Developing an improved approach to solving a new gas lift optimization problem
- 436 Downloads
Abstract
The increased speed and accuracy in solving optimization problems of gas allocation in the gas lift process are of high importance. Solving gas allocation optimization problems generally involves two steps: (1) The gas lift performance curve (GLPC) fitting (gas lift modeling) and (2) optimizing the allocation of gas between wells. Therefore, in order to increase the speed and accuracy of solving gas allocation optimization problems, both steps need to be improved. In order to increase the accuracy of the first step, a new correlation was proposed in which, in addition to increasing the accuracy of fit, the optimization speed was improved by decreasing the number of constants used in the correlation. Besides, in order to improve the performance of the second step, water cycle optimization algorithm was used and the results obtained from this algorithm were compared with the results obtained from previous studies on teaching–learning-based optimization (TLBO) algorithm, continuous ant colony (CACO) algorithm, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for solving the five-well Nishikiori index problem. The results suggested that the water cycle optimization algorithm has a very good performance in terms of convergence rate, non-capture at local optimum points and repeatability. Finally, as a new problem, the gas allocation between the wells of one of the heavy oil fields in the southwest of Iran was optimized with predetermined oil production rates. The goal of optimization was to obtain the minimum amount of gas required to produce the predetermined oil rates using the water cycle optimization algorithm. The results showed that optimization is of higher importance in lower oil production targets, resulting in higher additional oil production.
Keywords
Gas lift optimization GLPC correlation Water cycle optimization GA PSOIntroduction
When the natural energy of the reservoir is not able to transfer the fluid to surface and to overcome the weight of the fluid column in the well, one of the artificial lift techniques should be used to produce the fluid in an economically efficient way. Artificial lift techniques reduce the pressure from the fluid column in the well and, as a result, reduce the pressure at the well bottom, causing a large pressure difference between the reservoir and the well bottom, resulting in the transfer of the fluid produced to the surface. One of the most commonly used artificial lift techniques is the gas lift process. In this method, the pressured gas is injected in the bottom of the tubing, and the oil is produced through two mechanisms of pushing through the expansion of the gas and reducing the hydrostatic pressure of the fluid column inside the well. However, the excessive increase in the amount of gas injected leads to the increased frictional pressure drop and, consequently, a decrease in the production increase due to the reduction in hydrostatic pressure (Economides et al. 2013). Therefore, determining the optimal amount of injected gas between network of production wells is one of the main gas lift challenges and is referred to as the gas lift allocation problem (Miresmaeili et al. 2015).
Gas lift allocation optimization
Literature review
Considering the importance of gas allocation optimization, many studies have been conducted to explore it. For instance, Kanu et al. (1981) used the equal slope method to properly distribute gas rates between wells. Later, Nishikiori et al. (1995) used the nonlinear constrained formulation with a stochastic quasi-Newton method to find optimal solutions. Fang and Lo (1996) departed from the nonlinear programming process and proposed the piecewise linearization of the well performance curves and thus changed the problem into a linear programming problem. Afterward, Buitrago et al. (1996) combined stochastic search and heuristic descent direction and called their method Ex-In. Alarcon et al. (2002) improved the method proposed by Nishikiori et al. (1995) by replacing the quasi-Newton algorithm with sequential quadratic programming (SQP). Wang et al. (2002) proposed a mixed-integer nonlinear programming (MINLP) techniques for integrating the previous methods. Their model included the allocation of gas rates, the production rate of wells and equipment constraints. Nakashima and Camponogara (2006) developed the recursive algorithm for gas rate allocation. They were the first to study the discontinuity of the well performance curve in detail. Ray and Sarker (2006) used the piecewise linearization method and genetic algorithm (GA) to optimize the gas allocation rate. Camponogara and Conto (2009) improved the piecewise linearization formulation that was previously developed. Zerafat et al. (2009) solved