  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.
