A Review of Intelligent Optimization Algorithms for Oilfield Development

Nian Lu; Mengli Hong

doi:10.62051/ijgem.v10n3.13

Authors

Nian Lu
Mengli Hong

DOI:

https://doi.org/10.62051/ijgem.v10n3.13

Keywords:

Oilfield development, Optimization problems, Intelligent optimization algorithms

Abstract

To identify the most suitable oilfield development strategy for maximizing energy supply or economic returns, optimization of oilfield development remains one of the most critical challenges in closed-loop reservoir management. In recent decades, with the advancement of artificial intelligence technologies, intelligent optimization algorithms have been increasingly applied to improve the efficiency and accuracy of optimization outcomes in oilfield development. This paper provides a comprehensive review of intelligent optimization algorithms applied to oilfield development optimization problems. It covers several key topics within this domain, ranging from the fundamental components of such problems—decision variables, objective functions, and constraints—to various algorithmic approaches developed from different perspectives and for different types of optimization problems.

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References

[1] Alshamasin M., Lim Y., Jang D. Adaptive Selection Operator for Genetic Algorithm with Enhanced Convergence and Diversity. IEEE Access, Vol. 9, pp. 69734–69747, 2021.

[2] Antonio C., Roland N.H. Reservoir Development and Design Optimization. SPE38895, 2017.

[3] Carlos A., Coello C. Evolutionary Multi-Objective Optimization. European Journal of Operational Research, 2007.

[4] Chen L., Wei G. Application of TOPSIS Method for Oilfield Production Management Scheme Selection. Petroleum Storage & Transportation, Vol. 41, pp. 95–99, 2022.

[5] Chen W., Mei P. Optimization of Oil and Gas Field Development Using Multi-Objective Decision-Ideal Point Method. Journal of Petroleum and Natural Gas, Vol. 03, pp. 358-360+270, 2005.

[6] Chen Z., Wu Z., Liu J. An Integrated Fuzzy-AHP and Fuzzy-GRA Approach for Optimization of Development Schemes in a Tight Oilfield: A Case Study in China. Journal of Petroleum Science and Engineering, Vol. 175, pp. 38–53, 2019.

[7] Coello C., Pulido G.T., Lechuga M.S. Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, Vol. 8, pp. 256–279, 2004.

[8] Deb K., Pratap A., Agarwal S. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, Vol. 6(2), pp. 182–197, 2002.

[9] Durillo J.J., Garcia-Nieto J., Nebro A.J. Multi-Objective Particle Swarm Optimizers: An Experimental Comparison. Evolutionary Multi-Criterion Optimization, 2009.

[10] Feng Q., Sun T. Comprehensive Evaluation of Benefits from Environmental Investment: Take China as an Example. Environmental Science and Pollution Research, Vol. 27, pp. 15292–15304, 2020.

[11] Ge J., Zhao L. Optimal Planning and Decision-Making for Clustered Gas Field Development. In: Oil & Gas Field Development System Engineering Methods Collection (Vol. II), Beijing: Petroleum Industry Press, pp. 411–417, 1991.

[12] Guo X. Research on Risk-Based Multi-Criteria Decision Analysis Methods. Chemical Industry, Vol. 23, pp. 25–27, 2005.

[13] Khishvand M., Khamehchi E. Nonlinear Risk Optimization Approach to Gas Lift Allocation Optimization. Industrial & Engineering Chemistry Research, Vol. 51, pp. 2637–2643, 2012.

[14] Koopmans T.C. An Analysis of Production as an Efficient Combination of Activities. Analysis of Production and Allocation, Vol. 3, pp. 30–40, 1951.

[15] Li L., Zhai Y., Huang Z., Wang X. An Artificial Intelligence-Based Method for Optimizing Hydrocarbon Production Allocation. Journal of Petroleum Science and Engineering, Vol. 195, pp. 108–118, 2021.

[16] Li Y., Liu J., Wei L. Multi-Objective Optimization for Emergency Base Location of Oil Spill in Bohai Sea. Journal of Harbin Engineering University, Vol. 37, pp. 533-537+624, 2016.

[17] Lin W., Shi Y., Zhang X. Application of Grey Relational Analysis in Optimal Oilfield Development Scheme Selection. Petroleum & Natural Gas (Jianghan Petroleum Institute), Vol. 4, pp. 437–439, 2006.

[18] Omkar S.N., Mudigere D., Naik G.N. Vector Evaluated Particle Swarm Optimization (VEPSO) for Multi-Objective Design Optimization of Composite Structures. Computers & Structures, Vol. 86, pp. 1–14, 2018.

[19] Piroozfard H., Wong K.Y., Wong W.P. Minimizing Total Carbon Footprint and Total Late Work Criterion in Flexible Job Shop Scheduling Using an Improved Multi-Objective Genetic Algorithm. Resources Conservation and Recycling, Vol. 128, pp. 267–283.

[20] Reddy M.J., Kumar D.N. Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm. Water Resources Management, 2006.