Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development  

作　　者：Yun-Tian Chen Dong-Xiao Zhang Qun Zhao De-Xun Liu 

机构地区：[1]Eastern Institute for Advanced Study,Zhejiang,315200,China [2]Department of Mathematics and Theories,Peng Cheng Laboratory,Guangdong,518055,China [3]National Center for Applied Mathematics Shenzhen(NCAMS),Southern University of Science and Technology,Guangdong,518055,China [4]Research Institute of Petroleum Exploration and Development,CNPC,Bejing,100083,China

出　　处：《Petroleum Science》2023年第3期1788-1805,共18页石油科学（英文版）

摘　　要：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 inter-pretable 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.

关 键 词：Interpretable machine learning Operational parameters optimization Shapley value Shale gas development Neural network 

分 类 号：TE311[石油与天然气工程—油气田开发工程]