Chemical Engineering
Biosci. Biotech. Res. Comm. 10(1): 161-166 (2017)
A case study on simulation and optimization of
arti cial lift methods in one of the Iranian oil  elds
Amin Azdarpour*, Mohammad Afkhami Karaei and Abdolreza Dabiri
Department of Petroleum Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Oil production from reservoirs usually occurs by natural  ow of the  uid out of the formation. This oil recovery is
called primary recovery, where the production is solely controlled by the natural energy of the formation. However,
after some times of production reservoir pressure declines, which causes a decline in oil production rate. Thus, regain-
ing the reservoir pressure to enhance oil production is of great importance. Gas lift as one of the best methods of oil
recovery when reservoir pressure declines has been implemented for decades. The reservoir pressure of one of the
oil  elds in Iran has been dropped to a level where no natural  uid  ow occurs form the reservoir. Gas lift has been
proposed to compensate the natural pressure of the reservoir and ease the petroleum production from the reservoir.
PIPESIM software was used to study the effectiveness of the gas lift system. Different parameters including tub-
ing diameter, injected gas rates, and injection depth and their effect on in ow performance relationship (IPR) were
investigated. The simulation results showed that natural energy of the reservoir is not suf cient for producing oil.
Thus, gas lift as of the best methods of increasing the production rate in this  eld could be implemented successfully.
*Corresponding Author:;
Received 15
Dec, 2016
Accepted after revision 19
March, 2017
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Gas lift is a method ofarti cial liftthat uses an exter-
nal source of high-pressure gas for supplementing for-
mation gas to lift the well  uids. The principle of gas
lift is that gas injected into the tubing reduces the den-
sity of the  uids in the tubing, and the bubbles have
a scrubbing action on the liquids. Both factors act to
lower the  owing bottom-hole pressure (BHP) at the
bottom of the tubing. There are two basic types of
gas lift in use today, continuous and intermittent  ow
(Denney, 1999, Al Abdin, 2000, Decker, 2008, Hearn,
2008, Akinnibosun et al. 2011, Allyeva and Novruzaliyev,
Azdarpour, Karaei and Dabiri
Bahadori et al. (2001) performed a simulation study
using PVD data along with  uid and multiphase  ow
correlations. They monitored the actual pressure and
temperature data to determine the point of gas injection
and construct the gas lift performance curve. They used
solution nodal method to determine the optimal gas lift
conditions including optimum injection depth, optimum
wellhead pressure, optimum production rate and mini-
mum injection gas volume. Their analysis showed that
the optimum production rate of 2200 bbl/day and opti-
mum injection gas volume of 1.1 MM SCF/day could
be gained using gas lift process. In addition, they also
concluded that the optimum gas liquid ratio is about
2100 SCF/bbl where increasing the gas liquid ratio from
its optimum value has no signi cant effect on produc-
tion rate.
de Souza et al. (2010) simulated the process of con-
tinuous gas lift using an optimization algorithm cou-
pled to a stationary two phase  ow network model. They
declared that the solution of the optimization system
could be used for the injected gas  ow rate to maxi-
mize the production rate and pro t as well as lowering
the capital cost of the process. Their study particularly
focused on two cases, an offshore single well case and
a complex subsea system. The analysis on offshore well
showed that the gas lift system could be optimized to
yield the maximum oil production for different well
pressure, while maximizing the pro t gained from the
process. At the second case study, a complex petroleum
production system with multiple wells is simulated and
optimized to obtain the optimal design considering
annualized costs of compressor, turbine driver, gas pipe-
lines and fuel gas consumption.
In another study by Mahmudi and Sadeghi (2013) the
gas lift system was optimized to maximize the pro t of
the well for a long period of time. They investigated the
effects of gas injection rate, tubing diameter and sepa-
rator pressure on overall performance of gas lift sys-
tem. They developed a mathematical model coupled
to a combination of Marquardt optimization method
and a genetic algorithm to simulate the gas lift system.
Their results showed that production lifetime should be
divided into a number of consecutive operation intervals
with different tubing diameter, lift gas injection rates
and separator pressures and an optimum value for tub-
ing diameter.
Naderi et al. (2014) investigated the feasibility of arti-
cial lift selection in the Khesht  eld. The Khesht  eld is
located in in south of Iran and was discovered in 1992.
