Abstract
Optimizing oil and gas production is of paramount importance in the petroleum sector, as it ensures the economic success of oil companies and meets the growing global demand for energy. The optimization of subsurface oil and gas production is critical for decision-makers, as it determines essential strategies like optimal well placement and well control parameters. Traditional reservoir production optimization methods often involve high computational costs and difficulties in achieving effective optimization. Evolutionary algorithms, inspired by biological evolution, have proven to be powerful tools for solving complex optimization challenges due to their independence from gradient information and efficient parallel processing capabilities. This paper proposes a highly efficient evolutionary algorithm for global optimization and oil and gas production optimization by enhancing the optimization performance of fruit fly optimization algorithm (FOA) through multi-swarm mechanism and greedy selection mechanism, which balance the algorithm's search and development capabilities. Specifically, after updating the population of FOA, we first apply multi-swarm mechanism to help the population escape local optima and improve the algorithm's search ability, and then apply greedy selection mechanism to enhance the population's development potential. To verify the optimization performance of MGFOA, we conducted comprehensive experimental validations at IEEE CEC 2017 and IEEE CEC 2022, including ablation studies, scalability experiments, search trace visualizations, and comparisons with other similar algorithms. Finally, MGFOA significantly outperformed other comparable algorithms in oil and gas production optimization.