论文标题
可解释的机器学习优化（InterOPT）用于操作参数：高效页岩气体开发的案例研究
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development
论文作者
论文摘要
基于可解释的机器学习提出了一种名为InterOPT优化操作参数的算法，并通过优化页岩气发展来证明。 InterOpt由三个部分组成：一个神经网络用于构建矢量空间中实际钻孔和液压压裂过程的模拟器（即虚拟环境）；可解释的机器学习中的Sharpley价值方法用于分析每个井中地质和操作参数的影响（即单个井特征影响分析）；并进行集合随机最大似然（ENRML）以优化操作参数，以全面提高页岩气发展的效率并降低平均成本。在实验中，InterOpt根据其特定地质条件为每个井提供了不同的钻孔和破裂计划，并最终在104个井的案例研究中达到了9.7％的平均成本降低9.7％。
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.