the gas rate distribution using two genetic and ant colony (ACO) algorithms. Hamedi et al. (2011) used the particle swarm optimization algorithm (PSO) to optimize the gas rate allocation in the wells of an Iranian oil field. Sharma and Glemmestad (2013) used the generalized reduced gradient (GRG) technique, self-optimizing control structure and multi-start technique to optimize gas allocation in the gas lift process. Ghaedi et al. (2014) employed the continuous ant colony (CACO) algorithm to explore gas allocation optimization and compared their results with previous studies. Ghassemzadeh and Pourafshary (2015) proposed a new method for considering the time factor in optimizing the gas allocation process. They used a piecewise cubic hermite function for modeling the gas lift performance and genetic algorithm for optimization. Miresmaeili et al. (2015) used a multi-objective optimization algorithm, Gaussian Bayesian networks and Gaussian kernels to solve the gas allocation problem in the gas lift process. Mahdiani and Khamehchi (2015) used the genetic algorithm to investigate instability in the gas allocation optimization problem. Tavakoli et al. (2017) used the artificial neural networks (ANNs) to the gas lift modeling and then studied the gas allocation optimization using the genetic algorithm. Also, Miresmaeili et al. (2019) employed the artificial neural networks and used Levenberg–Marquardt (LM) and Bayesian regularization (BR) algorithms to model gas lift operation and then used the teaching–learning-based optimization (TLBO) algorithm to optimize gas allocation. Moreover, Namdar and Shahmohammadi (2019), with the help of a simple method without programming, optimized the gas allocation by the excel solver optimization tool.
In this study, in order to increase the speed and accuracy of solving gas lift allocation optimization problems, at the first a new correlation was proposed for modeling the gas lift process and GLPC curve fitting. The modeling results were compared with those obtained through other modeling techniques. In order to improve the optimization performance, a water cycle optimization algorithm (WCA) was used and the results of its implementation were compared with the results obtained from previous studies on TLBO, CACO, GA and PSO algorithms for solving the five-well Nishikiori (1989) index problem. Also, given the effect of the gas lift process modeling on the optimization results, and since previous studies have used various modeling techniques, we compared the performance of the water cycle optimization algorithm with the two well-known PSO and GA algorithms in terms of convergence rate, non-capture at local optimum points and repeatability with the same correlation for modeling gas lift and with equal number of iterations in order to evaluate the performance of the algorithm itself individually and eliminate the modeling accuracy effects. Finally, given that the optimization problem of scenario 2 has not been investigated so far, this study attempted to optimize the gas allocation between the wells of one of the heavy oil fields in the southwest of Iran with predetermined quantities of oil production. In this scenario, the optimization goal was to obtain the minimum amount of gas required to produce the predetermined oil levels using the water cycle optimization algorithm.
Water cycle algorithm
If the solution presented by a river is more effective than that of the sea, then the position of the river and the sea is exchanged (that is, the sea changes into the river and the river into the sea). This exchange can also occur for the streams and the sea as well as the streams and the rivers (Sadollah et al. 2015).
In order to prevent the rapid convergence of the algorithms (immature convergence) and increase the exploration ability of the algorithms, a new chance commensurate with the distance of the rivers and streams from the sea is given to them. This concept in the WCA algorithm is applied under evaporation condition and raining process. If the Euclidean distance of the rivers and streams from the sea is less than a predetermined insignificant value (near zero) (\(d_{ \hbox{max} }\)), the conditions for evaporation from the sea are fulfilled and the raining process starts, and the rivers and streams are reformed (Sadollah et al. 2015).
The application of constraints
- 1.
Of two feasible solutions, the solution whose objective function has a lower value is selected.
- 2.
Of a feasible solution and a non-feasible solution, the feasible solution is preferred.
- 3.
Of the two non-feasible solutions, a solution that has the slightest violation of the constraints is selected as the feasible solution.
Methodology
- 1.
Modeling the gas lift process and fitting the oil production data against gas injections by one of the correlations presented.
- 2.
