Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning
The reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important bran...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2020/6641642 |
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| _version_ | 1832552514621276160 |
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| author | Yi Du Jie Chen Ting Zhang |
| author_facet | Yi Du Jie Chen Ting Zhang |
| author_sort | Yi Du |
| collection | DOAJ |
| description | The reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. The proposed method was evaluated on shale and sandstone samples by comparing multiple-point connectivity functions, variogram curves, permeability, porosity, etc. The experimental results show that the proposed method is of high efficiency while preserving similar features with the target image, shortening reconstruction time, and reducing the burdens on CPU. |
| format | Article |
| id | doaj-art-8a300389abf344c5a51e325b50fcfc61 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-8a300389abf344c5a51e325b50fcfc612025-02-03T05:58:27ZengWileyGeofluids1468-81151468-81232020-01-01202010.1155/2020/66416426641642Reconstruction of Three-Dimensional Porous Media Using Deep Transfer LearningYi Du0Jie Chen1Ting Zhang2College of Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaCollege of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, ChinaCollege of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, ChinaThe reconstruction of porous media is widely used in the study of fluid flows and engineering sciences. Some traditional reconstruction methods for porous media use the features extracted from real natural porous media and copy them to realize reconstructions. Currently, as one of the important branches of machine learning methods, the deep transfer learning (DTL) method has shown good performance in extracting features and transferring them to the predicted objects, which can be used for the reconstruction of porous media. Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. The proposed method was evaluated on shale and sandstone samples by comparing multiple-point connectivity functions, variogram curves, permeability, porosity, etc. The experimental results show that the proposed method is of high efficiency while preserving similar features with the target image, shortening reconstruction time, and reducing the burdens on CPU.http://dx.doi.org/10.1155/2020/6641642 |
| spellingShingle | Yi Du Jie Chen Ting Zhang Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning Geofluids |
| title | Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning |
| title_full | Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning |
| title_fullStr | Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning |
| title_full_unstemmed | Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning |
| title_short | Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning |
| title_sort | reconstruction of three dimensional porous media using deep transfer learning |
| url | http://dx.doi.org/10.1155/2020/6641642 |
| work_keys_str_mv | AT yidu reconstructionofthreedimensionalporousmediausingdeeptransferlearning AT jiechen reconstructionofthreedimensionalporousmediausingdeeptransferlearning AT tingzhang reconstructionofthreedimensionalporousmediausingdeeptransferlearning |