Research on spatial carving method of glutenite reservoir based on opacity voxel imaging
Abstract The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heteroge...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63643-2 |
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| author | Hu Zhao Zhong-wei Zhang Hong-wei Yang Guo-hua Wei |
| author_facet | Hu Zhao Zhong-wei Zhang Hong-wei Yang Guo-hua Wei |
| author_sort | Hu Zhao |
| collection | DOAJ |
| description | Abstract The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs. |
| format | Article |
| id | doaj-art-cd35d5f08625407daf91b74dc8721d10 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cd35d5f08625407daf91b74dc8721d102025-01-12T12:24:46ZengNature PortfolioScientific Reports2045-23222024-06-0114111310.1038/s41598-024-63643-2Research on spatial carving method of glutenite reservoir based on opacity voxel imagingHu Zhao0Zhong-wei Zhang1Hong-wei Yang2Guo-hua Wei3Natural Gas Geology Key Laboratory of Sichuan Province, Southwest Petroleum UniversitySchool of Geoscisence and Technology, Southwest Petroleum UniversityGeophysical Exploration Institute, Shengli Oilfield Company, SINOPECGeophysical Exploration Institute, Shengli Oilfield Company, SINOPECAbstract The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.https://doi.org/10.1038/s41598-024-63643-2OpacitySpatial carvingVoxel imagingSeismic attributesVolume attributes fusion |
| spellingShingle | Hu Zhao Zhong-wei Zhang Hong-wei Yang Guo-hua Wei Research on spatial carving method of glutenite reservoir based on opacity voxel imaging Scientific Reports Opacity Spatial carving Voxel imaging Seismic attributes Volume attributes fusion |
| title | Research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| title_full | Research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| title_fullStr | Research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| title_full_unstemmed | Research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| title_short | Research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| title_sort | research on spatial carving method of glutenite reservoir based on opacity voxel imaging |
| topic | Opacity Spatial carving Voxel imaging Seismic attributes Volume attributes fusion |
| url | https://doi.org/10.1038/s41598-024-63643-2 |
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