Implementation of the objective function of the gas allocation optimization problem and its constraints and solving it by one of the optimization algorithms.
Results and discussion
Gas lift modeling process
The formation of the gas lift performance curve (GLPC) is the first step in modeling the gas lift process. Obtaining an accurate GLPC has a significant effect on optimizing the allocation of injected gas into wells because if the curves are not sufficiently accurate, despite the high performance of the optimization algorithm, the optimization results will not be accurate and the produced oil overestimated or underestimated. So far, many methods have been used to model the gas lift process and the fitting of GLPCs. In general, these methods can be divided into two groups: modeling methods by fitting through correlation and modeling methods based on artificial neural networks. To date, various correlations have been proposed for GLPC fitting. The quadratic polynomial model is one of these models (Nishikiori 1989).
As it can be seen, the GLPC fitting curve trend in the model proposed by Behjoomanesh et al. (2015) is in a way that leads to the overestimation or underestimation of oil production in some parts of the fitting curve. Generally, the GLPC curve should follow a downward trend on the right side, while, as shown in Fig. 4, in wells 2, 3 and 5, the model proposed by Behjoomanesh et al. (2015) moves upward in the right. Also, however, the downward trend of the curve for well 6 is suddenly bulged and these behaviors generally invalidate the oil production values estimated by this model.
As before, n is the number of observation data, \(y_{i}\) is the ith observed value, \(\bar{y}_{i}\) is the mean of all observed data and \(w_{i}\) is the weight used for each data point, which is usually 1.
References | Well no. | Alarcon et al.’s (2002) model | Hamedi et al.’s (2011) model | Author’s model | |||
---|---|---|---|---|---|---|---|
R^{2} | RMSE | R^{2} | RMSE | R^{2} | RMSE | ||
Nishikiori (1989) | 1 | 0.987848 | 12.15155 | 0.997918 | 4.591787 | 0.999817 | 1.924778 |
2 | 0.991633 | 27.43855 | 0.995863 | 17.86276 | 0.999992 | 1.013721 | |
3 | 0.988812 | 48.66602 | 0.997012 | 23.28292 | 0.99998 | 2.509012 | |
4 | 0.996597 | 7.947265 | 0.999726 | 2.522818 | 0.999965 | 1.27366 | |
5 | 0.996583 | 16.63779 | 0.980617 | 37.06711 | 0.999939 | 2.634642 | |
Ave. | 0.992295 | 22.56823 | 0.994227 | 17.06548 | 0.999939 | 1.871163 | |
Jung and Lim (2016) | 1 | 0.998931 | 20.69668 | 0.978634 | 86.55026 | 0.999973 | 3.563994 |
2 | 0.999433 | 14.29458 | 0.975063 | 88.70493 | 0.999993 | 1.701711 | |
3 | 0.995613 | 56.26503 | 0.987708 | 88.10038 | 0.999979 | 4.198918 | |
4 | 0.999499 | 12.88447 | 0.974472 | 86.01951 | 0.999996 | 1.241376 | |
Ave. | 0.998369 | 26.03519 | 0.978969 | 87.34377 | 0.999985 | 2.6765 |
Reference | Well no. | Alarcon et al.’s (2002) model | Hamedi et al.’s (2011) model | Author’s model | |||
---|---|---|---|---|---|---|---|
R^{2} | RMSE | R^{2} | RMSE | R^{2} | RMSE | ||
Vieira (2015) | 1 | 0.9999 | 4.279252 | 0.988402 | 42.10739 | 0.9999173 | 5.028378 |
2 | 0.999659 | 15.47992 | 0.998197 | 31.83748 | 0.9999833 | 4.847634 | |
3 | 0.999802 | 6.364839 | 0.987168 | 47.4466 | 0.9999904 | 1.720824 | |
4 | 0.974538 | 21.61731 | 0.981893 | 22.95411 | 0.9999996 | 0.127734 | |
5 | 0.975266 | 18.99519 | 0.96637 | 19.8109 | 0.9999997 | 0.091736 | |
Ave. | 0.989833 | 13.3473 | 0.984406 | 32.8313 | 0.9999780 | 2.363261 |
As shown in Table 2, although the proposed model has one constant less than the model proposed by Behjoomanesh et al. (2015), it has higher R^{2} values and lower RMSE than the other three models and thus provides more accurate results in terms of the operational data fitting. The lower number of constants while maintaining the fitting accuracy makes it possible to reduce the time of computation by maintaining accuracy in cases where it is necessary to optimize the gas allocation between a large numbers of wells.
