Post-stack multi-scale fracture prediction and characterization methods for granite buried hill reservoirs: a case study in the Pearl River Mouth Basin, South China Sea

Granite buried hill oil and gas reservoirs are relatively scarce worldwide, and the fine prediction and characterization of their fractures have always been a significant industry challenge. Particularly in the South China Sea region, large and thick granite buried-hill reservoirs are influenced by various geological processes such as weathering and tectonics, resulting in a complex internal fracture system. The seismic reflection characteristics exhibit high steepness, discontinuity, and significant amplitude differences, posing significant difficulties for the fine characterization of fractures. A systematic and comprehensive research approach has not yet been established. Therefore, this study considers the large granite-buried hill A reservoir in the South China Sea as a typical case study and proposes a multi-scale fracture fine prediction and characterization methodology system. The method starts with analyzing the fracture scale and genesis to refine the fracture scales identifiable by conventional seismic data. Based on this, the U-SegNet model and transfer learning are utilized to achieve fine detection of large-scale fractures. Meanwhile, using high-resolution ant tracking technology based on MVMD frequency division and sensitive attribute preferences realizes a fine prediction of medium-to-small-scale fractures. Furthermore, the discrete fracture network is used for fracture deterministic modeling, ranging from geometric morphology to percolation behavior. Ultimately, a post-stack seismic multi-scale fracture prediction and characterization workflow is established. The results indicate that the buried hill in the study area exhibits a high degree of fracture development with evident multi-scale characteristics. Among them, large-scale fractures have a relatively low development density, primarily oriented in the NW and NE directions; medium-to-small-scale fractures exhibit high-density and omnidirectional development. The development of fractures significantly improves the storage space and fluid flow capacity of the buried hill. Compared with traditional methods, the proposed method notably enhances the accuracy of characterizing the degree of fracture development, spatial morphology, and percolation behavior in the buried hill reservoir, providing a scientific basis for oil and gas exploration and development.

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Publication Details

Journal
Frontiers in Earth Science
Published
2024-08-19
DOI
https://doi.org/10.3389/feart.2024.1456122
Citations
2
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
1.07
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article

Post-stack multi-scale fracture prediction and characterization methods for granite buried hill reservoirs: a case study in the Pearl River Mouth Basin, South China Sea

Qiuyu Li, Cong Niu, Junping Liu, Huailai Zhou et al.
2 citations
Frontiers in Earth Science
Seismic Imaging and Inversion Techniques
1.07
article

Post-stack multi-scale fracture prediction and characterization methods for granite buried hill reservoirs: a case study in the Pearl River Mouth Basin, South China Sea

Qiuyu Li, Cong Niu, Junping Liu, Huailai Zhou, L. Liao
article en
2 citations

Abstract

Granite buried hill oil and gas reservoirs are relatively scarce worldwide, and the fine prediction and characterization of their fractures have always been a significant industry challenge. Particularly in the South China Sea region, large and thick granite buried-hill reservoirs are influenced by various geological processes such as weathering and tectonics, resulting in a complex internal fracture system. The seismic reflection characteristics exhibit high steepness, discontinuity, and significant amplitude differences, posing significant difficulties for the fine characterization of fractures. A systematic and comprehensive research approach has not yet been established. Therefore, this study considers the large granite-buried hill A reservoir in the South China Sea as a typical case study and proposes a multi-scale fracture fine prediction and characterization methodology system. The method starts with analyzing the fracture scale and genesis to refine the fracture scales identifiable by conventional seismic data. Based on this, the U-SegNet model and transfer learning are utilized to achieve fine detection of large-scale fractures. Meanwhile, using high-resolution ant tracking technology based on MVMD frequency division and sensitive attribute preferences realizes a fine prediction of medium-to-small-scale fractures. Furthermore, the discrete fracture network is used for fracture deterministic modeling, ranging from geometric morphology to percolation behavior. Ultimately, a post-stack seismic multi-scale fracture prediction and characterization workflow is established. The results indicate that the buried hill in the study area exhibits a high degree of fracture development with evident multi-scale characteristics. Among them, large-scale fractures have a relatively low development density, primarily oriented in the NW and NE directions; medium-to-small-scale fractures exhibit high-density and omnidirectional development. The development of fractures significantly improves the storage space and fluid flow capacity of the buried hill. Compared with traditional methods, the proposed method notably enhances the accuracy of characterizing the degree of fracture development, spatial morphology, and percolation behavior in the buried hill reservoir, providing a scientific basis for oil and gas exploration and development.

Frontiers in Earth ScienceVol. 12
China National Offshore Oil Corporation (China) (CN), Chengdu University of Technology (CN), China National Petroleum Corporation (China) (CN)
Life below water
Openalex Percentile: Top 25%
Seismic Imaging and Inversion Techniques
1.07
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