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

ObjectiveDeep 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 de...

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Main Authors: Zhengrong CHEN, Wei LIU, Xueshen ZHU, Yongjing TIAN, Xin XIE
Format: Article
Language:zho
Published: Editorial Office of Coal Geology & Exploration 2025-06-01
Series:Meitian dizhi yu kantan
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Online Access:http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.11.0727
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author Zhengrong CHEN
Wei LIU
Xueshen ZHU
Yongjing TIAN
Xin XIE
author_facet Zhengrong CHEN
Wei LIU
Xueshen ZHU
Yongjing TIAN
Xin XIE
author_sort Zhengrong CHEN
collection DOAJ
description ObjectiveDeep 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. MethodsThis 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). ResultsThe 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. ConclusionsBased 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.
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spelling doaj-art-eba7f286d8cf462cbce7473b23553d552025-08-20T02:22:03ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862025-06-0153619120010.12363/issn.1001-1986.24.11.072724-11-0727-chenzhengrongPrediction of geological and engineering integrated sweet spots of deep coalbed methaneZhengrong CHEN0Wei LIU1Xueshen ZHU2Yongjing TIAN3Xin XIE4College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCNOOC Research Institute Co., Ltd., Beijing 100028, ChinaCNOOC Research Institute Co., Ltd., Beijing 100028, ChinaCNOOC Research Institute Co., Ltd., Beijing 100028, ChinaObjectiveDeep 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. MethodsThis 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). ResultsThe 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. ConclusionsBased 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.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.11.0727deep coalbed methane (cbm)geology-engineering integrationsweet spotvitrinite reflectancecoal structurenatural fracture indexin-situ stress difference
spellingShingle Zhengrong CHEN
Wei LIU
Xueshen ZHU
Yongjing TIAN
Xin XIE
Prediction of geological and engineering integrated sweet spots of deep coalbed methane
Meitian dizhi yu kantan
deep coalbed methane (cbm)
geology-engineering integration
sweet spot
vitrinite reflectance
coal structure
natural fracture index
in-situ stress difference
title Prediction of geological and engineering integrated sweet spots of deep coalbed methane
title_full Prediction of geological and engineering integrated sweet spots of deep coalbed methane
title_fullStr Prediction of geological and engineering integrated sweet spots of deep coalbed methane
title_full_unstemmed Prediction of geological and engineering integrated sweet spots of deep coalbed methane
title_short Prediction of geological and engineering integrated sweet spots of deep coalbed methane
title_sort prediction of geological and engineering integrated sweet spots of deep coalbed methane
topic deep coalbed methane (cbm)
geology-engineering integration
sweet spot
vitrinite reflectance
coal structure
natural fracture index
in-situ stress difference
url http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.11.0727
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AT xueshenzhu predictionofgeologicalandengineeringintegratedsweetspotsofdeepcoalbedmethane
AT yongjingtian predictionofgeologicalandengineeringintegratedsweetspotsofdeepcoalbedmethane
AT xinxie predictionofgeologicalandengineeringintegratedsweetspotsofdeepcoalbedmethane