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An algorithm named InterOpt for optimizing operational parameters is proposedbased on interpretable machine learning, and is demonstrated via optimizationof shale gas development. InterOpt consists of three parts: a neural network isused to construct an emulator of the actual drilling and hydraulic fracturingprocess in the vector space (i.e., virtual environment); the Sharpley valuemethod in interpretable machine learning is applied to analyzing the impact ofgeological and operational parameters in each well (i.e., single well featureimpact analysis); and ensemble randomized maximum likelihood (EnRML) isconducted to optimize the operational parameters to comprehensively improve theefficiency of shale gas development and reduce the average cost. In theexperiment, InterOpt provides different drilling and fracturing plans for eachwell according to its specific geological conditions, and finally achieved anaverage cost reduction of 9.7% for a case study with 104 wells.

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