LAPSE:2023.36660v1
Published Article

LAPSE:2023.36660v1
Prediction of Marine Thin Shale Gas Reservoir with Seismic Phase-Controlled Nonlinear Stochastic Inversion
September 20, 2023
Abstract
Due to the complicated interference sources and low signal-to-noise ratio of seismic records, conventional seismic inversion methods are difficult to accurately identify shale gas reservoirs with a thickness of less than 10 m. This presents great challenges to shale gas exploration and development in China. Seismic phase-controlled nonlinear stochastic inversion (SPCNSI) is related to the heterogeneity of underground media. With the constraints of the stratigraphic sequence or seismic facies models, the minimum value between the seismic model and seismic record can be solved through iterative processes. Based on the solved acoustic velocity in formation, the constraints for SPCNSI can be formed with the matching relationship between target layers and different sequences in three-dimensional space. The prediction resolution of an unconventional reservoir can be effectively improved by combining logging, geological and seismic information. The method is suitable for predicting thin shale gas reservoirs in complex geological structures. In this study, SPCNSI was developed to predict the thin-layer marine shale gas reservoir in the southeast of Chongqing; the horizontal and vertical distribution characteristics were proven in the Longmaxi Formation and the Wufeng Formation; thin layers with a thickness of less than 7 m were also discovered. According to the results, three sets of potential development areas were determined by the inversion method, and the accuracy and reliability of the method were verified by logging and productivity testing.
Due to the complicated interference sources and low signal-to-noise ratio of seismic records, conventional seismic inversion methods are difficult to accurately identify shale gas reservoirs with a thickness of less than 10 m. This presents great challenges to shale gas exploration and development in China. Seismic phase-controlled nonlinear stochastic inversion (SPCNSI) is related to the heterogeneity of underground media. With the constraints of the stratigraphic sequence or seismic facies models, the minimum value between the seismic model and seismic record can be solved through iterative processes. Based on the solved acoustic velocity in formation, the constraints for SPCNSI can be formed with the matching relationship between target layers and different sequences in three-dimensional space. The prediction resolution of an unconventional reservoir can be effectively improved by combining logging, geological and seismic information. The method is suitable for predicting thin shale gas reservoirs in complex geological structures. In this study, SPCNSI was developed to predict the thin-layer marine shale gas reservoir in the southeast of Chongqing; the horizontal and vertical distribution characteristics were proven in the Longmaxi Formation and the Wufeng Formation; thin layers with a thickness of less than 7 m were also discovered. According to the results, three sets of potential development areas were determined by the inversion method, and the accuracy and reliability of the method were verified by logging and productivity testing.
Record ID
Keywords
nonlinear stochastic inversion, reservoir prediction, seismic facies, shale gas, thin layer
Subject
Suggested Citation
Xie Q, Wu Y, Huang Q, Hu Y, Hu X, Guo X, Jia D, Wu B. Prediction of Marine Thin Shale Gas Reservoir with Seismic Phase-Controlled Nonlinear Stochastic Inversion. (2023). LAPSE:2023.36660v1
Author Affiliations
Xie Q: Chongqing College of Electronic Engineering, Chongqing 401331, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China [ORCID]
Wu Y: Chongqing College of Electronic Engineering, Chongqing 401331, China
Huang Q: Chongqing College of Electronic Engineering, Chongqing 401331, China
Hu Y: Chongqing College of Electronic Engineering, Chongqing 401331, China [ORCID]
Hu X: Chongqing College of Electronic Engineering, Chongqing 401331, China
Guo X: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Jia D: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Wu B: National and Local Joint Engineering Research Center for Shale Gas Exploration and Development, Chongqing Institute of Geology and Mineral Resources, Chongqing 401120, China
Wu Y: Chongqing College of Electronic Engineering, Chongqing 401331, China
Huang Q: Chongqing College of Electronic Engineering, Chongqing 401331, China
Hu Y: Chongqing College of Electronic Engineering, Chongqing 401331, China [ORCID]
Hu X: Chongqing College of Electronic Engineering, Chongqing 401331, China
Guo X: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Jia D: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Wu B: National and Local Joint Engineering Research Center for Shale Gas Exploration and Development, Chongqing Institute of Geology and Mineral Resources, Chongqing 401120, China
Journal Name
Processes
Volume
11
Issue
8
First Page
2301
Year
2023
Publication Date
2023-08-01
ISSN
2227-9717
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Original Submission
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PII: pr11082301, Publication Type: Journal Article
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LAPSE:2023.36660v1
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https://doi.org/10.3390/pr11082301
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[v1] (Original Submission)
Sep 20, 2023
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Sep 20, 2023
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Calvin Tsay
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