Optimizing multistage fractured horizontal wells (MFHW) can tap the full potential of tight oil reservoirs. Although recent studies have introduced various frameworks, most of the significant parameters for MFHW are not optimized simultaneously, which may lead to actual performance that is far below expected performance, especially in heterogeneous reservoirs. Here, we present an efficient optimization framework that couples embedded discrete fracture model (EDFM) and intelligent algorithms to maximize net present value. We also examined the performance of four optimization algorithms in our model: genetic algorithm (GA), multilevel coordinate search (MCS), covariance matrix adaptation evolution strategy (CMA-ES), and generalized pattern search (GPS). Our results suggest that because CMA-ES handles MFHW optimization robustly and effectively, it may be utilized in future applications. Our framework serves as an efficient tool to optimize MFHW design, which plays an increasingly significant role in the enhancement of tight oil recovery.

优化多级压裂水平井（MFHW）可以充分利用致密油藏的全部潜力。尽管最近的研究引入了各种框架，但是MFHW的大多数重要参数并未同时优化，这可能导致实际性能远远低于预期性能，尤其是在非均质油藏中。在这里，我们提出了一个有效的优化框架，该框架结合了嵌入式离散裂缝模型（EDFM）和智能算法，以最大化净现值。我们还检查了模型中四种优化算法的性能：遗传算法（GA），多级坐标搜索（MCS），协方差矩阵适应进化策略（CMA-ES）和广义模式搜索（GPS）。我们的结果表明，由于CMA-ES可以有效地处理MFHW优化，它可能会在将来的应用程序中使用。我们的框架可作为优化MFHW设计的有效工具，在提高致密油采收率方面起着越来越重要的作用。