As it is shown, the accuracy of the model proposed in the present study is within the limits of the artificial neural network approach. Accordingly, it can be suggested that the proposed model possesses a high accuracy in terms of the operational data fitting. In the following section, the proposed model is used for the gas lift performance curve (GLPC) fitting.
Performance evaluation of the water cycle algorithm
Comparison of the optimization results for the five-well Nishikiori (1989) index problem by using the TLBO, CACO, GA and PSO algorithms
Well | Available gas = 4600 MSCF/D | Available gas = 3000 MSCF/D | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GA | CACO | TLBO | WCA | PSO | WCA | |||||||
Q_{g} (MSCF/D) | Q_{o} (STB/D) | Q_{g} (MSCF/D) | Q_{o} (STB/D) | Q_{g} (MSCF/D) | Q_{o} (STB/D) | Q_{g} (MSCF/D) | Q_{o} (STB/D) | Q_{g} (MSCF/D) | Q_{o} (STB/D) | Q_{o} (STB/D) | Q_{o} (STB/D) | |
1 | 473.5147 | 365.9301 | 482.25 | 366.72 | 394.3 | 367.4 | 444.18 | 3.64E+02 | 158.75 | 316.557 | 241.74 | 330.36 |
2 | 785.8542 | 759.4612 | 768.52 | 758.05 | 672.3 | 758.9 | 764.57 | 757.68 | 590.45 | 734.19 | 550.35 | 721.58 |
3 | 1134.233 | 1131.002 | 1132.41 | 1132.38 | 1167.5 | 1141.1 | 1211.73 | 1142.21 | 913.48 | 1085.24 | 874.74 | 1085.35 |
4 | 918.9989 | 633.4793 | 898.75 | 631.32 | 918.2 | 633.4 | 887.30 | 632.02 | 575.27 | 578.21 | 574.81 | 579.75 |
5 | 1287.399 | 784.3733 | 1285.87 | 784.86 | 1447.7 | 839.3 | 1292.19 | 785.03 | 762.04 | 669.44 | 758.34 | 696.53 |
Total | 4600 | 3674.25 | 4600 | 3673.33 | 4600 | 3740.1 | 4600 | 3680.58 | 3000 | 3383.63 | 3000 | 3413.59 |
Also investigation of initial operational data for oil production versus gas injection for well no. 5 in Nishikiori (1989) studies indicates that by injecting 1667.3 MSCF/D gas into the well no. 5, the oil production rate will be 813.6 STB/D. Therefore, in order to produce 839.3 STB/D oil, over 1667.3 MSCF/D gas needs to be injected into the well. However, the required amount of the injected gas to produce the same amount of oil as estimated by this algorithm is less than 1667.3 MSCF/D (1447.7 MSCF/D). Therefore, it can be said that the oil production rate is overestimated by the TLBO algorithm, while the results obtained by the WCA algorithm are more reasonable than the initial operational data of the Nishikiori’s (1989) study.
