Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks
Accurately establishing a 3D digital core model is of great significance in oil and gas production. The physical experiment method and numerical modeling method are common modeling methods. With the development of deep learning technology, a variety of deep learning algorithms have been applied to d...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/9159242 |
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| _version_ | 1832555209974349824 |
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| author | Qing Zhang Benqiang Wang Xusheng Liang Yizhen Li Feng He Yuexiang Hao |
| author_facet | Qing Zhang Benqiang Wang Xusheng Liang Yizhen Li Feng He Yuexiang Hao |
| author_sort | Qing Zhang |
| collection | DOAJ |
| description | Accurately establishing a 3D digital core model is of great significance in oil and gas production. The physical experiment method and numerical modeling method are common modeling methods. With the development of deep learning technology, a variety of deep learning algorithms have been applied to digital core modeling. The digital core modeling method based on generative adversarial neural networks (GANs) has attracted wide attention due to its good quality and simple generation process. The disadvantage of this method is that the network needs thousands of trainings to achieve acceptable results. For this reason, this paper proposes to use the pretrained GANs for digital core modeling training, which can greatly reduce the number of network training while ensuring the core modeling effect. We can use the presented method to quickly complete the training and use the trained generator model to obtain multiple digital cores. By analyzing the quality of the generated cores from multiple aspects, it is revealed that the properties of the generated cores are in good agreement with the ones of the real core samples. The results indicate the reliability of the pretrained GAN method. |
| format | Article |
| id | doaj-art-da83e384fc304d2a98766abd004d8ed8 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-da83e384fc304d2a98766abd004d8ed82025-02-03T05:49:21ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/9159242Digital Core Modeling Based on Pretrained Generative Adversarial Neural NetworksQing Zhang0Benqiang Wang1Xusheng Liang2Yizhen Li3Feng He4Yuexiang Hao5Shale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedShale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedShale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedShale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedShale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedShale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company LimitedAccurately establishing a 3D digital core model is of great significance in oil and gas production. The physical experiment method and numerical modeling method are common modeling methods. With the development of deep learning technology, a variety of deep learning algorithms have been applied to digital core modeling. The digital core modeling method based on generative adversarial neural networks (GANs) has attracted wide attention due to its good quality and simple generation process. The disadvantage of this method is that the network needs thousands of trainings to achieve acceptable results. For this reason, this paper proposes to use the pretrained GANs for digital core modeling training, which can greatly reduce the number of network training while ensuring the core modeling effect. We can use the presented method to quickly complete the training and use the trained generator model to obtain multiple digital cores. By analyzing the quality of the generated cores from multiple aspects, it is revealed that the properties of the generated cores are in good agreement with the ones of the real core samples. The results indicate the reliability of the pretrained GAN method.http://dx.doi.org/10.1155/2022/9159242 |
| spellingShingle | Qing Zhang Benqiang Wang Xusheng Liang Yizhen Li Feng He Yuexiang Hao Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks Geofluids |
| title | Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks |
| title_full | Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks |
| title_fullStr | Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks |
| title_full_unstemmed | Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks |
| title_short | Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks |
| title_sort | digital core modeling based on pretrained generative adversarial neural networks |
| url | http://dx.doi.org/10.1155/2022/9159242 |
| work_keys_str_mv | AT qingzhang digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks AT benqiangwang digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks AT xushengliang digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks AT yizhenli digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks AT fenghe digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks AT yuexianghao digitalcoremodelingbasedonpretrainedgenerativeadversarialneuralnetworks |