Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing
水力压裂后煤层气井产量预测模型的可解释性与因果发现AI 解读 ↓
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水力压裂后煤层气井产量预测模型的可解释性与因果发现
- 研究问题
- 如何利用观测到的地质和工程因素，解释煤层气井压裂后的产量预测，并改善机器学习模型的泛化能力？
- 方法与思路
- 作者先通过因果发现构建含输入、输出、处理与混杂变量的因果图，再利用 SHAP 分析因素影响，解释产能预测中的非线性关系。
- 材料陈述的贡献
- 摘要称，识别出的关系与实际物理机制相符，可解释模型相对传统方法的预测准确性平均提高约百分之二十。
- 阅读关注点
- 应核对因果图的识别依据、混杂变量如何确定、SHAP 解释与因果效应的关系，以及准确性指标、比较基准和样本外验证设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核验；摘要未说明因果发现假设和准确性定义，物理机制一致性不足以单独验证因果识别。
原文摘要
Machine learning approaches are widely studied in the production prediction of CBM wells after hydraulic fracturing, but merely used in practice due to the low generalization ability and the lack of interpretability. A novel methodology is proposed in this article to discover the latent causality from observed data, which is aimed at finding an indirect way to interpret the machine learning results. Based on the theory of causal discovery, a causal graph is derived with explicit input, output, treatment and confounding variables. Then, SHAP is employed to analyze the influence of the factors on the production capability, which indirectly interprets the machine learning models. The proposed method can capture the underlying nonlinear relationship between the factors and the output, which remedies the limitation of the traditional machine learning routines based on the correlation analysis of factors. The experiment on the data of CBM shows that the detected relationship between the production and the geological/engineering factors by the presented method, is coincident with the actual physical mechanism. Meanwhile, compared with traditional methods, the interpretable machine learning models have better performance in forecasting production capability, averaging 20% improvement in accuracy.