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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
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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.
Fig. 6: Cumulative Oil Production for 10 Wells.
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Fig. 7: Cumulative Oil Production for 13 Wells.
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
respectively. After the optimization process based
on daily production rate these numbers turned to be
200 thousand barrels per day for each number of
wells.
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Fig. 8: Daily Oil Production Rate for 8 Wells.
Fig. 9: Daily Oil Production Rate for 10 Wells.
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Fig. 10: Daily Oil Production Rate for 13 Wells.
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.
Fig. 12: Producing Gas Oil Ratio for 10 Wells.
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Fig. 13: Producing Gas Oil Ratio for 13 Wells.
Figures 14 to 16 show reservoir pressure for 8, 10
and 13 wells respectively. As you can see from
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.
Mehdi Talebpour, Mahdi Rastegarnia and Ali Sanati / International Journal of Petroleum and Geoscience Engineering (IJPGE) 4 (2): 78-103, 2016
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Fig. 14: Reservoir Pressure for 8 Wells.
Fig. 15: Reservoir Pressure for 10 Wells.
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Fig. 16: Reservoir Pressure for 13 Wells.
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.
Fig. 18: Cumulative Gas Production for 10 Wells.
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Fig. 19: Cumulative Gas Production for 13 Wells.
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
respectively. After the optimization process based
on daily production rate these numbers turned to be
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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Fig. 20: Cumulative Oil Production for 8 Wells.
Fig. 21: Cumulative Oil Production for 10 Wells.
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Fig. 22: Cumulative Oil Production for 13 Wells.
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.
Fig. 24: Daily Oil Production for 10 Wells.
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Fig. 25: Daily Oil Production for 13 Wells.
Figures 26 to 28 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 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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Fig. 26: Producing Gas Oil Ratio for 8 Wells.
Fig. 27: Producing Gas Oil Ratio for 10 Wells.
Mehdi Talebpour, Mahdi Rastegarnia and Ali Sanati / International Journal of Petroleum and Geoscience Engineering (IJPGE) 4 (2): 78-103, 2016
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Fig. 28: Producing Gas Oil Ratio for 13 Wells.
Figures 29 to 31 show reservoir pressure 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 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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Fig. 29: Reservoir Pressure for 8 Wells.
Fig. 30: Reservoir Pressure for 10 Wells.
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Fig. 31: Reservoir Pressure for 13 Wells.
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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Fig. 32: Cumulative Gas Production for 8 Wells.
Fig. 33: Cumulative Gas Production for 10 Wells.
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Fig. 34: Cumulative Gas Production for 13 Wells.
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.
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