A combination of geographic information system (GIS) and spatial multicriteria decision making (MCDA) in mine water inrush risk evaluation is widely used, but the randomness in the process of index weight determination and the risk-coping attitude of decision makers are not considered in the decision making process. Therefore, this paper proposes a probability-based roof water inrush risk evaluation method (GIS-MCDA) by combining the Monte Carlo analytic hierarchy process (MAHP) and ordered weighted averaging (OWA) operator. This method uses MAHP to determine the weight of the evaluation indicators, reducing the randomness of the analytic hierarchy process (AHP) to determine the weight of the evaluation indicators using the OWA operator to quantify the five risk-coping attitudes of decision makers and incorporate the risk attitude of decision makers into the evaluation process. Taking the Liangshuijing Coal Mine in northern Shaanxi as an example, the application of the GIS-MCDA method showed that the method makes the risk results of roof water inrush more objective and comprehensive and reduces or avoids the risk of decision making due to human subjective tendency change.

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk.

Driven by global carbon neutrality targets and demand for a long-term stable energy supply, unconventional petroleum resources such as shale oil, tight oil and fractured carbonate reservoirs have become indispensable components of worldwide energy structures

The cleat-hydraulic fracture system in deep coal is the main medium controlling the seepage capacity of coal, but under the combined action of high in situ stress and retained fracturing fluid, the permeability of the fracture system is significantly affected by stress and hydrochemical condition. Clarifying the dynamic seepage evolution of coal fractures under stress-hydrochemical coupling is therefore of great significance for optimizing the production schedule and achieving efficient development of deep coalbed methane (CBM) wells. In this study, the No. 8 coal of the Benxi Formation in the Ordos Basin was taken as the research object. Eight standard cylindrical plugs (Φ25 mm × 50 mm) were tested, including four intact samples representing the natural cleat system and four pre-fractured (self-propped) samples representing the cleat-hydraulic fracture system. Overburden helium permeability experiments were conducted at a pore pressure of 1.38 MPa under stepwise confining pressures of 3.45–24.14 MPa along three stress paths (first loading, secondary loading and stepwise unloading). The hydrochemical treatment is realized by soaking the samples in water for 24 h followed by air-drying for 12 h, after which the permeability tests were repeated. Finally, a dynamic permeability model was established on the basis of the experimental results, and the permeability characterization parameters of coal fractures under different conditions were determined. The results show that the permeability of all samples decreases exponentially with increasing confining pressure, and that the fracture type and cleat connectivity dominate the sample permeability. A single directly through-going fracture can increase the initial permeability of intact coal by 5–10 times and markedly reduce its decay rate. A sample with highly developed and well-connected cleats has an initial permeability as high as 32.36 × 10⁻³ μm², i.e., 8–30 times higher than other pre-fractured samples. Secondary loading reduces the permeability at the same confining pressure by 20–40%, and after stress recovery the permeability only rebounds to 35–60% of the first-loading level, indicating that a large part of the fracture closure is irreversible plastic deformation. Hydrochemical treatment can improve fracture connectivity and enhance permeability, and the effect is jointly controlled by the degree of cleat development and mineral filling characteristics. For a sample with well-developed cleats filled with calcite, the permeability after treatment increases to 2.0–3.7 times the pre-treatment value and the stress sensitivity decreases markedly, indicating that dissolution of the filling calcite reopens and interconnects the originally cemented and sealed cleats and natural fractures, forming more continuous and closure-resistant seepage channels. By fitting the experimental data with the permeability model, the distribution of the fracture stress-sensitivity coefficient under different conditions was obtained. The main limitations of this study are that each test condition was performed on a single specimen without replicate tests, that the water chemistry and dissolved-ion concentrations during soaking were not measured, and that the model parameters were calibrated under specific loading conditions, so caution is required when extrapolating them to reservoir conditions. This study clarifies the dynamic permeability evolution of deep coal fractures and the parameters for predicting the permeability of coal fracture systems, providing an important reference for hydraulic fracturing and the optimized design of production schedules in gas wells.