Ke Ren

Deep coalbed methane, a key strategic resource for unconventional natural gas supply, is abundant in the Ordos Basin, China. However, limited systematic research on the state of gas occurrence and critical depth, combined with sparse gas content and in situ stress measurements, has severely restricted the optimization of developmental strategies. Focusing on the 8+9# coal seam of the Taiyuan Formation in the central Linxing–Shenfu Block, Ordos Basin, an improved gas content prediction model is established, which comprehensively accounts for matrix compression, thermal expansion, adsorption-induced swelling, and the pore volume occupied by adsorbed gas. The adsorbed gas content trend was exhibited by logging data. Continuous in situ stress profiles were predicted using logging data combined with the Eaton method and Huang Rongzun’s in situ stress model. Critical depth was determined using the gas content evolution and in situ stress regime transition. The minimum horizontal stress of the 8+9# coal seam exhibited a “higher in the south and lower in the north” distribution pattern. The gas content critical depth is 1076–1704 m, the in situ stress critical depth is 1138–1727 m, and the integrated critical depth is 1076–1727 m, showing a “deeper in the south and shallower in the north” distribution. Based on the three-stage variation pattern of gas content and stress regime, a layered development strategy was proposed. This study supports location optimization and drilling and fracturing parameter design, contributing to safe, efficient, and economical deep coalbed methane development under similar geological conditions.

The structure and evolution of coal are intricately linked to its properties at both nanometer and micrometer scales. The refinement of pores and fractures is crucial for assessing outburst risks, evaluating coalbed methane (CBM) reservoirs, and improving the CBM recovery efficiency. This study involved collecting four different coal-body structure samples from the Zhaozhuang colliery in the southern Qinshui Basin. We conducted laboratory analyses to determine the physical properties of the coal and employed X-ray computed tomography (CT) to quantitatively assess the pore size distribution (PSD), volume contribution, morphology, and connectivity across various coal structures. The evolution and comparative characteristics of these coal structures under different tectonic stresses is discussed. Results reveal that the hysteresis loop type of 3# anthracite primarily aligns with the H3 type as per IUPAC classification, featuring predominantly plate-like and wedge-shaped pores at the nanometer scale. A shape factor was introduced to quantitatively categorize the pore types, highlighting its sensitivity to the coal structure. Spherical and tubular pores are mainly present in the aperture range of 0-25 μm, while larger apertures appeared as prolate spheroids and flat fractures. Primary coal contains more spherical or tubular pores, whereas tectonic coal shows a prevalence of slit pores and flat fractures, suggesting that pores and fractures undergo progressive deformation, either breaking or elongating, under tectonic stress. The elastic property is affected by the multifactor of the increased pore volume and changes in pore morphology. Permeability is influenced by PSD and connectivity, demonstrating a quadratic positive correlation with porosity, aperture, and specific surface area. Granular coal exhibited the most favorable permeability characteristics for CBM extraction. The distribution trend of morphology and comprehensive data highlight an evolutionary pattern of different coal structures. The evolution of the coal structure is mainly shaped by brittle and ductile deformation mechanisms. Cataclastic coal is characterized by an increase in new fractures, and granular coal undergoes rapid new fracture formation and enhanced connectivity under strong stress conditions. Mylonitic coal develops under ductile deformation mechanisms. These insights into the properties of various coal structures can significantly enhance our understanding of the CBM recovery efficiency from both microscopic and mesoscopic perspectives.

Well-logging-constrained impedance inversion is an effective process for predicting the thickness and bifurcation of coal seams. Wavelet changes in a complex region achieve the best match between the inverse and source wavelets, affecting the accuracy of the inversion solution and the ability to obtain accurate inverted acoustic impedance (AI) data. We have conducted the joint inversion of wavelet and AI data using iterative methods, which combined the conjugate-gradient (CG) method and particle-swarm-optimization (PSO) algorithm. The Marmousi AI model was used to prove the reliability of the method. The CG-PSO algorithm achieved excellent results compared with the statistical wavelet pickup method. The wavelet obtained by the CG-PSO algorithm is preferred for inversion operations. We applied a new method to invert field data and predict the thickness and bifurcation of coal seams in the karst region. The results find that the wavelet spectrum obtained by the CG-PSO matches the spectrum map of the coal seam in the Yuwang colliery. We determined the distribution of the thickness and bifurcation of the 101 panel, Yuwang Colliery, Yunnan Province. The average error of the predicted coal thickness is 0.17 m (14.4%), which verifies the feasibility and effectiveness of the method. The method provides insights into the AI inversion of constrained waves in complex regions.

Faults are geologic structures that can cause disasters and thereby affect the safety of coal mines. To achieve improved fault interpretation accuracy during 3D seismic exploration of coal mines, we develop a seismic fault identification method based on a combination of kernel principal component analysis (KPCA), genetic particle swarm optimization (GPSO), and a support vector machine (SVM). The Dongwupan area of the Sihe Coal Mine in Shanxi Province, which mainly contains small faults, is the research area, and we extract 20 types of seismic attributes. According to the median difference between fault and nonfault data, we select the 12 leading seismic attributes with differences greater than 0.1 in descending order. Considering the information redundancy and the nonlinear relationships among seismic attributes, we adopt the KPCA method to reduce and optimize the selected seismic attributes, thereby effectively capturing the main information contained in the data and eliminating noise. Moreover, we introduce the GPSO algorithm to effectively optimize the SVM model parameters, and we construct a KPCA-GPSO-SVM model to classify and predict faults. Through model testing, the average fault identification accuracy of the model is 98.89%. Relative to the KPCA-particle swarm optimization-SVM, principal component analysis-GPSO-SVM, and GPSO-SVM models, the accuracy is improved by 3.3%, 7.8%, and 16.7%, respectively. We apply the proposed model to predict the fault distribution in the research area, and we compare the predictions to the actual exposed faults. The results indicate that the KPCA-GPSO-SVM model can suitably realize fault distribution prediction in a mining area. Moreover, compared with manual fault interpretation, the developed method is faster, more intuitive, and can better identify small faults.

Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared. Ten faults in the area were selected to analyze the prediction effect, and a comparative experiment was done with model structure parameters and training sets. The experimental results indicate that the training parameters have a significant influence on the training time and testing accuracy of the model, while structural parameters and training sets affect the actual prediction effect of the model. By comparison, the fault results predicted by the convolutional neural network are in good agreement with the manual interpretation, and the accuracy of the model is more than 85%, which proves that this method has certain feasibility and provides a new way to shorten the fault interpretation period and improve the interpretation accuracy.