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A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System
Published on: June 12, 2019
Simulation of coal resistivity dynamics during methane adsorption and desorption using an electrical rock physics
Jiaqi Zou1, Shuangquan Chen2, Yuanji Li3
1National Key Laboratory of Petroleum Resources and Engineering, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum (Beijing), Beijing, 102249, China.
None:
Understanding the correlation between coal resistivity and methane content is critical for optimizing coalbed methane (CBM) recovery and ensuring mining safety. Existing studies mainly rely on empirical trend fitting, leaving a gap in model-driven analyses of resistivity dynamics during methane adsorption and desorption. This study develops a dual-coefficient electrical rock physics model integrating inorganic mineral composition, organic resistivity, methane adsorption-desorption behavior, and pore inclusion structures. Correction coefficients (0.2 for methane and 0.4 for organic resistivity) were introduced to address adsorption heterogeneity and structural complexity. Experimental validation on coal samples (density: 1.45 g/cm3, porosity: 5.5%) showed strong agreement between simulated and measured resistivity during adsorption (0.8882-3.6973 m3/t) and desorption (3.3974-2.1773 m3/t), with high correlation (R2 = 0.9815 adsorption, 0.9956 desorption; P-values = 0.9861, 0.9763). Sensitivity analysis revealed that mineral composition (e.g., quartz, clay) and inclusion aspect ratios (0-1) notably affect resistivity. Flattened inclusions (low aspect ratios) reduce resistivity more than spherical ones, especially at methane volumes lower than 0.15 m3/t. Organic content inversely correlates with resistivity; when the volume fraction exceeds 0.92, pore structure effects diminish. This work links microscopic adsorption mechanisms to macroscopic electrical properties, providing a predictive framework for CBM resource evaluation, CO2 storage monitoring, and coal mine hazard mitigation. The model adapts to diverse coal types and structural conditions, demonstrating broad applicability in research and industry.
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