| Presentation | 2022-03-03 Estimating the permeability of rocks using three-dimensional CNN and ResNet Taro Kamano, Yutaka Jitsumatsu, Takeshi Tsuji, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Investigating the permeability and elastic wave velocity of rocks covering the surface of the earth is central in petroleum resource exploration, CO$_2$ storage, and earthquake and volcanic prediction, and is the basis of geophysical surveys. Among them, the permeability is one of the most basic parameters to evaluate the reservoir. Laboratory experiments for permeability estimation is time and cost-consuming to load high-pressure water on the excavated rock. In recent years, research is progressing to predict the permeability by CT scanning rocks and numerical analysis. On the other hand, numerical analysis also has the drawback that the computational cost of numerical simulation based on fluid mechanics is very high when the resolution of the digital rock is increased. Very recently, estimation of physical property of rock, such as permeability and elastic wave velocity, using deep learning by preparing a large amount of raw data and their feature values is conducted. In this paper, we report the results of predictions of permeability based on 3-dimensional convolutional neural network (CNN) and ResNet model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Deep Learning / Digital Rock Physics / Permeability Estimation |
| Paper # | NC2021-60 |
| Date of Issue | 2022-02-23 (NC) |
| Conference Information | |
| Committee | MBE / NC |
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| Conference Date | 2022/3/2(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Ryuhei Okuno(Setsunan Univ.) / Rieko Osu(Waseda Univ.) |
| Vice Chair | Junichi Hori(Niigata Univ.) / Hiroshi Yamakawa(Univ of Tokyo) |
| Secretary | Junichi Hori(Osaka Electro-Communication Univ) / Hiroshi Yamakawa(ATR) |
| Assistant | Jun Akazawa(Meiji Univ. of Integrative Medicine) / Emi Yuda(Tohoku Univ) / Nobuhiko Wagatsuma(Toho Univ.) / Tomoki Kurikawa(KMU) |
| Paper Information | |
| Registration To | Technical Committee on ME and Bio Cybernetics / Technical Committee on Neurocomputing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Estimating the permeability of rocks using three-dimensional CNN and ResNet |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Digital Rock Physics |
| Keyword(3) | Permeability Estimation |
| 1st Author's Name | Taro Kamano |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Yutaka Jitsumatsu |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 3rd Author's Name | Takeshi Tsuji |
| 3rd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| Date | 2022-03-03 |
| Paper # | NC2021-60 |
| Volume (vol) | vol.121 |
| Number (no) | NC-390 |
| Page | pp.pp.74-79(NC), |
| #Pages | 6 |
| Date of Issue | 2022-02-23 (NC) |