Petroleum Science >2023,??Issue3:??- DOI: https://doi.org/10.1016/j.petsci.2022.12.017
Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development Open?Access
文章信息
作者：Yun-Tian Chen, Dong-Xiao Zhang, Qun Zhao, De-Xun Liu
作者單位：
投稿時(shí)間：
引用方式：Yun-Tian Chen, Dong-Xiao Zhang, Qun Zhao, De-Xun Liu, Interpretable machine learning optimization (InterOpt) for operational parameters: A case study of highly-efficient shale gas development, Petroleum Science, Volume 20, Issue 3, 2023, Pages 1788-1805, https://doi.org/10.1016/j.petsci.2022.12.017.
文章摘要
Abstract: An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development. InterOpt consists of three parts: a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space (i.e., virtual environment); the Sharpley value method in interpretable machine learning is applied to analyzing the impact of geological and operational parameters in each well (i.e., single well feature impact analysis); and ensemble randomized maximum likelihood (EnRML) is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost. In the experiment, InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions, and finally achieves an average cost reduction of 9.7% for a case study with 104 wells.
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Keywords: Interpretable machine learning; Operational parameters optimization; Shapley value; Shale gas development; Neural network