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

TL;DR

This paper introduces InterOpt, an interpretable machine learning-based algorithm for optimizing operational parameters in shale gas development, achieving significant cost reductions by customizing plans for individual wells.

Contribution

The paper presents a novel interpretable machine learning framework combining neural networks, feature impact analysis, and optimization for operational decision-making in shale gas extraction.

Findings

Achieved an average cost reduction of 9.7% in a case study with 104 wells.

Provided well-specific drilling and fracturing plans based on geological conditions.

Demonstrated the effectiveness of InterOpt in operational optimization.

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