Coal permeability is one of the most critical parameters affecting gas flow behavior during coalbed methane (CBM) production. However, little research has been conducted on how permeability evolves after CO₂ injection in coking coal. Hence, examining possible chemical interactions between coal minerals, water, and injected CO₂ can be very helpful to better characterize coking coal. In this study, coking coal specimens obtained from the Malan and Tunlan mines located in the Gujiao block of the Qinshui basin were treated with water and CO₂ to achieve a better understanding of their dissolution kinetics, pore structure, and permeability. It was found that the relative carbonate mineral content decreases with time, while the relative clay mineral content increases after the reaction with CO₂ and water. Scanning electron microscopy (SEM) confirmed these mineral alteration phenomena. Carbonate minerals (calcite, dolomite) dissolve faster than clay minerals (montmorillonite, illite and kaolinite). In particular, the dissolution rates of Ca²⁺ in carbonate minerals increases with decreasing temperature (25–45 °C) and pH (4.3–6.3), and the dissolution rate of Ca²⁺ ions in the calcite reaction solution is higher than that in the dolomite solution. In addition, the results of low-pressure nitrogen adsorption analysis showed that CO₂ injection can enlarge smaller size pores into larger size pores and change the overall pore size distribution. Therefore, CO₂ injection can increase the porosity of coal beds and ultimately their permeability, which in turn facilitates CBM production.

The effect of different acids on the pore structure and fractal characteristics of micropores and mesopores was determined with the help of low-temperature liquid nitrogen adsorption, X-ray diffraction, and the Frenkel–Halsey–Hill (FHH) model by using Yuwu coal as a sample and placing it in acidic environments, such as HF, HCl, HNO₃, and CH₃COOH. The results show that the acidization effects of HF and CH₃COOH are separately dominated by the micropore and mesopore formation effects, while HCl and HNO₃ mainly play their roles in expanding mesopores. After acidization, the surface fractal dimensions D₁ and D₁′ of micropores and mesopores in coal are always negatively correlated with the total specific surface area S_BET, specific surface area S_mic of micropores, and specific surface area S_mes of mesopores. After being acidized by HF, D₂ is negatively correlated with the total volume V_tot and the corresponding micropore volume V_mic, while acidization with HCl and HNO₃ leads to the opposite result. After being acidized by CH₃COOH, D₂ has a negative correlation with V_tot and a positive correlation with V_mic. The structural fractal dimensions D₂′ of mesopores in samples acidized by HF and CH₃COOH are positively correlated with both the volume V_tot and mesopore volume V_mes, while it is the opposite for samples acidized by HNO₃. D₂′ of coal samples acidized by HCl is negatively correlated with V_tot while positively correlated with V_mes.

Coal structure directly correlates to permeability and hydraulic fracturing effects. Underground coal mining indicates that a single coal section generally contains multiple coal structures in superposition, making how to recognise the coal structure combination and predict its influence on coal permeability a challenging problem. Based on well-drilling sampled cores, the geological strength index (GSI), and well-logging data, the DEN, GR, CALX, and CALY were selected to establish a model to predict GSI by multiple regression to identify coal structure from 100 coalbed methane wells. Based on fitting GSI and corresponding permeability test values, injection fall-off (IFO) testing, and hydraulic fracturing results, permeability prediction models for pre- and post-fracturing behaviour were established, respectively. The fracturing effect was evaluated by the difference in permeability. The results show that a reservoir can be classified into one of nine types by different coal structure thickness proportion (and combinations thereof) and the fracturing curves can be classified into four categories (and eight sub-categories) by the pressure curve. Up-down type I and type II reservoirs (proportion of hard coal >60%) and intervening interval type I reservoir (proportion of hard coal >70%) are prone to form stable and descending fracturing curves and the fracturing effects are optimal. Intervening interval type II (hard coal:soft coal:hard coal or soft coal:hard coal:soft coal ≈1:1:1) and up-down type III (hard coal:soft coal =1:1) form descending type II, rising type I and fluctuating type I fracturing curves and fracturing effect ranks second; up-down type IV and V (proportion of hard coal <40%), interval type III (proportion of hard coal <30%), and multi-layer superposition-type reservoirs readily form fluctuating and rising fracturing curves and fracturing effects therein are poor. The research results provide guidance for the targeted stimulation measured under different coal structure combinations.