As the global energy structure continues to evolve toward low-carbon, cleaner, and more diversified systems, unconventional oil and gas resources—such as shale oil and gas, tight gas, and coalbed methane—are playing an increasingly vital role in ensuring energy security and promoting economic development

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.