RESUMO
To explore the multiparameter precursor characteristics of pre- and post-coal burst. Based on a coal burst of LW 1305 in the Zhaolou Coal Mine, an early warning method combining stressâstrain curve and microseismic multiparameter is proposed. The research results show that coal burst was induced by the intrinsic static high-stress concentration and the strong external impact loading generated by fracturing of the key stratum. The precursors mainly characterize the enhancement trend of the S value, the sudden and sharp increase in the A(t) value, the continuous and abnormal decrease in the b value, the increasing absolute value of Z sharply and larger than 2, the continuous and abnormal decrease in the Qt value, and the dominant frequency moving to the low-frequency band. Essentially, many micro-fissures inside the key stratum initiated, converged and connected to form macro-fractures, which was verified by the attenuation rate of the K value. Considering the time-varying effect of the overlying stratum movement, the curves of the six parameters agree well with those of stress vs. strain, which indicates that it is reasonable to take the observed zone as a whole system to investigate the variation in the multiple parameters and fracturing of the key stratum. The research results can be applied to the monitoring, early warning and control of coal burst so that effective safety measures can be taken in real time.
RESUMO
This study explores the causes of coal bursts in the Xinzhou Kiln Mine, identifying key factors such as residual pillars, hard coal seams and/or roofs, stress concentration due to complex geological structures, and the stress distribution characteristics of the primary rock. A significant finding is that hydraulic cutting not only diminishes and redistributes the stress concentration region inside the coal seam but also mitigates the burst potential of the coal-rock mass, fundamentally reducing the likelihood of coal bursts. By taking Face No. 8937 in Xinzhou Kiln Mine as the test object, a coal burst prevention test was performed using hydraulic cutting. In combination with theoretical analysis and numerical simulation, the mechanism of hydraulic cutting for preventing coal burst was discussed, and reasonable cutting parameters were established. Onsite monitoring revealed that hydraulic cutting disrupts the integrity of the coal-rock mass, releases internal stress, and increases its water content, thereby weakening its burst tendency. Additionally, the deformation and fracturing of the cutting slots and the closure of boreholes shifted the stress concentration from the coal seam to deeper areas and to the two ribs. Post-cutting observations showed a significant reduction in both the frequency and impact energy of coal bursts; there was also a noticeable increase in the convergence of the roadway in the cutting area compared to non-cutting areas. Furthermore, displacement of the roof and floor increased by 78.9 % and that of the two ribs increased by 47.4 % after cutting, preventing the coal-rock mass from accumulating high stress. In conclusion, hydraulic cutting is a promising method for effectively preventing coal bursts and enhancing the safety of mining operations.
RESUMO
In order to mitigate the risk of roof-dominated coal burst in underground coal mining, horizontal long borehole staged hydraulic fracturing technology has been prevailingly employed to facilitate the weakening treatment of the hard roof in advance. Such weakening effect, however, can hardly be evaluated, which leads to a lack of a basis in which to design the schemes and parameters of hydraulic fracturing. In this study, a combined underground-ground integrated microseismic monitoring and transient electromagnetic detection method was utilized to carry out simultaneous evaluations of the seismic responses to each staged fracturing and the apparent resistivity changes before and after all finished fracturing. On this basis, the comparable and applicable fracturing effects on coal burst prevention were evaluated and validated by the distribution of microseismic events and their energy magnitude during the mining process. Results show that the observed mining-induced seismic events are consistent with the evaluation results obtained from the combined seismic-electromagnetic detection method. However, there is a limited reduction effect on resistivity near the fractured section that induces far-field seismic events. Mining-induced seismic events are concentrated primarily within specific areas, while microseismic events in the fractured area exhibit high frequency but low energy overall. This study validates the rationality of combined seismic-electromagnetic detection results and provides valuable insights for optimizing fracturing construction schemes as well as comprehensively evaluating outcomes associated with underground directional long borehole staged hydraulic fracturing.
RESUMO
Directional hydraulic fracturing (DHF) is more and more widely used in coal mines in China for hard roof and coal burst control. The key to this technology is to determine the crack initiation pressure that affected by the shape of the artificial notch and the stress state around the fracturing hole. Reasonable and simple formula for fracturing pressure calculation is essential since the fracturing pump used in coal mines is usually limited by the harsh conditions and hardly replaced once selected. Based on the superposition principle, the simplified 2D model of DHF was established as the elliptical hole with the internal pressure and solved by using the complex functions method. The analytical solution of tangential stress on the inner surface was obtained meanwhile the corresponding criterion of fracturing pressure can be set up. Considering the characteristics of DHF in coal mines, we further got a simplified formula that controlled by the ratio of major to minor axis of the ellipse-like notch, the ratio of the minimum to the maximum principal stress, as well as the tensile strength of the rock. The formula also gave a guide to the design of the notch that major diameter should be at least twice the minor diameter, and the optimal solution for the ratio is to 2ï½4 and recommended 4, which can resist the initiation pressure to a large extent affected by the in-situ stress. Once the pressure of the fracturing fluid is high enough to satisfy the equation cracks would arise at the tips of the notch along the major axis which belongs to mode â crack and would grow unsteadily and rapidly. A PFC simulation model was used to verify the analysis, the results of which are very consistent with the theoretical solutions.
RESUMO
Seismic hazards are typical mining hazards causing dynamic failure of coal and rock mass, which greatly threatens the safety of personnel and equipment. At present, various seismic analysis methods are used to assess seismic risks but their accuracy is significantly limited by the incompleteness of seismic data. The probability of detecting earthquakes (PDE) method has been proven as a powerful means for retrieving missed seismic events and enhancing the seismic data integrity in mines. However, to date, the reliability of the results of the PDE method has not been assessed and the highly integrated seismic data have not been linked with the actual hazard potential. To fill these gaps, this paper investigated the impacts of the seismic data volume used for calculation and the modification of the layout of sensors on the reliability and robustness of the PDE method. The event counts and seismic energy were compensated using the PDE method, correlated with strong seismic events. The results indicated that the compensated seismic data presented higher accuracy in locating future hazardous events than before. This research provides references on enhancing the performance of seismic analysing methods for seismic risk assessments.
RESUMO
Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on the existing mine seismic physical model and utilize deep learning to automatically extract the implicit features of mine microseismic data. The key innovations of FDNet include an expert knowledge indicator selection method based on a subset search strategy, a mine microseismic data extraction method based on a deep convolutional neural network, and a feature deep fusion method of mine microseismic data based on an attention mechanism. We conducted a set of engineering experiments in Gaojiapu Coal Mine to evaluate the performance of FDNet. The results show that compared with the state-of-the-art data-driven machines and knowledge-driven methods, the prediction accuracy of FDNet is improved by 5% and 16%, respectively.