LAPSE:2024.0462
Published Article

LAPSE:2024.0462
The Inversion Method of Shale Gas Effective Fracture Network Volume Based on Flow Back Data—A Case Study of Southern Sichuan Basin Shale
June 5, 2024
Abstract
Fracture network fracturing is pivotal for achieving the economical and efficient development of shale gas, with the connectivity among fracture networks playing a crucial role in reservoir stimulation effectiveness. However, flow back data that reflect fracture network connectivity information are often ignored, resulting in an inaccurate prediction of the effective fracture network volume (EFNV). The accurate calculation of the EFNV has become a key and difficult issue in the field of shale fracturing. For this reason, the accurate shale gas effective fracture network volume inversion method needs to be improved. Based on the flow back characteristics of fracturing fluids, a tree-shaped fractal fracture flow back mathematical model for inversion of EFNV was established and combined with fractal theory. A genetic algorithm workflow suitable for EFNV inversion of shale gas was constructed based on the flow back data after fracturing, and the fracture wells in southern Sichuan were used as an example to carry out the EFNV inversion. The reliability of the inversion model was verified by testing production, cumulative gas production, and microseismic results. The field application showed that the inversion method proposed in this paper can obtain tree-shaped fractal fracture network structure parameters, fracture system original pressure, matrix gas breakthrough pressure, fracture compressibility coefficient, reverse imbibition index, equivalent main fracture half length, and effective initial fracture volume (EIFV). The calculated results of the model belong to the same order of magnitude as those of the HD model and Alkouh model, and the model has stronger applicability. This research has important theoretical guiding significance and field application value for improving the accuracy of the EFNV calculation.
Fracture network fracturing is pivotal for achieving the economical and efficient development of shale gas, with the connectivity among fracture networks playing a crucial role in reservoir stimulation effectiveness. However, flow back data that reflect fracture network connectivity information are often ignored, resulting in an inaccurate prediction of the effective fracture network volume (EFNV). The accurate calculation of the EFNV has become a key and difficult issue in the field of shale fracturing. For this reason, the accurate shale gas effective fracture network volume inversion method needs to be improved. Based on the flow back characteristics of fracturing fluids, a tree-shaped fractal fracture flow back mathematical model for inversion of EFNV was established and combined with fractal theory. A genetic algorithm workflow suitable for EFNV inversion of shale gas was constructed based on the flow back data after fracturing, and the fracture wells in southern Sichuan were used as an example to carry out the EFNV inversion. The reliability of the inversion model was verified by testing production, cumulative gas production, and microseismic results. The field application showed that the inversion method proposed in this paper can obtain tree-shaped fractal fracture network structure parameters, fracture system original pressure, matrix gas breakthrough pressure, fracture compressibility coefficient, reverse imbibition index, equivalent main fracture half length, and effective initial fracture volume (EIFV). The calculated results of the model belong to the same order of magnitude as those of the HD model and Alkouh model, and the model has stronger applicability. This research has important theoretical guiding significance and field application value for improving the accuracy of the EFNV calculation.
Record ID
Keywords
effective fracture network volume (EFNV), flow back data, fracture network fracturing, Genetic Algorithm, shale gas
Subject
Suggested Citation
Tang D, Wu J, Zhao J, Zeng B, Song Y, Shen C, Ren L, Huang Y, Wang Z. The Inversion Method of Shale Gas Effective Fracture Network Volume Based on Flow Back Data—A Case Study of Southern Sichuan Basin Shale. (2024). LAPSE:2024.0462
Author Affiliations
Tang D: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Wu J: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Zhao J: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Zeng B: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Song Y: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Shen C: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Ren L: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Huang Y: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Wang Z: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Wu J: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Zhao J: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Zeng B: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Song Y: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Shen C: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Ren L: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Huang Y: Shale Gas Research Institute, CNPC Southwest Oil and Gas Field Company, Chengdu 610051, China; Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation, Chengdu 610051, China
Wang Z: National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Journal Name
Processes
Volume
12
Issue
5
First Page
1027
Year
2024
Publication Date
2024-05-18
ISSN
2227-9717
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PII: pr12051027, Publication Type: Journal Article
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LAPSE:2024.0462
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https://doi.org/10.3390/pr12051027
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Jun 5, 2024
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