Prediction of geological and engineering integrated sweet spots of deep coalbed methane

Authors

CHEN Zhengrong, College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China; State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China; CNOOC Research Institute Co., Ltd., Beijing 100028, ChinaFollow
LIU Wei, College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China; State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
ZHU Xueshen, CNOOC Research Institute Co., Ltd., Beijing 100028, China
TIAN Yongjing, CNOOC Research Institute Co., Ltd., Beijing 100028, China
XIE Xin, CNOOC Research Institute Co., Ltd., Beijing 100028, China

Abstract

Objective Deep coalbed methane (CBM) has emerged as a hot topic in CBM resource development. However, deep CBM has characteristics such as great burial depths, complex stress environments, and strong reservoir heterogeneity, which seriously restrict sweet spot prediction and accurate well location deployment in its large-scale exploitation. Methods This study investigated a deep CBM field along the eastern margin of the Ordos Basin. Using sonic, density, and caliper logging, this study developed a coal structure index model for deep coals. By introducing the coal structure index based on the coal structure differences in deep coal seams and combining factors including overburden formation pressure, tectonic stress, and pore pressure, this study established an adaptive horizontal in-situ stress difference model for deep coal seams. Based on the rock strength parameter, the enlargement rate of wellbore diameter, and the fracture toughness of rocks, a natural fissure index model was constructed. By integrating these three models, as well as the six indices of geological and engineering sweet spots, this study developed an intelligent prediction model of geological and engineering integrated sweet spots of deep CBM using support vector machine (SVM). Results The results indicate that the intelligent prediction model of geological and engineering integrated sweet spots yielded a prediction accuracy of 88.2%. Classes I, II, and III sweet spots were identified in the study area, with areas of 117.4 km² (14.0%), 258.4 km² (30.8%), and 463.1 km² (55.2%), respectively, and average predicted production of 6478.6 m³/d, 5076.7 m³/d, and 4022 m³/d, respectively. Conclusions Based on the results of this study, it is recommended to focus on Class I sweet spots, actively explore Class II sweet spots, and proactively avoid Class III sweet spots in the well location deployment for deep CBM in the study area. The fine-scale prediction of geological and engineering integrated sweet spots can provide valuable guidance for reserve growth and production addition of deep CBM along the eastern margin of the Ordos Basin.

Keywords

deep coalbed methane (CBM), geology-engineering integration, sweet spot, vitrinite reflectance, coal structure, natural fracture index, in-situ stress difference

DOI

10.12363/issn.1001-1986.24.11.0727

Recommended Citation

CHEN Zhengrong, LIU Wei, ZHU Xueshen, et al. (2025) "Prediction of geological and engineering integrated sweet spots of deep coalbed methane," Coal Geology & Exploration: Vol. 53: Iss. 6, Article 16.
DOI: 10.12363/issn.1001-1986.24.11.0727
Available at: https://cge.researchcommons.org/journal/vol53/iss6/16

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