Factors influencing methane diffusion behaviors in micro-nano coal pores: a comprehensive study

Abstract

Gas diffusion in the coal matrix plays a significant role in forecasting the production performance of coalbed methane (CBM) wells. To better understand methane diffusion behavior, a systematic study was performed on various rank coals with vitrinite reflectance (R_o,m) ranging from 0.46% to 2.79%. Multiple experiments, including coal petrographic analysis, field emission scanning electron microscopy (FESEM), low-temperature N₂ adsorption/desorption, and mercury intrusion porosimetry (MIP), were conducted to quantitatively characterize the multiscale micro-nano pore system in different rank coals, which showed that the pore structure of coals exhibited a multimodal pore size and volume distribution. Isothermal adsorption-diffusion experiments using the volumetric method were also performed to understand the methane diffusion characteristics in the micro-nano pores of the coal reservoir. The applicability of the multiporous diffusion model is verified, and methane diffusion in the multi-scale pores of coal reservoirs exhibits the characteristics of early fast diffusion, transitional diffusion in the medium term, and slow diffusion in the later period. In addition, the factors affecting methane diffusion in coals were systematically analyzed, and gray relational analysis (GRA) was employed to analyze and identify the importance of these factors on methane diffusion. The results show the impact ranking of factors, in order from the most important to the least: particle size > moisture > surface area > pore volume > pressure > coal rank > temperature in all of three diffusion stages. These findings are helpful for forecasting production performance and enhancing the production efficiency of CBM.

Graphical abstract

Keywords

coalbed methane reservoir / micro-characteristic / diffusion coefficient / grey relational analysis

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Supplementary material

is available in the online version of this article at http://dx.doi.org/10.1007/s11707-022-0992-3 and is accessible for authorized users.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant Nos. 41922016, 41830427, and 41772160), the Fundamental Research Funds for the Central Universities (No. 2652019264).

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