Junping Liu, Huailai Zhou, Luyao Liao, Cong Niu, Qiuyu Li
{"title":"花岗岩埋藏丘陵储层的叠后多尺度断裂预测和表征方法:南海珠江口盆地案例研究","authors":"Junping Liu, Huailai Zhou, Luyao Liao, Cong Niu, Qiuyu Li","doi":"10.3389/feart.2024.1456122","DOIUrl":null,"url":null,"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.","PeriodicalId":12359,"journal":{"name":"Frontiers in Earth Science","volume":"20 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Junping Liu, Huailai Zhou, Luyao Liao, Cong Niu, Qiuyu Li\",\"doi\":\"10.3389/feart.2024.1456122\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":12359,\"journal\":{\"name\":\"Frontiers in Earth Science\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Earth Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3389/feart.2024.1456122\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Earth Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3389/feart.2024.1456122","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
期刊介绍:
Frontiers in Earth Science is an open-access journal that aims to bring together and publish on a single platform the best research dedicated to our planet.
This platform hosts the rapidly growing and continuously expanding domains in Earth Science, involving the lithosphere (including the geosciences spectrum), the hydrosphere (including marine geosciences and hydrology, complementing the existing Frontiers journal on Marine Science) and the atmosphere (including meteorology and climatology). As such, Frontiers in Earth Science focuses on the countless processes operating within and among the major spheres constituting our planet. In turn, the understanding of these processes provides the theoretical background to better use the available resources and to face the major environmental challenges (including earthquakes, tsunamis, eruptions, floods, landslides, climate changes, extreme meteorological events): this is where interdependent processes meet, requiring a holistic view to better live on and with our planet.
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