International Journal of Petroleum and Geoscience Engineering · 78 | P a g e International Journal...

78 | P a g e

International Journal of Petroleum and Geoscience Engineering

Volume 04, Issue 02, Pages 78-103, 2016 ISSN: 2289-4713

Production Optimization for One of Iranian Oil Reservoirs Using Non-

Linear Gradient Method

Mehdi Talebpour a, Mahdi Rastegarnia b and Ali Sanati c,*

a Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran. b Department of Petrophysics, Pars Petro Zagros Engineering and Services Company, Tehran, Iran.

c Faculty of Petrochemical and Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran.

* Corresponding author. Tel.:+985144012861;

E-mail address: [email protected]

A b s t r a c t

Keywords:

Optimization,

Objective Function,

Controlling Function.

Today optimization is the principal part of every engineering design such that it exists in

almost all engineering software products as a black box to optimize the simulated

parameters. Optimization methods in general are divided into two main groups: gradient

and non-gradient methods. In this work a non-linear non-gradient method was applied to

determine the reservoir optimized parameters such as production rate and bottom-hole

pressure. Also, maximizing the total production from reservoir regarding constraints like

gas oil ratio and water production was considered as the objective function. For this,

different scenarios with different well numbers were investigated to obtain the optimum

scenario. Moreover, sensitivity analysis is applied on different parameters like daily

production and bottom-hole pressure as the controlling parameters. Finally, cumulative

production was obtained from optimized production rate and bottom-hole pressure.

Accepted: 15 Jun 2016 © Academic Research Online Publisher. All rights reserved.

1. Introduction

Optimization methods generally are divided into

two main groups: gradient and non-gradient

methods. Non-gradient methods like simulated

annealing are used just for simple models.

Increasing the simulation parameters will increase

the optimization process’s run time. So, using

gradient methods in reservoir simulation is of great

importance. In this work a non-linear non-gradient

method was applied to determine the reservoir

optimized parameters such as production rate and

bottom-hole pressure and maximizing the total

production from reservoir regarding constraints like

gas oil ratio and water production, was considered

as the objective function. In this method, the

objective function’s gradient is calculated by an

adjoint technique and production constraints like

gas oil ratio and water production are included by

lagrangian formulations into the optimization

process. Moreover, hydrocarbon production will be

optimized drastically with the suitable well

placement. [1, 2]

Well placement is an important part of any filed

development which is considered as a non-linear

problem. Generally, two approaches are used to

Mehdi Talebpour, Mahdi Rastegarnia and Ali Sanati / International Journal of Petroleum and Geoscience Engineering (IJPGE) 4 (2): 78-103, 2016

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solve this problem. First one is the empirical

approach which is common practice in the industry

nowadays. This approach is obviously suitable for

small scale reservoirs with few numbers of wells.

Second one is the mathematical modeling approach

which is based on mathematics and computer to

determine the well places. This approach is divided

into three categories. First; direct methods which

are based on finite differences and adjoint methods.

Second; random algorithms like Simulated

Annealing, Particle Swarm Optimization,

Simultaneous Perturbation Stochastic Algorithm

and some evolutionary algorithms like Genetic

Algorithms, Evolutionary Programming and

Evolution Strategies. Third; hybrid methods which

are combinations of direct and random methods. [1-

7]

Optimization is increasingly involved in almost all

software programs today. Most simulators use a

black box for analyzing the objective function to

get the optimized parameters. Eclipse 300 is one of

these softwares which uses the objective function’s

gradients instead of production profiles. These

gradients are calculated using the adjoint methods,

after that the production constraints are included

with a lagrangian formulation [8].

In this study, different scenarios with different well

numbers were investigated to obtain the optimum

scenario. Moreover, sensitivity analysis is applied

on different parameters like daily production and

bottom-hole pressure as the controlling parameters.

A reservoir sector was modeled and different

parameters were change repeatedly in the

optimization process. Finally, cumulative

production was obtained from optimized

production rate and bottom-hole pressure. For the

sake of comparison, production resulted from

natural depletion is also studied and considered as a

base.

2. Methodology and Results

2.1. An Introduction to the Field

In this study, Azadegan field was investigated

which is located 80 kilometers to the west from

Ahwas city near Iran-Iraq barrier. Oil in place is

estimated to be around 33 billion barrels and the

field area is about 911 kilometers squared. This

field comprises of five layers named as Kazhdomi,

Gadvan, Fahlian, Sarvak and Ilam.

2.2. Cumulative Production Optimization

In order to optimize the production from Azadegan

field, different scenarios were investigated with

different well numbers in a sector of one of the

field’s reservoirs. Sensitivity analysis was also

performed on different parameters like daily

production rate and bottom-hole pressure. We used

Eclipse 300 simulation software for simulation

process where compositional model were selected.

