Coalbed methane content prediction using deep belief network

Abstract

Accurate measurement of coalbed methane (CBM) content is the foundation for CBM resource exploration and development. Machine-learning techniques can help address CBM content prediction tasks. Due to the small amount of actual measurement data and the shallow model structure, however, the results from traditional machine-learning models have errors to some extent. We have developed a deep belief network (DBN)-based model with the input as continuous real values and the activation function as the rectified linear unit. We first calculated a variety of seismic attributes of the target coal seam to highlight the features of the coal seam, then we preprocessed the original attribute features, and finally developed the performance of the DBN model using the preprocessed features. We used 23,374 training data to train our model, 23,240 for pretraining, and 134 for fine-tuning. For the purpose of demonstrating the advantages of the DBN model, we compared it with two typical machine-learning models, including the multilayer perceptron model and the support vector regression model. These two models were trained based on the same labeled training data. The results, obtained from different models, indicated that the DBN model has the least error, which means that it is more accurate than the other two models when used to predict CBM content.