Area of research
Mechanics of Materials · Ocean Engineering
Research interest
Research topics from publications: Data-Driven Approach for the Prediction of In Situ Gas Content of Deep Coalbed Methane Reservoirs Using Machine Learning: Insights from Well Logging Data. Representative work: 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