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A non-Darcy flow with moving boundary conditions in a low-permeability reservoir was solved using the homotopy analysis method (HAM), which was converted into a fixed-boundary mathematical model via similarity transformation. Approximate analytical solutions based on the HAM are guaranteed to be more accurate than exact analytical solutions, with relative errors between 0.0089% and 2.64%. When λ = 0, the pressure drop of the Darcy seepage model could be instantaneously transmitted to infinity. When λ > 0, the pressure drop curve of the non-Darcy seepage model exhibited the characteristics of tight support, which was clearly different from the Darcy seepage model's formation pressure distribution curve. According to the results of the HAM, a moving boundary is more influenced by threshold pressure gradients with a longer time. When the threshold pressure gradients were smaller, the moving boundaries move more quickly and are more sensitive to external influences. One-dimensional, low-permeability porous media with a non-Darcy flow with moving boundary conditions can be reduced to a Darcy seepage model if the threshold pressure gradient values tend to zero. [ABSTRACT FROM AUTHOR]

Coal seam impact risk assessment is the premise of coal mine safety, which can reduce the occurrence of underground impact pressure accidents and directly affect the safety, coal production, economic and social benefits of coal mining enterprises. In order to evaluate the impact risk of coal seams more reasonably and comprehensively, and consider the weights of different influencing factors on the impact risk of coal seams, the neural network model is proposed to evaluate the impact risk of coal seams. Mining depth, impact tendency, geological structure and mining technology are selected as the influencing factors of coal seam impact risk. Each influencing factor contains different evaluation indices, a total of 18. The 18 evaluation indices and the impact risk level are normalized and quantified. The BP neural network model for evaluating coal seam impact risk level is established, and the impact risk of 2-1 coal seams in a mine in Inner Mongolia is comprehensively evaluated and analyzed in this study. The results show that the BP neural network model can represent coal seam impact risk level well. The application of the BP neural network model to evaluate coal seam impact risk level has the characteristics of high precision, fast calculation speed and less artificial calculation, which provides an efficient and convenient method for the evaluation of coal seam impact risk. [ABSTRACT FROM AUTHOR]

Intelligent unmanned mining is a key process in coal mine production, which has direct impact on the production safety, coal output, economic benefits and social benefits of coal mine enterprises. With the rapid development and popularization of 5G+ intelligent mines and coal mine intelligent equipment in China, the intelligentization of intelligent unmanned mining has become an important research topic. Especially with the promulgation of some Chinese policies and regulations, intelligent unmanned mining technology has become one of the key technologies of coal mine production. To understand the connotation, status quo and development trends of intelligent unmanned mining, this paper takes the relationship between key technologies and engineering application of intelligent unmanned mining in China as the perspective. It is proposed that the intelligent unmanned mining technology is in the whole process of working face mining. A research structure of unmanned follow-up operation and safe patrol is changing to the mode of intelligent adaptive mining, followed by the basic concepts and characteristics of intelligent unmanned mining. Relevant researches that maybe beneficial to the proposed research content are reviewed in four layers, which include basic theory, key technology, mining mode, and overall design system theory and technology. Finally, the current intelligent unmanned mining mode and future trends in this field in China are summarized. [ABSTRACT FROM AUTHOR]

Shale oil is hosted in nanopores of organic-rich shales, so pore characteristics are significant for shale oil accumulation. Here we analyzed pore characteristics of 39 lacustrine shale samples of the Late Cretaceous Qingshankou Formation (K2qn) in the Songliao Basin, which is one of the main shale oil resource basins in China, using field emission-scanning electron microscopy (FE-SEM), and low-pressure nitrogen adsorption. We accomplished fractal analysis, correlation analysis using correlation matrix and multidimensional scaling (MDS), and prediction of fractal dimensions, which is the first time to predict pore fractal dimensions of shales. Interparticle pores are highly developed in K2qn. These shales have mesoporous nature and slit-shaped pores. Compared with the second and third members (K2qn2,3), the first member of the Qingshankou Formation (K2qn1) has a larger average pore diameter, much smaller surface area, fewer micropores, simpler pore structure and surface indicated by smaller fractal dimensions. In terms of pore characteristics, K2qn1 is better than K2qn2,3 as a shale oil reservoir. When compared with marine Bakken Formation shales, lacustrine shales of the Qingshankou Formation have similar complexity of pore structure, but much rougher pore surface. This research can lead to an improved understanding of the pore system of lacustrine shales. [ABSTRACT FROM AUTHOR]