Prediction method of gas content in deep coal seams based on logging parameters:A case study of the Baijiahai region in the Junggar Basin
Yijie Wen1
Shu Tao1
Fan Yang2
Yi Cui3
Qinghe Jing3
Jie Guo3
Shida Chen1
Bin Zhang1
Jincheng Ye1
1.School of Energy Resources,China University of Geosciences(Beijing),No.29 Xueyuan Road,Haidian District,Beijing 100083,China2.PetroChina Research Institute of Petroleum Exploration and Development,Beijing 100083,China3.Zhalainuoer Coal Industry Co.,Ltd.,Hulunbuir 021410,China
摘要：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 capabil-ity,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(J2x)ranges from 1.32 m3/t to 16.11 m3/t,while that in the Badaowan Formation(J1b)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.
机标关键词：predictioncontentregionmethodstudycasedeepbaijiahai
论文发表日期：2025-10-31
在线出版日期：2026-01-31（本平台首次上网日期，不代表文献的发表时间）
页数：18( 239-256 )
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