A quantitative analysis of the porosity, pore size distribution, and fractal dimensions of pores is significant for studying the pore structure characteristics of coal. This study utilized 12 anthracite coal samples from the Sihe mining area to explore the pore structure characteristics of the coal therein. Hundred randomly selected points on each sliced coal sample were imaged via scanning electron microscopy, and a total of 1200 images were used for the analysis. The porosity and fractal dimensions of the coal samples were analyzed via digital image processing and box-counting dimension methods. This method is characterized by extensive graphical analysis, and the results are based on statistical methods. These were also used to analyze the structural and development characteristics of the microscopic pores in the coal. The results reveal that the surface porosity obtained via digital image processing was 16.11% lower than that measured experimentally. The fractal dimension and porosity of the pore surface were fitted to a natural logarithmic curve. The rate of change in the pore fractal dimension depends on the porosity such that, to some degree, a greater porosity is associated with more complex pore structures, a higher degree of micropore development, and improved pore connectivity.

Permeability of coal reservoirs influence the extraction of coal gas from coal seams. Twelve coal samples were collected at an anticline and a syncline of the No. 3 coal seam in the Sihe coal mine. Porosity, permeability, pore size, vitrinite reflectance, and liquid nitrogen adsorption of the samples were evaluated. Structural curvatures at the sample locations, and the distance between the sampling locations and the nearest faults were calculated based on seismic data. The influences of the evaluated parameters on permeability were analyzed. Major factors that influence permeability of the No. 3 coal seam were extracted using principal component analysis (PCA). Based on the porosity–permeability model derived from the Archie formula and classic Kozeny–Carman equation, we deduced that the permeability of coal increased with an increase in porosity. With an increase in average vitrinite reflectance, permeability decreases first and then increases. PCA results showed that coal permeability was regulated by three key components representing three modes. The first component included pore size, depth, and pore complexity accounting for 52.59% of the variability indicating that it was the most important in controlling permeability. The second component included specific surface area, structural curvature, and porosity, and the third component comprised of specific surface area, porosity, and average vitrinite reflectance. Overall, pore diameter and complexity had significant effects on coal permeability. The results show that researchers and stakeholders must consider the interactions among multiple factors rather than single factors to understand the influences on permeability to facilitate efficient utilization of coalbed methane resources.

The bulk density and bulk modulus of adsorbed coalbed methane are required to calculate the undrained bulk and shear moduli as well as the fluid-saturated density of the coal reservoir. We derived the formula for the bulk modulus and bulk density of adsorbed methane using the Langmuir equation for isothermal adsorption. The bulk density of adsorbed methane is positively correlated with the gas pressure and Langmuir volume, but it is negatively correlated with the Langmuir pressure and adsorbed methane saturation. The bulk density of adsorbed methane is greater than that of free-state methane for the same gas pressure. The bulk modulus of adsorbed methane is positively correlated with the gas pressure and negatively correlated with the Langmuir pressure; it is much larger than that of free methane, but it is still much smaller than that of water within the normal gas pressure range. Coal samples from the study area demonstrate that the water saturation is less than 95%. Considering adsorbed methane as a pseudosolid, calculations yield a very small difference in the bulk modulus of coal under room-dry conditions. Considering adsorbed methane as a gas, calculations based on spatially uniform or patchy saturation indicate that multiphase fluid saturation produces a very small difference in the elastic parameters and wave velocity of the fluid-saturated coal from the dual-phase fluid (water and free methane).

SUMMARY: As an important method of 3-D seismic exploration, intelligent structural interpretation has been improved in recent years. Traditional human–computer interaction explanations typically assess fault risk on a hierarchical basis. However, the majority of intelligent fault interpretations utilize binary classification methods, which limits the utility and development of this technology to a certain extent. In order to break this limitation, a fault risk assessment scheme based on particle swarm optimization (PSO) and support vector machine (SVM) posterior probability model has been proposed. In this study, the fault exposure situations for 11 wells and 4 roadways of a coal mine in Yunnan Province, China, were identified. A total of 15 559 data points with known fault and non-fault information were obtained in the target coal seam. Based on these data, SVM fault recognition models were constructed. The key parameters of the SVM models were optimized by PSO, and the test accuracy of each model was above 87 per cent. Meanwhile, the SVM posterior probability model was introduced to evaluate the fault identification results in the study area. The SVM posterior probability model was used to convert the decision value of a standard SVM into a posterior probability value. Probability values in the range of [0, 0.5) are evaluated as non-fault points, [0.5, 0.7) as ‘unreliable’ fault points, [0.7, 0.9) as ‘less reliable’ fault points, and [0.9, 1] as ‘reliable’ fault points. By constructing data sets with different sample numbers and fault types, the influence of each factor on the evaluation of the SVM posterior probability model was analysed. The results show that the SVM posterior probability model can be used to hierarchically evaluate fault risk in the study area and provide decision makers with more accurate information for decision making.

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.