A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application
Gas-bearing shales have become a major source of future natural gas production worldwide. It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sweet spots. The formation of gas-bearing shales is closely linked to relative sea-l...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Energy Geoscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759225000137 |
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| author | Hongyan Wang Zhensheng Shi Xi Yang Qun Zhao Changmin Guo |
| author_facet | Hongyan Wang Zhensheng Shi Xi Yang Qun Zhao Changmin Guo |
| author_sort | Hongyan Wang |
| collection | DOAJ |
| description | Gas-bearing shales have become a major source of future natural gas production worldwide. It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sweet spots. The formation of gas-bearing shales is closely linked to relative sea-level changes, providing an important approach to predicting sweet spots in the Wufeng-Longmaxi shale in the southern Sichuan Basin, China. Three types of marine shale gas sweet spots are identified in the shale based on their formation stages combined with relative sea-level changes: early, middle, and late transgression types. This study develops a prediction model and workflow for identifying shale gas sweet spots by analyzing relative sea-level changes and facies sequences. Predicting shale gas sweet spots in an explored block using this model and workflow can provide a valuable guide for well design and hydraulic fracturing, significantly enhancing the efficiency of shale gas exploration and development. Notably, the new prediction model and workflow can be utilized for the rapid evaluation of the potential for shale gas development in new shale gas blocks or those with low exploratory maturity. |
| format | Article |
| id | doaj-art-64a6fa69787c4d5ea9fbf486a817d6ff |
| institution | OA Journals |
| issn | 2666-7592 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy Geoscience |
| spelling | doaj-art-64a6fa69787c4d5ea9fbf486a817d6ff2025-08-20T02:23:11ZengKeAi Communications Co., Ltd.Energy Geoscience2666-75922025-06-016210039210.1016/j.engeos.2025.100392A model for predicting marine shale gas sweet spots based on relative sea-level changes and its applicationHongyan Wang0Zhensheng Shi1Xi Yang2Qun Zhao3Changmin Guo4National Elite Institute of Engineering, CNPC, Beijing, 100096, China; National Energy Shale Gas R&D Center, Langfang, Hebei, 065007, ChinaNational Energy Shale Gas R&D Center, Langfang, Hebei, 065007, China; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing, 100083, China; Corresponding author.National Energy Shale Gas R&D Center, Langfang, Hebei, 065007, China; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing, 100083, China; Key Laboratory of Groundwater Conservation of Ministry of Water Resources, China University of Geosciences (Beijing), Beijing 100083, ChinaNational Energy Shale Gas R&D Center, Langfang, Hebei, 065007, China; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing, 100083, ChinaResearch Institute of Petroleum Exploration & Development, PetroChina, Beijing, 100083, ChinaGas-bearing shales have become a major source of future natural gas production worldwide. It has become increasingly urgent to develop a reliable prediction model and corresponding workflow for identifying shale gas sweet spots. The formation of gas-bearing shales is closely linked to relative sea-level changes, providing an important approach to predicting sweet spots in the Wufeng-Longmaxi shale in the southern Sichuan Basin, China. Three types of marine shale gas sweet spots are identified in the shale based on their formation stages combined with relative sea-level changes: early, middle, and late transgression types. This study develops a prediction model and workflow for identifying shale gas sweet spots by analyzing relative sea-level changes and facies sequences. Predicting shale gas sweet spots in an explored block using this model and workflow can provide a valuable guide for well design and hydraulic fracturing, significantly enhancing the efficiency of shale gas exploration and development. Notably, the new prediction model and workflow can be utilized for the rapid evaluation of the potential for shale gas development in new shale gas blocks or those with low exploratory maturity.http://www.sciencedirect.com/science/article/pii/S2666759225000137Shale gasSweet spotRelative sea-level changeWufeng-longmaxi shaleSouthern sichuan basin |
| spellingShingle | Hongyan Wang Zhensheng Shi Xi Yang Qun Zhao Changmin Guo A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application Energy Geoscience Shale gas Sweet spot Relative sea-level change Wufeng-longmaxi shale Southern sichuan basin |
| title | A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application |
| title_full | A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application |
| title_fullStr | A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application |
| title_full_unstemmed | A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application |
| title_short | A model for predicting marine shale gas sweet spots based on relative sea-level changes and its application |
| title_sort | model for predicting marine shale gas sweet spots based on relative sea level changes and its application |
| topic | Shale gas Sweet spot Relative sea-level change Wufeng-longmaxi shale Southern sichuan basin |
| url | http://www.sciencedirect.com/science/article/pii/S2666759225000137 |
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