Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning
Abstract The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent i...
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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/s41598-024-63430-z |
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| _version_ | 1841544598594256896 |
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| author | Tao Liu Zongbao Liu Kejia Zhang Chunsheng Li Yan Zhang Zihao Mu Mengning Mu Mengting Xu Yue Zhang Xue Li |
| author_facet | Tao Liu Zongbao Liu Kejia Zhang Chunsheng Li Yan Zhang Zihao Mu Mengning Mu Mengting Xu Yue Zhang Xue Li |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Abstract The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency. |
| format | Article |
| id | doaj-art-88ffe4b39a904da1830920970039b52a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-88ffe4b39a904da1830920970039b52a2025-01-12T12:25:12ZengNature PortfolioScientific Reports2045-23222024-06-0114111610.1038/s41598-024-63430-zResearch on the generation and annotation method of thin section images of tight oil reservoir based on deep learningTao Liu0Zongbao Liu1Kejia Zhang2Chunsheng Li3Yan Zhang4Zihao Mu5Mengning Mu6Mengting Xu7Yue Zhang8Xue Li9School of Computer and Information Technology, Northeast Petroleum UniversitySchool of Earth Sciences, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversityExploration and Development Research Institute of Huabei Oilfield CompanyExploration and Development Research Institute of Huabei Oilfield CompanyExploration and Development Research Institute of Huabei Oilfield CompanyAbstract The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.https://doi.org/10.1038/s41598-024-63430-zTight oil reservoirCast thin section imageDeep learningImage generationReservoir evaluation |
| spellingShingle | Tao Liu Zongbao Liu Kejia Zhang Chunsheng Li Yan Zhang Zihao Mu Mengning Mu Mengting Xu Yue Zhang Xue Li Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning Scientific Reports Tight oil reservoir Cast thin section image Deep learning Image generation Reservoir evaluation |
| title | Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| title_full | Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| title_fullStr | Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| title_full_unstemmed | Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| title_short | Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| title_sort | research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning |
| topic | Tight oil reservoir Cast thin section image Deep learning Image generation Reservoir evaluation |
| url | https://doi.org/10.1038/s41598-024-63430-z |
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