Donglin Lin

Area of research

Mechanics of Materials · Environmental Chemistry

Research interest

Research topics from publications: Dynamic evolution and differential enrichment of deep coalbed methane: A case study in Qinshui Basin; Depositional controlling factors on pore distribution and structure in the lower Silurian Longmaxi shales: Insight from geochemistry and petrology; Organic matter enrichment in shale deposited proximal to paleo-uplifts and its impact on shale gas exploration; Enrichment mechanism of organic matter and silicon in lower Cambrian shale of the Yangtze Platform; Grain assemblages and diagenesis in Ordovician-Silurian transition shale deposits of the Upper Yangtze Platform, South China; 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

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