For the sake of comparison, production resulted

from natural depletion is also studied and

considered as a base for each scenario.

2.2.1. Well Fluid’s PVT

Table 1 shows the original composition the well

fluid which is derived from a differential separation

test at elevated pressure more than the bubble point

pressure. Based on this test the bubble point

pressure of the sample at 191 F was 2959 psi.


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Table 1: Differential Separation Test at Elevated Pressure

Higher Than the Bubble Point Pressure

2.2.2. Natural Depletion Scenario

In this step, a reservoir sector was simulated using

the rock and fluid properties to get the natural

production from reservoir. 8 wells were producing

from reservoir without any scenarios for improving

the recovery. Figures 1 to 4 show the daily

production rate, cumulative oil production,

reservoir pressure and gas oil ratio respectively.

2.2.3. Increasing Well Numbers as a Production

Optimization Scenario

In this scenario, we increased the number of wells

while considering constraints like well spacing and

well interferences. Surface production limitations

were also considered. Optimization performed with

8, 10 and 13 wells using bottom-hole pressure and

daily production rate as the controlling parameters.

2.2.3.1. Daily Production Rate as the Controlling

Parameter

To perform this scenario, the keyword OPTPARS

was used to investigate the daily production rate as

the controlling parameter to get the optimum

cumulative production. Figures 5 to 7 show this

fact for different well numbers. As you can see

from these figures, cumulative oil production

before applying any optimization process were 2.9,

3 and 3.1 million barrels for 8, 10 and 13 wells

respectively. After the optimization process based

on daily production rate these numbers turned to be

3.2 million barrels for each number of wells.

Components ZI (percent) Weight fraction (percent) Molar weight Specific Gravity

INER 0.33 0.11126 37.708 0.78764

C1 40.26 5.7752 16.043 0.425

C2 7.39 1.9869 30.07 0.548

C3 5.05 1.9912 44.097 0.582

C4 4.32 2.2451 58.124 0.57238

C5 1.78 1.1483 72.151 0.6231

C6+ 40.87 86.742 237.37 0.84404


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Fig. 1: Daily Oil Production in Natural Depletion

Scenario.

Fig. 2: Cumulative Oil Production in Natural Depletion

Scenario.


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Fig. 3: Reservoir Pressure in Natural Depletion Scenario.

Fig. 4: Producing Gas Oil Ratio in Natural Depletion

Scenario.


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Fig. 5: Cumulative Oil Production for 8 Wells.



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Figures 8 to 10 show daily production rate for 8, 10

and 13 wells respectively. As you can see from

these figures, daily production rate before applying

any optimization process were 80, 100 and 130

thousand barrels per day for 8, 10 and 13 wells



200 thousand barrels per day for each number of

wells.


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Fig. 8: Daily Oil Production Rate for 8 Wells.



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Figures 11 to 13 show producing gas oil ratio for 8,

10 and 13 wells respectively. As you can see from

these figures, producing gas oil ratio before

applying any optimization process and just like

natural depletion scenario were between 1 to 1.2

thousand cubic feet per day for 8, 10 and 13 wells.

After the optimization process based on daily

production rate, these numbers turned to be 6

thousand cubic feet per day for each number of

wells.


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Fig. 11: Producing Gas Oil Ratio for 8 Wells.



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Figures 14 to 16 show reservoir pressure for 8, 10


these figures, reservoir pressure before applying

any optimization process was 3940 psi for each

well numbers. After the optimization process,

reservoir pressure declined with respect to natural

depletion scenario. One possible reason may be the

increase in cumulative oil production and also

increase in daily production rate.


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Fig. 14: Reservoir Pressure for 8 Wells.



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Figures 17 to 19 show gas cumulative production

for 8, 10 and 13 wells respectively. As you can see

from these figures, gas cumulative production

before applying any optimization process were 300,

320 and 340 million cubic feet per day for 8, 10

and 13 wells respectively. After the optimization

process based on daily production rate these

numbers turned to be 440 million cubic feet per day

for each number of wells.


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Fig. 17: Cumulative Gas Production for 8 Wells.



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2.2.3.2. Bottom-hole pressure as the Controlling

Parameter

To perform this scenario, the key word OPTPARS

was used to investigate the bottom-hole pressure as

the controlling parameter to get the optimum

cumulative production. Figures 20 to 22 show this

fact for different well numbers. As you can see

from these figures, cumulative oil production

before applying any optimization process were 2.9,

3 and 3.1 million barrels for 8, 10 and 13 wells



3.2 million barrels for each number of wells. These

numbers are in complete agreement with the

optimization scenario based on daily production

rate as expected.


