Fan Yang

The Tazhong No. 2 fault zone is a major fault zone in the Tazhong area, and studying its structural characteristics is of great significance for hydrocarbon exploration. Based on detailed structural interpretation of 2D and 3D seismic data, this study suggests that the Tazhong No. 2 fault zone exhibits distinct segmented deformation characteristics and can be divided into four segments along strike, with the individual structural segments bounded by NE-trending strike-slip faults. The eastern and western parts of the Tazhong No. 2 fault zone differ in their evolutionary characteristics: during the early stage, deformation was intense in the east and relatively weak in the west, whereas during the late stage, the central segment was more active while the eastern and western segments were essentially inactive. Vertically, the fault zone displays stratified deformation: taking the Middle–Lower Cambrian evaporites as the boundary, the suprasalt strata are more strongly deformed than the subsalt strata. The segmented and layered deformation characteristics of the Tazhong No. 2 fault zone are mainly controlled by factors such as the basement structure, the Middle–Lower Cambrian evaporites, and regional tectonic events. Based on the segment-specific accumulation–evolution model, the shallow fault–lithologic traps and volcanic-rock-related traps in the west, the subsalt structural traps, and the buried-hill interior and drape traps are all favorable exploration targets.

Currently, regression prediction methods based on logging data is one of the main methods for analyzing gas content of coal seams. However, the complexity of logging parameters for deep coal seams and the scarcity of measured gas content data significantly affects the accuracy and generalizability of data regression models. Accurately predicting the gas content of coal seams under small-sample condition become a difficult point in deep coalbed methane (CBM) exploration. The Model-Agnostic Meta-Learning (MAML) and Support Vector Regression (SVR) algorithms are among the few suitable for small-sample learning, exhibiting strong adaptability under limited sample conditions. In this study, logging parameters are used as input variables to construct MAML and SVR models, and their performance in predicting gas content of deep coal seams across different regions and layers is compared. The results demonstrate that the MAML algorithm effectively addresses the complex relationships between gas content of deep coal seam and logging parameters. The prediction errors for test dataset and new samples are merely 3.61% and 4.52% respectively, indicating exceptional adaptability, robust generalization capability, and stable model performance. In contrast, the dependency of SVR model on input parameters restricts its accuracy and generalizability in predicting gas content in deep coal seams with varying geological conditions. Although achieving a test dataset error of 4.71%, the SVR model demonstrates substantially degraded performance when applied to novel samples, with prediction errors escalating to 12.46%. Therefore, the MAML model is selected to predict gas content in the unknown areas of the Baijiahai region. The prediction results reveal that the gas content of coal seams in the Xishanyao formation (J ₂ x) ranges from 1.32 m³/t to 16.11 m³/t, while that in the Badaowan Formation (J ₁ b) varies between 1.73 m³/t and 11.27 m³/t. Notably, the gas enrichment areas are predominantly distributed in well blocks adjacent to fault systems, such as wells C31 and BJ8, etc., which align with the favorable geological conditions for deep CBM accumulation in the Baijiahai region. These spatial distribution patterns not only corroborate existing geological insights but also further validate the reliability of the MAML model in predicting gas content within deep coal seams.

The Shenfu-Linxing block in the Ordos Basin holds abundant deep coalbed methane (CBM) resources, which can alleviate gas shortages and aid dual carbon target achievement. Considering isothermal adsorption traits and parameters like vitrinite reflectance, temperature, pressure, and water saturation, a prediction model for adsorbed and free gas content was formulated. This model helps to reveal the deep CBM occurrence mechanism in the Shenfu-Linxing block. Results show that deep CBM exists in both adsorbed and free states, with adsorbed gas initially increasing then decreasing, and free gas rising then stabilizing as burial depth increases. A critical transition depth for total CBM content exists, shallowing with higher water saturation. As depth increases, temperature and pressure evolution results in a “rapid growth—slow growth—stability—slow decrease” pattern in total gas content. Adsorbed gas resides in micropores, while free gas occupies larger pores.