Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

优化油井的位置和运行条件在油田的开发中起着重要的作用。近年来，不同的作者已经使用了几种自动优化算法。但是，根据问题的性质，不同的优化器会给出不同的结果。在当前的研究中，遗传算法和粒子群优化算法进行了比较，以优化注入和生产井的运行条件，并优化伊朗南部油田中注入井的位置。进行本研究的主要目的是评估和比较两种最常用的优化算法的性能，这些算法用于对实地数据进行田间开发优化。还，在决策变量的顺序优化和同时优化的结果之间进行了比较。将项目的净现值用作目标函数，并根据十二年中为项目增加的获利能力增量对两种算法进行了比较。首先，优化生产者的生产率，然后在通过工程判断确定的位置将水交替注气井添加到油田。然后，使用两种算法依次优化喷射器的位置，喷射速率和水交替气体比率。接下来，再次优化生产者的生产率。最后，以两种方式完成了同时优化，以评估其对优化结果的影响：最后两个步骤的同时优化和所有决策变量的同时优化。结果表明，该算法在每个阶段对项目的获利能力和粒子群优化算法均优于遗传算法。而且，与顺序优化方法相比，同步优化有助于更好地确定结果。最后，进行了敏感性分析，以指定对项目盈利能力影响最大的决策变量。与顺序优化方法相比，同步优化有助于更好地确定结果。最后，进行了敏感性分析，以指定对项目盈利能力影响最大的决策变量。与顺序优化方法相比，同步优化有助于更好地确定结果。最后，进行了敏感性分析，以指定对项目盈利能力影响最大的决策变量。