Search

Abstract Determination of optimum well location and operational settings for existing and new wells is crucial for maximizing production in field development. These optimum conditions depend on geological and petrophysical factors, fluid flow regimes, and economic variables. However, conducting numerous simulations for various parameters can be time-consuming and costly. Also, due to the high dimension of the possible solutions, there is still no general approach to address this problem. The application of searching algorithm as a general approach to solve such problems has received much attention in recent years. In this study, the efficiency, and reliability of genetic algorithm, particle swarm optimization and in particular a newly developed algorithm was analyzed and compared. The novelty of this work is the integrated algorithm, which improves searching performance by leveraging the memorizing characteristics of the particle swarm optimization algorithm to enhance genetic algorithm efficiency. In traditional genetic algorithms, solutions lacking adequate qualifications are deleted from the algorithmic process; however, the new algorithm provides these solutions with additional opportunities to prove themselves by acquiring new velocities from particle swarm optimization. The results indicate that while the genetic algorithm and particle swarm optimization do not guarantee optimal outcomes, the newly developed algorithm outperforms both methods. This performance was tested across various scenarios focused on well pattern optimization, highlighting its innovative contribution to the field development.