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

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 hydraul...

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Bibliographic Details
Published in:Petroleum science 2023-06, Vol.20 (3), p.1788-1805
Main Authors: Chen, Yun-Tian, Zhang, Dong-Xiao, Zhao, Qun, Liu, De-Xun
Format: Article
Language:English
Subjects:
Interpretable machine learning
Neural network
Operational parameters optimization
Shale gas development
Shapley value
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Summary: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.
ISSN:1995-8226
1995-8226
DOI:10.1016/j.petsci.2022.12.017