Journal
ENERGIES
Volume 10, Issue 4, Pages -Publisher
MDPI AG
DOI: 10.3390/en10040527
Keywords
gas permeability; granular coal; porosity; stress rate; grain size distribution; grain crushing
Categories
Funding
- National Basic Research Program of China [2015CB060200]
- National Natural Science Foundation of China [51322403, 51674247]
- Fundamental Research Funds for the Central Universities [2015XKZD06]
- Natural Environment Research Council [bgs05017] Funding Source: researchfish
- NERC [bgs05017] Funding Source: UKRI
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Coal seam gas, held within the inner pores of unmineable coal, is an important energy resource. Gas release largely depends on the gas seepage characteristics and their evolution within granular coal. To monitor this evolution, a series of experiments were conducted to study the effects of applied compressive stress and original grain size distribution (GSD) on the variations in the gas seepage characteristics of granular coal samples. Grain crushing under higher stress rates was observed to be more intense. Isolated fractures in the larger diameter fractions transformed from self-extending to inter-connecting pathways at a critical compressive stress. Grain crushing was mainly caused by compression and high-speed impact. Based on the test results of the original GSD effect, the overall process of porosity and permeability evolution during compression can be divided into three different phases: (1) rapid reduction in the void ratio; (2) continued reduction in the void ratio and large particle crushing; and (3) continued crushing of large particles. Void size reduction and particle crushing were mainly attributed to the porosity and permeability decreases that occurred. The performance of an empirical model, for porosity and permeability evolution, was also investigated. The predictive results indicate that grain crushing caused permeability increases during compression, and that this appeared to be the main cause for the predictive values being lower than those obtained from the experimental tests. The predictive accuracy would be the same for samples under different stress rates and the lowest for the sample with the highest proportion of large grain diameters.
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