Data-Driven Approach for the Prediction of In Situ Gas Content of Deep Coalbed Methane Reservoirs Using Machine...

The in situ gas content is a critical determinant of the exploitation potential and recovery of coalbed methane (CBM) resources. Deep CBM resources have enormous exploitation potential, but their intricate geological conditions hinder the acquisition of in situ gas content data. To enhance the efficiency and accuracy of acquiring in situ gas content data for deep CBM, this study integrates gray relational analysis (GRA) and the genetic algorithm (GA) into the back-propagation neural network (BPNN) model, establishing a novel prediction model for in situ gas content of deep CBM using well logging data. The results show that the multialgorithm joint model can overcome the inherent shortcomings of the BPNN. The GRA method effectively identifies the optimal input parameters for the BPNN model, the GA method optimizes the initial weights and thresholds of the BPNN, thereby enhancing the prediction accuracy and stability of the model. The mean square error (MSE) of the GRA-GA-BPNN joint model decreases by 77.60% compared with the BPNN model. Furthermore, taking the deep CBM wells in the Ningwu Basin of North China as an example, the reliability of the multialgorithm joint model was verified (4.62% average relative error only). The GRA-GA-BPNN model proposed in this study exhibits high robustness and strong generalization ability. It can achieve high-precision prediction of deep CBM in situ gas content, thereby circumventing overreliance on experimental measurements, holding significant practical application significance.