Asmari reservoir, which is one the most important oil
reservoirs is located in this  eld. The authors performed
a comprehensive study to select the most suitable arti -
cial lift method, which can be applied in this  eld. They
investigated different production scenarios including
natural  ow, electrical submersible pump, and gas lift.
They concluded that, initially natural  ow should be
considered. However, by increasing the water cut gas lift
can be implemented and if the water cut is too high the
electrical submersible pump can be utilized.
Ebrahimi and Khamehchi (2016) have investigated
the feasibility and effectiveness of the natural gas lift
(NGL) using supportive vector machine (SVM). They
optimized the process using particle swarm optimiza-
tion (PSO) and genetic algorithm (GA). The optimum
SVM parameters were determined by the PSO algorithm.
Taguchi experiment design was used to determine opti-
mum GA and PSO parameters. The simulation results
showed that SVM could be used effectively to simulate
the gas lift process.
In this study, the feasibility of gas lift implementation
in one of the oil reservoirs in Iran was investigated. The
reservoir characteristics data including rock and  uid
properties were used as input data to be used in PIPESIM
software. In addition, different parameters including
tubing size, injection depth and  ow rates were varied
to determine the optimum production scenario.
In order to perform the optimization study, PIPESIM
software was used. This software is capable of deter-
ming optimum prodcution scenarios during arti cail
lift activities (Sedarat et al. 2014, Silva et al. 2015,
AlHarooni et al. 2015). The required data such as pro-
duction rate data, average reservoir pressure, bottom
hole  owing pressure, and well pro le data were used
as input data. The vertical multiphase  ow correlation
and  uid properties were utilized to determine produ-
ction rate as a function of gas injection rates. Injected
gas rate and production tubing size are the only main
parameters that affect well performance, however, since
production tubing is already  xed in the reservoir, thus
injection gas rate can be controlled to optimize the well
The methodology of perform the simulation study
was taken by the method presented by Bahadori et al.
(2001). The following steps were followed to perform the
simulation and optimization study of gas lift system. In
the  rst step of this study, appropriate  uid properties
correlations were selected. Then, pressure travers (P
inside the tubing string was calculated. Then, different
values of GLR and production rates were assumed and
gas injection pressure (P
) in the casing was calculated.
The values of P
and P
were compared and if P
, the depth of injection was determined. The well head
Azdarpour, Karaei and Dabiri
FIGURE 2. IPR of producing well with variable produc-
tivity index.
FIGURE 3. IPR-VLP plot of the production well.
pressure was calculated using the assumed GLR and pro-
duction rates. Then, well head pressure as a function of
ow rates for horizontal and vertical tubing were plot-
ted and the intersection of these curves was determined.
Finally, the performance curve was plotted and optimum
production rates and injection rates were determined.
The studied oil  led is located in south of Iran. Pres-
sure data analysis shows that reservoir pressure has been
declining to about 4368 psi, which is near the bubble
point pressure of the reservoir (4100 psi). The reservoir
temperature is 220 ºF. The reservoir permeability is 3
mD, connate water saturation is 0.24, and crude oil API
is 32. In addition, productivity index (PI) of the reser-
voir is 0.8 STB/Day/Psi and gas oil ratio is about 381
Figure 1 represents the IPR of this reservoir under dif-
ferent  ow rates, where PI is about 0.8 STB/Day/Psi and
reservoir pressure is 4368 psi. In this case the absolute
open  ow (AOF) of the well is determined to be around
2927.9 STB/Day. On the other hand, Figure 2 represents
the IPR of this reservoir under different PIs. As shown
in this  gure, increasing PI increases the AOF of the
well, which is considered positive from production per-
spective. It is worth mentioning that increasing the AOF
increases the prodcution from the reservoir, whcih is the
main target of this study.
FIGURE 1. In ow perfromance relationship (IPR) of the
production well.
Figure 3 represents the in ow (IPR) and out ow
(VLP) curve of the investigated well where AOF was
calculated to be 2927.9 STB/day. The optimum produc-
tion rate of 184.4 bbl/day was yield based on the tubing
size and present condition of well. This  ow rate yields
a well head pressure of 350 psi. In order to optimize
the production rate form this well, different tubing size
were utilized and the production pro le (IPR-VLP) curve
was plotted as shown in Figure 4. Different tubing size
including 2.44, 2.99, 3.92, and 8.60 inches were utilized
Azdarpour, Karaei and Dabiri
with the bottom measured depth of 3593.3 m and bot-
tom true vertical depth of 3299.9 m.