Also, given the effect of the gas lift process modeling on the optimization results, and since previous studies have used various modeling techniques for solving the five-well Nishikiori (1989) index problem, the performance of the water cycle optimization algorithm was compared with the two well-known PSO and GA algorithms in terms of convergence rate, non-capture at local optimum points and repeatability with the same correlation for modeling gas lift process and with equal number of iterations in order to evaluate the performance of the algorithm itself individually and eliminate the modeling accuracy effects. For this purpose, in all three algorithms, the correlation proposed by the author was used in the present study for gas lift fitting and modeling. Also, following previous studies for solving Nishikiori (1989) index problem (Miresmaeili et al. 2019, Ghaedi et al. 2014, Hamedi et al. 2011), the number of iterations was considered equal to 100 in all three algorithms.
As it is shown Fig. 5, the exploration and exploitation phase in the search space in the genetic algorithm is highly dependent on the population and in the lower population, the algorithm is trapped in the local optimum points, while the PSO and WCA algorithms have a more powerful, exploration and extraction phase in the search space and, therefore, are not so dependent on the population and are not trapped in the local optimum points. In addition, the PSO and WCA algorithms have high repeatability in the obtained results.
Case study (Scenario 2)
Specifications of the three productive wells in the field
Property | Well 1 | Well 3 | Well 4 |
---|---|---|---|
Well TVD (ft) | 9730.7 | 9898.29 | 10080.8 |
Well MD (ft) | 9730.97 | 12513.1 | 11302.5 |
Reservoir pressure (psia) | 4640 | 4640 | 4640 |
Bottom hole temperature (F) | 204 | 204 | 204 |
Well head temperature (F) | 77 | 77 | 77 |
Well head flowing pressure (Psia) | 150 | 150 | 150 |
Formation gas–liquid ratio (scf/STB) | 137 | 137 | 137 |
API oil gravity (API) | 13.11 | 13.11 | 13.11 |
Water cut (%) | 40 | 40 | 40 |
PI (STB/day/psi) | 9.3 | 8.5 | 10.1 |
Tubing O.D. (in.) | 4 1/2 | 4 1/2 | 4 1/2 |
Casing O.D. (in.) | 5 | 5 | 5 |
Specific gravity of gas | 0.91 | 0.91 | 0.91 |
Data of oil production versus gas injection in wells of the field
Well 1 | Well 2 | Well 3 | |||
---|---|---|---|---|---|
Qg (MMSCF/D) | Qo (STB/D) | Qg (MMSCF/D) | Qo (STB/D) | Qg (MMSCF/D) | Qo (STB/D) |
0.00E+00 | 1.46E+03 | 0.00E+00 | 8.68E+02 | 0.00E+00 | 1.40E+03 |
6.99E−01 | 4.19E+03 | 5.84E−01 | 3.51E+03 | 7.16E−01 | 4.30E+03 |
1.11E+00 | 4.77E+03 | 9.46E−01 | 4.06E+03 | 1.15E+00 | 4.91E+03 |
1.75E+00 | 5.35E+03 | 1.52E+00 | 4.65E+03 | 1.80E+00 | 5.52E+03 |
2.66E+00 | 5.82E+03 | 2.37E+00 | 5.19E+03 | 2.76E+00 | 6.04E+03 |
3.96E+00 | 6.18E+03 | 3.61E+00 | 5.64E+03 | 4.15E+00 | 6.48E+03 |
5.75E+00 | 6.42E+03 | 5.36E+00 | 5.98E+03 | 6.10E+00 | 6.80E+03 |
8.19E+00 | 6.53E+03 | 7.75E+00 | 6.18E+03 | 8.73E+00 | 6.96E+03 |
1.14E+01 | 6.49E+03 | 1.09E+01 | 6.22E+03 | 1.22E+01 | 6.95E+03 |
Error indices values of GLPC fitting through different correlations
Well no. | Error index | Alarcon et al.’s (2002) model | Hamedi et al.’s (2011) model | Behjoomanesh et al.’s (2015) model | Author’s model |
