علی رنجبر

Well placement optimization is a crucial issue in the oil and gas industry, aiming to enhance efficiency and reduce costs. In this study, we investigate research conducted on optimizing the placement of production and injection wells using optimization algorithms. This study was carried out in order to observe how to use optimization algorithms, their relative advantages and disadvantages, as well as the use of input parameters that help in the optimal location of production and injection wells using machine learning. In this regard, general optimization methods used in the articles are presented. Then, effective parameters, used algorithms, and library science metrics are introduced, and finally, previous studies are reviewed, as well as discussion and conclusions. Analyzes show that PSO and GA methods are the most popular and efficient methods among researchers. Research results indicate that PSO and GA algorithms can achieve high efficiency in optimizing well placements in the oil and gas industry. These improvements can lead to increased oil production, cost reduction, and enhanced resource efficiency. This article describes the principles and methods of using these algorithms for optimizing the placement of wells and presents an evaluation of the practical results.