Selection of machine learning algorithms in coalbed methane content predictions

Accurate prediction of coalbed methane (CBM) content plays an essential role in CBM development. Several machine learning techniques have been widely used in petroleum industries (e.g., CBM content predictions), yielding promising results. This study aims to screen a machine learning algorithm out of several widely applied algorithms to estimate CBM content accurately. Based on a comprehensive literature review, seven machine learning algorithms, i.e., deep neural network, convolutional neural network, deep belief network, deep & cross network (DCN), traditional gradient boosting decision tree, categorical boosting, and random forest, are implemented and tuned in this study. Well-logging (i.e., gamma ray, density, acoustic, and deep lateral resistivity) and coal-seam (i.e., moisture, ash, volatile matter, fixed carbon, cover depth, porosity, and thickness) properties are selected as the input features of the above machine learning models. Density-based spatial clustering of applications with a noise algorithm is implemented before the training process to identify outliers. Prediction results reveal that DCN is the best model in CBM content predictions (among the ones examined in this study), with a mean absolute percentage error of 3.7826%.

Automated summary

This study compares the performance of seven machine learning algorithms in predicting coalbed methane (CBM) content and finds that the deep & cross network (DCN) model is the best, with a mean absolute percentage error of 3.7826%.