---|---|---|---|---|---|
1 | R^{2} | 0.989154 | 0.996935 | 0.999981 | 0.999987 |
RMSE | 215.7698 | 104.7083 | 11.68057 | 8.254048 | |
3 | R^{2} | 0.979262 | 0.993753 | 0.999982 | 0.999993 |
RMSE | 297.11 | 150.9735 | 10.64476 | 6.668139 | |
4 | R^{2} | 0.98866 | 0.997422 | 0.999976 | 0.999990 |
RMSE | 241,768 | 105.23 | 14.23711 | 7.994826 |
Constants of the proposed model obtained from the GLPC fitting
Well no. | a | b | c | d | e |
---|---|---|---|---|---|
1 | 5976.40 | 32.19 | − 477.73 | 1129.00 | − 4397.74 |
3 | 5138.54 | − 375.23 | 1071.04 | − 186.29 | − 4289.66 |
4 | 5669.25 | − 280.89 | 661.66 | 367.49 | − 4230.72 |
Gas allocation optimization for wells of the field at different oil flow rates
Required gas | Min. | Max. | |||
---|---|---|---|---|---|
Determined oil rate (STB/D) | Well | Gas injected (MMSCF/D) | Oil produced (STB/D) | Gas injected (MMSCF/D) | Oil produced (STB/D) |
12500 | 1 | 0.772406 | 4314.469 | 1.20246 | 4874.365 |
3 | 0.724736 | 3746.082 | 10.017 | 6222.635 | |
4 | 0.800147 | 4439.449 | 0 | 1403 | |
Total | 2.297289 | 12500 | 11.21946 | 12500 | |
Q_{o}/Q_{g} (STB/MMSCF) | 5441.195 | 1114.136 | |||
15000 | 1 | 1.455337 | 5117.931 | 8.718 | 6534.253 |
3 | 1.434568 | 4575.322 | 0.025065 | 1485.387 | |
4 | 1.539948 | 5306.747 | 10.136 | 6980.359 | |
Total | 4.429853 | 15000 | 18.87906 | 15000 | |
Q_{o}/Q_{g} (STB/MMSCF) | 2821.764 | 794.5309 | |||
17500 | 1 | 2.790915 | 5870.636 | 0.761482 | 4297.009 |
3 | 2.985055 | 5445.478 | 10.017 | 6222.635 | |
4 | 3.105087 | 6183.886 | 10.13 | 6980.356 | |
Total | 8.881057 | 17500 | 20.90848 | 17500 | |
Q_{o}/Q_{g} (STB/MMSCF) | 1970.486 | 836.9809 | |||
Max = 19,737.25 | 1 | 8.718 | 6534.253 | 8.718 | 6534.253 |
3 | 10.017 | 6222.635 | 10.017 | 6222.635 | |
4 | 10.13 | 6980.359 | 10.13 | 6980.359 | |
Total | 28.865 | 19737.25 | 28.865 | 19737.25 | |
Q_{o}/Q_{g} (STB/MMSCF) | 683.7779 | 683.7779 |
In addition to the gas lift, the method used in this paper can be used in improving steam allocation management in thermal enhanced oil recovery methods such as SAGD. One of the challenges in these methods is excessive water production which is due to an improper steam injection plan. Thus, for a given volume of steam, steam allocation between wells should be managed in a manner to delay the water breakthrough in producer wells and, as a result, improves sweep efficiency and increases the oil recovery.
Conclusion
- 1.
In the gas lift process modeling, the correlation proposed by Behjoomanesh et al. (2015), despite the high precision, cannot be applied to all wells because firstly, in some wells, convergence requires limiting the variations of constant coefficients, and secondly, the oil production rates in some oil wells are overestimated or underestimated
- 2.
The model proposed in this study, despite the reduction of a constant compared to the model presented by Behjoomanesh et al. (2015), has a higher fitting accuracy and is also free from the limitations of the mentioned model. Reducing a constant coefficient in this model will increase the speed of optimization. Also, the correlation proposed in this study is more accurate than the Hamedi et al. (2011) and Alarcon et al. (2002).correlations.
- 3.
