Understanding the influence of coal composition on pore structure during thermal evolution is fundamental for elucidating the patterns of pore development and is essential for evaluating deep coalbed methane resources as well as identifying favorable target zones. This study analyzed deep coal samples of varying thermal maturity from the main strata of the Ordos Basin using basic property analysis, mercury intrusion porosimetry (MIP), low-temperature N2 adsorption (LT-N2A), and low-pressure CO2 adsorption (LP-CO2A) to comprehensively quantify the multiscale pore structures. Additionally, the fractal characteristics of the pore structure were investigated. The results indicate that, as thermal maturity increases, vitrinite content decreases while inertinite becomes enriched, fixed carbon initially declines then rises, volatile matter steadily decreases, and ash content relatively increases. Pore structure analysis reveals a negative correlation between the content of active components (vitrinite, volatile matter) and total pore volume, while inert components (inertinite, fixed carbon) correlate positively. Based on this component-pore relationship, a Pore Development Index (α) and a total pore volume fitting model were developed as quantitative indicators for reservoir evaluation. Fractal analysis reveals distinct fractal characteristics across all pore scales. Pore parameters significantly correlate with fractal dimension: the positive correlation between pore volume/specific surface area and fractal dimension in micropores and mesopores; the negative correlation for macropore pore volume. Specifically, the macropore fractal dimension decreases after the maximum vitrinite reflectance (Ro, max) of 1.9%, whereas the fractal dimensions of micropores and mesopores continuously increase. Furthermore, the content of macerals and volatile matter exerts a controlling influence on the fractal dimensions of micropores and mesopores. This study elucidates the multiscale evolution and compositional control of deep coal pore systems, providing an index and model to support reservoir quality assessment and sweet-spot prediction in deep coalbed methane exploration.
Yang et al. (Mon,) studied this question.