  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 achieved an
average cost reduction of 9.7% for a case study with 104 wells.
