Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Sonny Irawan (), Dennis Delali Kwesi Wayo, Alfrendo Satyanaga () and Jong Kim
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Sonny Irawan: Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana 010000, Kazakhstan
Dennis Delali Kwesi Wayo: Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana 010000, Kazakhstan
Alfrendo Satyanaga: Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan
Jong Kim: Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan

Energies, 2024, vol. 17, issue 9, 1-14

Abstract: Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG.

Keywords: well deployment; genetic algorithms; CMG; global optimization; reservoir (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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