In another production scenario, well performance
was investigated where the liquid and gas rates were
344.1 STB/day and 0.13109 MMSCF/day, respectively
and total depth was 1500 m. Figure 5 represents the
IPR-VLP curve under this production scenario. On the
other hand, the effect of different tubing size on produc-
tion rate at total depth of 1500 m was investigated and
the results are shown in Figure 6. Table 1 represents the
calculated production rates under different tubing size.
As shown in this table, the maximum production rate
is achieved under 2.875 inch tubing size. The minimum
pro table production rate of 1000 bbl/day is expected
under natural  ow from the well, which in this case has
not been achieved. Thus, gas lift could be implemented
to maximize the production rate from this  eld.
Gas lift approach can be implemented in this  eld
since intersection of IPR and VLP curves happens in low
ow rates. Gas was injected to the  eld with the gas
to liquid ratio (GLR) of 850 SCF/STB. The optimum oil
production rate of 1139 STB/day was achieved under gas
lift implementation in this  eld. Figure 7 represents the
IPR-VLP plot of this  eld after gas lift implementation.
Ga s injection is one of the most important paramters for
prodcution optimization during arti cial lift activities.
The pressure drop due to  uid level drop is reduced by
gas injection to the wellbore, thus, the density of the
uid is reduced and it moves more easily to the sur-
face (Hana zadeh et al., 2014; Ebrahimi and Khamechi,
2016; Elldakli and Soliman, 2017). Increasing the tubing
FIGURE 4. System analysis plot of production well with
different tubing size.
FIGURE 5. IPR-VLP plot of production well at 1500 m.
Table 1. Oil production rate under different tubing size
Tubing size (inch) Oil rate (STB/day)
350.6 2.875
188.8 3.5
182.8 4.5
179 9.875
FIGURE 6. System analysis plot with differnet
tubing size at 1500 m
Azdarpour, Karaei and Dabiri
size decreases the pressure drop due to friction in the
wellbore, thus increasing the prodcution rate. However,
the pressure drop due to  uid level drop would be domi-
ninat over the pressure drop due to friction when the
tubing size is more than the optimum tubing size. This
would result in production rate to decrease (Campono-
gara et al., 2010; Mahmudi and Sadeghi, 2013; Allyeva
and Novruzaliyev, 2015; Elldakli and Soliman, 2017).
Therfore, there always must be an optimum value for
the tubing size as it was shown in Figures 4-6 in this
study. The value of GLR is increased with increasing gas
injection rate. Hence, the pressure drop is reduced and
prodcution rate is increased, which creates a favorable
condition in terms of oil recovery. On the other hand,
when the value of the GLR is more than its critical value
), the results is inverse, which causes the
production rate to decline. The reason is that the pres-
sure drop due to friction would be dominant over the
pressure drop due to  uid level drop (Ray and Sarker,
2007; Guerrero-Sarabia and Fairuzov, 2013; Elldakli
and Soliman, 2017). The same thing happened in this
study, where the value of GLR
was found to be 850
SCF/STB, which resulted in the maximum prodcution
rate as shown in Figure 7.
In this study, different tubing size and total depth were
simulated to determined the effectiveness of natural
ow from the reservoir. The simulation results showed
that the natural  ow from the well is not capable of
producing suf cient hydrocarbon to the surface. Gas lift
process can be implemented in wide range of depth and
ow rate, which is one of the advantages of this method
over other arti cial methods. The simulation results
showed that the optimum tubing size during gas lift
process is 2.875 inch, which resulted in the maximum
ow rate. In addition, the simulation results showed that
gas lift can be implemented in wells with low produc-
tivity index and low gas to liquid ratio. Moreover, the
simualtion results showed that gas lift is one of the best
arti cial methods to be considered in Iran since large
gas resources are available. This is one of the most
important advantages of using gas lift for improving oil
recovery in Iran.
The authors would like to appreciate the Department of
Petroleum Engineering, Marvdasht Branch, Islamic Azad
University, Marvdasht, Iran for the provision of the
laboratory facilities necessary for completing this work.
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FIGURE 7. IPR-VLP plot of production well after gas
Azdarpour, Karaei and Dabiri
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