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Fast and Accurate Calculation on Competitive Adsorption Behavior in Shale Nanopores by Machine Learning Model

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1 Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, China

* Corresponding Author: Hao Yu. Email:

The International Conference on Computational & Experimental Engineering and Sciences 2024, 30(2), 1-1. https://doi.org/10.32604/icces.2024.011120

Abstract

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 [1], 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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