovery. First International Oil

Conference and Exhibition in Mexico (pp. 1-4).

Cancun: Society of Petroleum engineers

[8] Nocedal, J. and Wright, S.J.: “Numerical

Optimization”, Second Edition, Springer press,

2006.

[9] Onwunalu, J., Durlofsky, L., 2011. A new well-

pattern-optimization procedure for large-scale

field development. SPE Journal 16 (3), 594–607.

[10] Pedro Pinto (2013). Dynamic simulation on

the synthetic reservoir CERENA I; Compositional

fluid flow simulation with 4D seismic mitoring on a

reservoir with a large content of CO2.

[11] Salako Abiodun Ebenezer, 2005 Removal of

Carbondioxide from Natural gas for LNG

production. Semester Project Work, Institute of

Petroleum Technology, Norwegian University of

Science and Technology, Norway.

[12] Saleem Qadir Tunio, Tariq Ali Chandio and

Muhammad Khan Memon, 2012. Comparative

Study of FAWAG and SWAG as an Effective

EOR Technique for a Malaysian Field. Research

Journal of Applied Sciences, Engineering and

Technology 4(6): 645-648, 2012.