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
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…