Fast and accurate calculation on CO2/CH4 competitive adsorption in shale nanopores: From molecular kinetic theory to machine learning model

MC Huang and H Yu and HY Xu and HL Zhang and XY Hong and HA Wu, CHEMICAL ENGINEERING JOURNAL, 474, 145562 (2023).

DOI: 10.1016/j.cej.2023.145562

Understanding the competitive adsorption behavior of CO2 and CH4 in shale nanopores is crucial for enhancing the recovery of shale gas and sequestration of CO2, which is determined by both the inherent characteristics of the molecules and external environmental factors such as pore size, temperature, and partial pressures of CO2 and CH4. While the competitive adsorption behavior of CO2/CH4 has been analyzed by previous studies, a comprehensive understanding from the perspective of molecular kinetic theory and the efficient calculation for competitive adsorption behavior considering various geological situations is still challenging, limited by the huge computation cost of classical molecular dynamics (MD) methods. In this work, the theoretical connection between inherent characteristics of molecules and adsorption behavior is firstly built to reveal the general laws in the behavior of CO2/CH4 competitive adsorption through posture analysis of the molecules. A machine learning algorithm, aided by molecular kinetic theory, is proposed to facilitate the fast and accurate predictions of competitive adsorption behavior, and detailed analyses of the influencing factors are conducted accordingly. The insights gained from this work provide a foundation for expeditiously optimizing the competitive adsorption behavior of CO2/CH4, with potential implications for CO2 sequestration and enhanced gas recovery (CSEGR) process.

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