The GA algorithm is highly dependent on population and is trapped in low populations at local optimum points, while the water cycle and PSO algorithms are not so population dependent and have good repeatability.
- 4.
In terms of the convergence rate, the water cycle algorithm has a much better performance than the PSO and GA algorithms. In high populations, the performance of the PSO and GA algorithms is reduced significantly in terms of speed. The PSO algorithm converges to the optimal response faster than the GA algorithm in a smaller population and more rapidly.
- 5.
In optimization problems that aim to produce a predetermined amount of oil, the closer the targeted oil production rates to the maximum oil production rate in the gas lift process, the surplus oil producible through the optimization can be reduced. As a result, in lower oil production targets, the optimization is more important and generates more extra oil.
Notes
References
- Alarcon G, Torres C, Gomez L (2002) Global optimization of gas allocation to a group of wells in artificial lift using non-linear constrained programming. ASME J Energy Resour Technol 124(4):262–268. https://doi.org/10.1115/1.1488172 CrossRefGoogle Scholar
- Behjoomanesh M, Keyhani M, Ganji-Azad E, Izadmehr M, Riahi S (2015) Assessment of total oil production in gas-lift process of wells using Box-Behnken design of experiments in comparison with traditional approach. J Nat Gas Sci Eng 27:1455–1461. https://doi.org/10.1016/j.jngse.2015.10.008 CrossRefGoogle Scholar
- Buitrago S, Rodrıguez E, Espin D (1996) Global optimization techniques in gas allocation for continuous flow gas lift systems. In: Presented at the SPE gas technology symposium, Calgary, Alberta, Canada, SPE Number 35616. https://doi.org/10.2118/35616-ms
- Camponogara E, Conto AM (2009) Lift-gas allocation under precedence constraints: MILP formulation and computational analysis. IEEE Trans Autom Sci Eng 6(3):544–551. https://doi.org/10.1109/TASE.2009.2021333 CrossRefGoogle Scholar
- Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2/4):311–338. https://doi.org/10.1016/S0045-7825(99)00389-8 CrossRefGoogle Scholar
- Economides MJ, Hill AD, Economides CE, Zhu D (2013) Petroleum production systems. Prentice Hall Publication, Englewood CliffsGoogle Scholar
- Fang W, Lo K (1996) A generalized well-management scheme for reservoir simulation, Society of Petroleum Engineers. https://doi.org/10.2118/29124-pa CrossRefGoogle Scholar
- Ghaedi M, Ghotbi C, Aminshahidy B (2014) The optimization of gas allocation to a group of wells in a gas lift using an efficient ant colony algorithm (ACO). Energy Sources Part A Recovery Util Environ Effects 36(11):1234–1248. https://doi.org/10.1080/15567036.2010.536829 CrossRefGoogle Scholar
- Ghassemzadeh S, Pourafshary P (2015) Development of an intelligent economic model to optimize the initiation time of gas lift operation. J Pet Explor Prod Technol 5(3):315–320. https://doi.org/10.1007/s13202-014-0140-z CrossRefGoogle Scholar
- Hamedi H, Rashidi F, Khamehchi E (2011) A Novel approach to the gas-lift allocation optimization problem. J Pet Sci Technol 29(4):418–427. https://doi.org/10.1080/10916460903394110 CrossRefGoogle Scholar
- Jung SY, Lim JS (2016) Optimization of gas lift allocation for improved oil production under facilities constraints. Geosyst Eng 19(1):39–47. https://doi.org/10.1080/12269328.2015.1084895 CrossRefGoogle Scholar
- Kanu EP, Mach J, Brown KE (1981) Economic approach to oil production and gas allocation in continuous gas lift. J Pet Technol. https://doi.org/10.2118/9084-pa CrossRefGoogle Scholar
