Review of the adsorption equilibria of CO2, CH4, and their mixture on coals and shales at high pressures for enhanced CH4 recovery and CO2 sequestration

• Published in: Fluid phase equilibria Vol. 564; p. 113591
• Main Authors: Jeong, Seo Ryung, Park, Jung Hyeok, Lee, Jun Hyeong, Jeon, Pil Rip, Lee, Chang-Ha
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: Adsorption; Carbon dioxide; Coal; Machine learning; Methane; Shale; Methane; Adsorption; Carbon dioxide; Coal; Shale; Machine learning
• ISSN: 0378-3812; 1879-0224
• DOI: 10.1016/j.fluid.2022.113591

Producing CH4 from coal beds and shale reservoirs is an attractive unconventional energy option. Injecting CO2 into a coal or shale basin can extract more CH4 and enable the permanent sequestration of CO2, and this is called CO2-enhanced coal bed methane (CO2-ECBM) or CO2-enhanced shale gas recovery (CO2-ESGR), respectively. Since the injection of captured CO2 into geological formations (geological CO2 sequestration, GCS) is considered a feasible strategy for CO2 sequestration, CO2-ECBM and CO2-ESGR have received great attention for the mitigation of global warming as well as energy recovery. Coals and shales, which are porous solids mainly containing carbons with silica-based materials, can adsorb a large amount of CO2 and CH4 depending on their pore structures/geochemical properties. Therefore, to design and practice CO2-ECBM and CO2-ESGR, it is important to understand the adsorption of CO2 and CH4 on coals and shales. A large number of laboratory experiments have been conducted to estimate the adsorption capacity of CO2 and CH4 with the structural characterization of coals and shales. However, the heterogeneous properties of coals and shales make it difficult to develop a prediction model for various types of coals and shales. This study reviews the present state of the adsorption of CO2, CH4, and their mixture on coals and shales and suggests a future research direction. The experimental results of adsorption on coals and shales from the literature are introduced, and the properties of coals and shales are discussed to understand their influence on adsorption. Then, thermodynamic models of the adsorption are analyzed to elucidate the correlation between the thermodynamic parameters and structural properties of coals and shales. Applications of machine learning (ML) models, which have received significant attention recently, to the prediction of CO2/CH4 adsorption on coals and shales are also reviewed. Finally, the combination of the thermodynamic model and the ML model, known as a hybrid model, is discussed to develop a universal prediction model for CO2/CH4 adsorption on various types of coals and shales.