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Figures 23 to 25 show daily oil production rate for

8, 10 and 13 wells respectively. As you can see

from these figures, daily production rate before

applying any optimization process were 80, 100

and 130 thousand barrels per day for 8, 10 and 13

wells respectively. After the optimization process

based on bottom-hole pressure, these numbers

turned to be 200 thousand barrels per day for each

number of wells.


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Fig. 23: Daily Oil Production for 8 Wells.



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Figures 26 to 28 show producing gas oil ratio for 8,

10 and 13 wells respectively. As you can see from


applying any optimization process was between 1

to 1.2 thousand cubic feet per day for 8, 10 and 13

wells respectively. After the optimization process

based on bottom-hole pressure, these numbers

turned to be 6 thousand cubic feet per day for each

number of wells.


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Figures 29 to 31 show reservoir pressure for 8, 10



applying any optimization process and just like the

natural depletion scenario was 3940 psi for each

well numbers. After the optimization process based

on bottom-hole pressure, reservoir pressure

declined with respect to natural depletion scenario.

The reason for this is described before.


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Figures 32 to 34 show gas cumulative production

for 8, 10 and 13 wells respectively. As you can see

from these figures, gas cumulative production

before applying any optimization process were 300,

320 and 340 million cubic feet per day for 8, 10

and 13 wells respectively. After the optimization

process based on daily production rate these

numbers turned to be 440 million cubic feet per day

for each number of wells.


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Conclusions

Sensitivity analysis was performed based on the

number and location of the wells regarding the

well’s drainage radius and other factors like surface

facilities. Well location turned to be an important

parameter affecting cumulative production. Also

with applying a suitable optimization process,

target production can be achieved with less number

of wells. Cumulative oil production before and

after optimization is shown below.

Scenario FOPT (STBD)

Before

Optimization

After

Optimization

Natural

Depletion

2.90E+8 2.90E+8

8 Wells 2.90E+8 3.20E+8

10 Wells 2.90E+8 3.20E+8

13 Wells 2.90E+8 3.20E+8


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Daily production rate before and after optimization

is shown below:

Scenario FOPR (STBD)

Before

Optimization

After

Optimization

Natural

Depletion

80000 80000

8 Wells 80000 200000

10 Wells 100000 200000

13 Wells 120000 200000

Producing gas oil ratio after 34 years of production

is shown below:

Scenario FGOR

(STBD)

Before

Optimization

After

Optimization

Natural

Depletion

1.00E+00 1.00E+00

8 Wells 6.00E+00 3.20E+08

10 Wells 8.00E+00 1.16E+01

13 Wells 8.00E+00 1.16E+01

Reservoir pressure after the optimization process

declined more than that of natural depletion which

is thought to be the result of the increase in

cumulative production and also increase in daily

production rate. However, reservoir pressure at late

time increases due to the fact that gas oil ratio will

be the maximum at that time which in turn will

increase the reservoir pressure. Simulation results

show that the optimization process can be an

important factor in increasing cumulative

production. Also based on the simulation results,

the target production will be achieved with less

number of wells which in turn reduce the

operational cost drastically. So we strongly advise

optimization as the key to success in oil and gas

field development.

References

[1] Bouzarkouna, Z., Well Placement

Optimization, Ph.D. Thesis, Department of

Computer Science, University Paris-SUD, Paris,

France, 2012.

[2] Nasrabadi, H., Morales, A., Zhu, D., Well-

placement optimization: a survey with special

focus on application for gas/gas-condensate

reservoirs, J. Nat. Gas Sci. Eng. 5:6-16, 2012.

[3] Sarma, P., Chen, W. H., Efficient well-

placement optimization with gradient-based

algorithms and adjoint models, SPE Intelligent

Energy Conference, Amsterdam, The Netherlands,

2007.

[4] Zandvliet, M. J., Handels, M., Van Essen,

G. M., Brouwer, D. R., Jansen, J. D., Adjoint-

based well- placement optimization under

production constraints, SPEJ, 13(4): 392-399,

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[5] Vlemmix, S., Joosten, G. J. P., Brouwer,

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EUROPEC/EAGE Annual Conference,

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[6] Wang, C., Li, G., Reynolds, A. C.,

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optimization, SPE Eastern Meeting Lexington,

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[7] Zakirov, I. S., Aanonsen, S. I., Zakirov, E.

S., Palatnik, B. M., Optimization of reservoir

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[8] Asheim, H., Maximization of Water

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International Journal of Petroleum and Geoscience Engineering · 78 | P a g e International Journal...

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