Abstract Coal mines may change from non-outburst mines into coal and gas outburst mines with increasing mining depth. Therefore, scientific and rapid prediction of the coal seam outburst risk and effective prevention and control measures could ensure coal mine safety and production. This study aimed to propose a solid–gas–stress coupling model and assessed its applicability in predicting the coal seam outburst risk. Based on a large amount of outburst case data and the research results of previous scholars, coal and coal seam gas constitute the material basis of outbursts, and gas pressure is the energy source of coal seam outbursts. A solid–gas–stress coupling model was proposed, and a solid–gas–stress coupling equation was established via regression. Among the three major outburst factors, the sensitivity to the gas content during outbursts was the lowest. The causes of coal seam outbursts with a low gas content and the effect of the structure on outbursts were explained. It was theoretically revealed that the coupling of the coal firmness coefficient, gas content and gas pressure determined whether coal seams could experience outbursts. This paper provided a basis for assessing coal seam outbursts and classifying outburst mine types and listed application examples of solid–gas–stress theory. 

/pdf/study-of-the-solid-gas-stress-coupling-model-and-its-836gh0r2.pdf

Study of the solid–gas–stress coupling model and its application

To study the influence of small faults on the gas content of Coal Seam 3# in the Changping mine field, the influence scope and degree of small faults on Coal Seam 3# and gas content in the Changping mine field are analyzed based on the field measured data such as the gas content on both sides of the fault, the initial speed of gas emission, and the solidity coefficient of the coal seam, combined with the FLAC3D stress simulation results, and the influence area of small faults is zoned from the perspective of gas control. The analysis results show that the measured gas content in the hanging wall of SF250 fault with a drop of 1.3 m and SF353 fault with a drop of 1.9 m is 7.14 and 9.47 m3/t, respectively, and the gas content in the footwall is 5.29 and 7.41 m3/t, respectively. The gas content in the hanging wall is obviously higher than that in the footwall; the gas content in the coal seam near the small fault surface is slightly lower. With the increase of the distance from the fault surface, the gas content in the coal seam decreases first and then increases; the firmness coefficients of hanging wall coal of small fault are 0.40 and 0.45, respectively, and those of footwall coal are 0.73 and 0.75, respectively. The firmness coefficient of hanging wall coal seam is obviously smaller than that of footwall coal seam. The closer it is to the fault surface, the smaller the firmness coefficient of coal is, and the greater the initial gas release speed is; the permeability coefficient of the coal seam far from the fault surface in the hangingwall of the small fault shows a decreasing trend after an increasing trend. The maximum stress of the coal seam is 21.85 MPa at 14 m from the hangingwall of the fault to the fault, and the minimum stress of the coal seam is 2.79 MPa at 1 m from the footwall of the fault to the fault. The stress of the hangingwall of the fault is greater than that of the footwall of the fault, and the stress concentration area is 14 m from the hangingwall of the fault to the fault.

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c00284

Study on the Effect of Small Faults on the Gas Content in No. 3 Coal Seam of the Changping Mine Field

The formation and development of faults are a manifestation of stress concentrations and energy release in crustal rocks, and faults have a great influence on the occurrence of dynamic mine disasters. This study proposed mapping technology as a fault identification method. The mapping procedure included four steps: (1) Drawing points on topographic maps. The information of topographic maps, such as elevation, rivers, and lakes was copied onto sulfuric acid paper. (2) Classifying the landform. Based on the highest and lowest points in the studied area, the minimum elevation difference was calculated and the elevation points were graded according to the minimum elevation difference. (3) Determining the block boundaries. The elevation points in the same grade were categorized into the same blocks. (4) Mapping the fault distribution. The boundaries between different blocks were considered as faults. In this regard, numbers were assigned to the faults, and a graphic scale, coordinate grid, and legends were added to the map. Fault identification for classes I–V was conducted at different scales. Fault identification for the next class always retained the previous results. Using this method, the faults in the Pingdingshan coal mining zone were divided into classes I–V. By comparing the classes with historical coal and gas outbursts, it was indicated that more than 90% of the coal and gas outbursts occurred in the vicinity of faults, especially at the intersections of multiple faults and in areas with concentrated faults. This study provided a scientific basis for predicting coal and gas outbursts.

/pdf/fault-identification-approach-and-its-application-for-26zrw68bt4.pdf

Xiufeng Zhang

Papers

Fault identification approach and its application for predicting coal and gas outbursts