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Hydraulic fracturing is extensively utilized for the prevention and control of gas outbursts and rockbursts in the deep sections of coal mines. The determination of fracturing construction parameters based on the coal seam conditions and stress environments merits further investigation. This paper constructs a damage analysis model for coal under hydraulic loads, factoring in the influence of the intermediate principal stress, grounded in the octahedron strength theory analysis approach. It deduces the theoretical analytical equation for the damage distribution of a coal medium subjected to small-flow-rate hydraulic fracturing in underground coal mines. Laboratory experiments yielded the mechanical parameters of coal in the study area and facilitated the fitting of the intermediate principal stress coefficient. Leveraging these datasets, the study probes into the interaction between hydraulic loads and damage radius under assorted influence ranges, porosity, far-field crustal stresses, and brittle damage coefficients. The findings underscore that hydraulic load escalates exponentially with the damage radius. Within the variable range of geological conditions in the test area, the effects of varying influence range, porosity level, far-field stress, and brittle damage coefficient on the outcomes intensify one by one; a larger hydraulic load diminishes the impact of far-field stress variations on the damage radius, inversely to the influence range, porosity, and brittle damage. The damage radius derived through the gas pressure reduction method in field applications corroborates the theoretical calculations, affirming the precision of the theoretical model. These findings render pivotal guidance for the design and efficacy assessment of small-scale hydraulic fracturing in underground coal mines. [ABSTRACT FROM AUTHOR]

Vibrations caused by downhole operations often induce coal and gas outburst accidents in tectonic zone coal seams. To clarify how vibration affects the pore structure, gas desorption, and diffusion capacity of tectonic coal, isothermal adsorption-desorption experiments under different vibration frequencies were carried out. In this study, high-pressure mercury intrusion experiments and low-pressure liquid nitrogen adsorption experiments were conducted to determine the pore structures of tectonic coal before and after vibration. The pore distribution of vibration-affected tectonic coal, including local concentration, heterogeneity, and connectivity, was analyzed using multifractal theory. Further, a correlation analysis was performed between the desorption diffusion characteristic parameters and the pore fractal characteristic parameters to derive the intrinsic relationship between the pore fractal evolution characteristics and the desorption diffusion characteristics. The results showed that the vibration increased the pore volume of the tectonic coal, and the pore volume increased as the vibration frequency increased in the 50 Hz range. The pore structure of the vibration-affected tectonic coal showed multifractal characteristics, and the multifractal parameters affected the gas desorption and diffusion capacity by reflecting the density, uniformity, and connectivity of the pore distribution in the coal. The increases in the desorption amount (Q), initial desorption velocity (V0), initial diffusion coefficient (D0), and initial effective diffusion coefficient (De) of the tectonic coal due to vibration indicated that the gas desorption and diffusion capacity of the tectonic coal were improved at the initial desorption stage. Q, V0, D0, and De had significant positive correlations with pore volume and the Hurst index, and V0, D0, and De had negative correlations with the Hausdorff dimension. To a certain extent, vibration reduced the local density regarding the pore distribution in the coal. As a result, the pore size distribution was more uniform, and the pore connectivity was improved, thereby enhancing the gas desorption and diffusion capacity of the coal. [ABSTRACT FROM AUTHOR]

The coal and gas outburst and rockburst coupling disaster is becoming increasingly serious due to deep mining. To clarify the mechanism inducing the outburst–rockburst coupling disaster, a true triaxial single-sided unloading mechanical test was conducted with the aid of a true triaxial solid–thermal–gas coupling test device, an industrial computed tomography (CT) system, and an acoustic emission system. Through this test, the mechanical characteristics, meso crushing characteristics, and acoustic characteristics in the disaster formation process were obtained. Additionally, the outburst–rockburst coupling instability disaster law was verified by numerical simulation. The results demonstrated that the stress unloading degree of the coal body was negatively correlated with the initial gas pressure in the outburst–rockburst coupling disaster. The time domain parameter count and energy of acoustic emission exhibited a "bimodal" distribution pattern in the instability stage. The rockburst would occur when the peak value was in a "low-count and high-energy" state, while coal and gas outburst would occur when the peak value was in a "high-count and low-energy" state. The meso slice revealed that gas degradation promoted the development of microcracks in the coal body, and the penetration of cracks resulted in the main cracks of structural instability during rockburst. The coal and gas outburst was mainly attributed to the "cross" shear failure pattern of the coal body. These research findings may lay a foundation for the effective prevention and control of outburst–rockburst coupling disasters. [ABSTRACT FROM AUTHOR]

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations. [ABSTRACT FROM AUTHOR]

China's coal mines have to extend to greater depths for the exploitation of more mineral resources, and they have suffered catastrophic mining-induced disasters, such as rockbursts, water inrushes, coal and gas outbursts, and roof fall accidents. The microseismic monitoring technique is a practical tool for mine safety management, which is extensively utilized in many Chinese coal mines. Microcracks of coal/rock masses are recorded as microseismicities in the field, and the potential mining-induced instabilities can be assessed by in-depth analysis of the microseismic parameters. This study provides a state-of-the-art review of the achievements and developments of the microseismic monitoring technique in coal mines. It also presents some prospects for improving the location accuracy of microseismicity, efficient and intelligent processing of the microseismic data, comprehensive assessment of coal/rock instabilities, and development of new microseismic monitoring equipment. This study is valuable for mine safety management and may contribute to improving the deep mining production. [ABSTRACT FROM AUTHOR]

The gas contained in coal plays a crucial role in triggering coal and gas outbursts. During an outburst, a large quantity of gas originally absorbed by coal is released from pulverized coal. The role this part of the gas plays in the process of coal and gas outbursts has not been clearly elucidated yet. Therefore, investigating the changes in gas desorption rate from coal particles of different sizes could provide some meaningful insights into the outburst process and improve our understanding of the outburst mechanism. First, combining the diffusivity of coal of different particle sizes and the distribution function of broken coal, we present a gas desorption model for fragmented gas-bearing coal that can quantify gas desorption from coal particles within a certain range of size. Second, the gas desorption rate ratio is defined as the ratio of the gas desorption rate from coal being crushed to that from coal before breaking. The desorption rate ratio is mainly determined by the desorption index (γ) and the granularity distribution index (α). Within the limit range of coal particle sizes, the ratio of effective diffusion coefficient for coal particles with different sizes is directly proportional to the reciprocal of the ratio of particle sizes. Under uniform particle size conditions before and after fragmentation, the gas desorption rate ratio is the square root of the reciprocal of the effective diffusion coefficient. The gas desorption model quantitatively elucidates the accelerated desorption of adsorbed gas in coal during the continuous fragmentation process of coal during an outburst. [ABSTRACT FROM AUTHOR]