Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing

TL;DR

This paper introduces a novel causal discovery and interpretability methodology for machine learning models predicting CBM well production after hydraulic fracturing, improving understanding and accuracy.

Contribution

It combines causal graph theory and SHAP analysis to enhance interpretability and capture nonlinear relationships, outperforming traditional methods in accuracy.

Findings

Detected causal relationships align with physical mechanisms.

Interpretable models improve prediction accuracy by 20%.

Method effectively captures nonlinear factor effects.

Abstract

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.