- Mahdiani MR, Khamehchi E (2015) Stabilizing gas lift optimization with different amounts of available lift gas. J Nat Gas Sci Eng 26(1):18–27. https://doi.org/10.1016/j.jngse.2015.05.020 CrossRefGoogle Scholar
- MATLAB Manual, Version 9.5.0.944444 (R2018b), 2018. Natick, Massachusetts: The MathWorks IncGoogle Scholar
- Miresmaeili SOH, Pourafshary P, Jalali Farahani F (2015) A novel multi-objective estimation of distribution algorithm for solving gas lift allocation problem. J Nat Gas Sci Eng 23:272–280. https://doi.org/10.1016/j.jngse.2015.02.003 CrossRefGoogle Scholar
- Miresmaeili SOH, Zoveidavianpoor M, Jalilavi M, Gerami S, Rajabi A (2019) An improved optimization method in gas allocation for continuous flow gas-lift system. J Pet Sci Eng 172(1):819–830. https://doi.org/10.1016/j.petrol.2018.08.076 CrossRefGoogle Scholar
- Nakashima P, Camponogara E (2006) Solving a gas-lift optimization problem by dynamic programming. IEEE Trans Syst Man Cyber Part A 36(2):407–414. https://doi.org/10.1016/j.ejor.2005.03.004 CrossRefGoogle Scholar
- Namdar H, Shahmohammadi M (2019) Optimization of production and lift-gas allocation to producing wells by a new developed GLPC correlation and a simple optimization method. J Energy Sources Part A: Recovery, Util Environ Effects. https://doi.org/10.1080/15567036.2019.1568635 CrossRefGoogle Scholar
- Nishikiori N (1989) Gas allocation optimization for continues flow gas lift systems, M.S. Thesis, University of TulsaGoogle Scholar
- Nishikiori NN, Redner RA, Doty DR, Schmidt ZZ (1995) An improved method for gas lift allocation optimization. ASME J Energy Resour Technol 1995. https://doi.org/10.2118/19711-ms
- Rashid K (2010) Optimal Allocation Procedure for Gas-Lift Optimization. Ind Eng Chem Res 49(5):2286–2294. https://doi.org/10.1021/ie900867r CrossRefGoogle Scholar
- Ray T, Sarker R (2006) Multiobjective evolutionary approach to the solution of gas lift optimization problems, IEEE Congress on Evolutionary Computation, pp 3182–3188. https://doi.org/10.1109/cec.2006.1688712
- Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71. https://doi.org/10.1016/j.asoc.2015.01.050 CrossRefGoogle Scholar
- Sharma R, Glemmestad B (2013) A novel multi-objective estimation of distribution algorithm for solving gas lift allocation problem. J Process Control 23(8):1129–1140. https://doi.org/10.1016/j.jngse.2015.02.003 CrossRefGoogle Scholar
- Sukarno P, Saepudin D, Dewi S, Soewono E, Sidarto K, Gunawan A (2009) Optimization of gas injection allocation in a dual gas lift well system. ASME, J Energy Resour Technol. https://doi.org/10.1115/1.3185345 CrossRefGoogle Scholar
- Tavakoli R, Daryasafar A, Keyhani M, Behjoomanesh M (2017) Optimization of gas lift allocation using different models. Recent Adv Petrochem Sci 1(2):555559. https://doi.org/10.19080/RAPSCI.2017.01.555559 CrossRefGoogle Scholar
- Vieira CRG (2015) Model-based optimization of production systems, M.S. Thesis, Norwegian University of Science and TechnologyGoogle Scholar
- Wang P, Litvak M, Aziz K (2002) Optimization of production operations in petroleum fields. In: SPE Annual Technical Conference and Exhibition, San Antonio, Texas’, Society of Petroleum Engineers, 29 September-2 October. https://doi.org/10.2118/77658-ms
- Zerafat MM, Ayatollahi S, Roosta AA (2009) Genetic algorithm and ant colony approach for gas-lift allocation optimization. J Jpn Pet Inst 52(1):102–107. https://doi.org/10.1627/jpi.52.102 CrossRefGoogle Scholar
Copyright information
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.