The evaluation and prediction of favorable coalbed methane (CBM) sweet spot play an important role in well location deployment and recovery prediction in CBM blocks. This work investigates the CBM geology and accumulation characteristics of No. ( ) coal in the Carboniferous Taiyuan Formation in Yangjiapo block based on data from 14 CBM wells. The desorption index is proposed to be used to study the CBM desorption potential in Yangjiapo block, and the parameter of reduced water level is adopted to study the CBM hydrodynamics of the block. Furthermore, the analytical hierarchy fuzzy evaluation method is used to evaluate and prediction the CBM development sweet spot in Yangjiapo block. The results show that the buried depth of the No. ( ) coal seam in Yangjiapo block varies from 693.20 to 1213.20 m, the coal thickness is from 5.40 to 13.10 m, the gas content is from 5.89 to 10.55 m³/t, and the minimum horizontal principal stress is from 9.80 to 20.82 MPa. The desorption potential is better in the southeastern and central-western part of the block. It is found that there is a positive relationship between CBM content and hydrodynamics and indicated that CBM easily concentrates in the lower reduced water level area. The CBM favorable development sweet spot is forecasted to be located in the southeastern part, central-western region, and northeastern part of Yangjiapo block.

In the process of coalbed methane development, drilling fluid and fracturing fluid cannot achieve absolute compatibility with formation. The incompatibility between the working fluid and reservoir will lead to the intrusion of working fluid into the reservoir and cause reservoir pollution. This is a very common phenomenon. There is a large amount of pulverized coal in the coal seam, and the intrusion of working liquid will be combined with the pulverized coal to form cement to block the seepage space in the reservoir. Since pressure relief and fracturing fluid backflow will be performed at the first time after fracturing, the intrusion range of the working fluid is small, generally reaching 10 m to 50 m. Compared with a conventional gas reservoir or shale gas reservoir, the working fluid loss during CBM development will seriously affect the subsequent production project and even make the gas well lose production capacity. On the other hand, in order to avoid this phenomenon, measures such as acidification or volumetric fracturing are sometimes used to improve the seepage environment near the well and near the fracture. The purpose of this study is to quantitatively evaluate the impacts of working fluid filtration and reservoir reconstruction on production. In this study, a single well productivity evaluation model and sensitivity analysis method considering drilling fluid filtration loss, fracturing fluid filtration loss, reservoir reconstruction and other processes is proposed. The formation mechanism of fluid loss during drilling and fracturing is described, and the productivity evaluation model considering fluid loss is combined with the Langmuir isothermal adsorption equation, steady-state diffusion law, Darcy’s seepage law and Duhamel convolution formation. Combined with the distribution of actual gas reservoir flow characteristics, the sensitivity of single well productivity to gas reservoir porosity, gas reservoir permeability, coal seam adsorption coefficient, working fluid filtration loss and reservoir reconstruction measures are analyzed. Through the analysis and fitting of the actual production data on site, the relationship curve can better fit the field production data, and the evaluation results are in line with the drilling and fracturing conditions at that time and the subsequent production conditions, with small errors. The obtained method is suitable for predicting the productivity of fractured vertical wells in different working conditions and provides a basis for the development and productivity prediction of CBM reservoirs in China and in international cooperation.

The adsorption/desorption characteristics of a coal reservoir play an important role in coalbed methane (CBM) development. The proximate analysis, maceral analysis and methane isothermal adsorption/desorption experiment are carried out based on coal samples from no. 4+5 coal seam in Baode block. Combing with coal experimental data and the CBM well-produced water salinity data in the Baode block, the effect of salinity and water content on CBM adsorption/desorption characteristics of the coal reservoir and its influencing mechanism is discussed. The results show that the CBM adsorption and desorption capacity decreases with the increase of water salinity and the decrease value shows a decreasing trend. The increase of water salinity reduces the solubility of methane in coal seam water and then reduces the adsorption capacity of methane. With the increase of water content, the adsorption and desorption capacities of CBM decrease gradually. The CBM adsorption and desorption capacities decrease with the increase of water content in coal samples. The adsorption/desorption capacities of coal samples change rapidly in the low-water content stage and slowly in the high-water content stage. The competitive adsorption effect and water blocking effect between water and methane molecules are the main influencing mechanisms of water content on CBM adsorption/desorption. It can be seen that the salinity and water content will have a certain adverse impact on the adsorption and desorption capacity of CBM in the Baode block. The influence of the difference in water content and salinity in the coal seam cannot be ignored in reserve evaluation and productivity prediction of CBM. The continuous, stable, and effective drainage is one of the key factors to ensure the efficient development of CBM wells in the Baode block.

High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM images, such as unlocated fringe regions, single-threshold segmentation, unclassified fuzzy superpixels, and tedious fringe pruning, an intelligent recognition method based on semantic segmentation, deep neural networks, fuzzy superpixels, and other algorithms is proposed. For unlocated fringe regions, the fringe regions are automatically localized with semantic segmentation. The whole semantic segmentation network adopts DeepLab V3+ based on ResNet to reduce unnecessary operations brought by non-fringe regions. For single-threshold segmentation of the image, the image is chunked before anything else. The genetic-optimized watershed algorithm is applied to divide the fringe base maps and non-fringe ones in order to avoid the distortion caused by different lights and shades of the image. For the fuzzy superpixels between the fringes and non-fringes, a similarity category judgment method based on neighboring pixels is proposed to solve the problem of unclassified fuzzy superpixels and to enrich and perfect the information of the lattice fringe base map. Eventually, as for lattice fringe overlap caused by coals piling together, a similarity judgment method based on the fringes' characteristics is proposed to remove the bur portion of the lattice fringes and improve the pruning rate. Combining the above theories, a visualization tool based on MATLAB App Designer is designed, and the above four steps can be completed by this app to accurately display the results of coal aromatic lattice fringe identification in HRTEM images. Comparison with the lattice fringes drawn by leading experts shows that the fringes interpreted by this method are reliable. This method facilitates the extraction of lattice fringes in HRTEM, which lays the foundation for the labeling of HRTEM images in a variety of deep learning algorithms and facilitates the direct observation of coal structures by researchers.