Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network
In order to simulate the response of electromagnetic wave resistivity logging while drilling efficiently in complex media and accelerate the inversion of logging data, the physical-informed neural network (PINN) is used to simulate the response of electromagnetic wave resistivity logging while drill...
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| Format: | Article |
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Editorial Office of Well Logging Technology
2023-12-01
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| Series: | Cejing jishu |
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| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5538 |
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| author | LIU Yang WANG Jian XU Delong |
| author_facet | LIU Yang WANG Jian XU Delong |
| author_sort | LIU Yang |
| collection | DOAJ |
| description | In order to simulate the response of electromagnetic wave resistivity logging while drilling efficiently in complex media and accelerate the inversion of logging data, the physical-informed neural network (PINN) is used to simulate the response of electromagnetic wave resistivity logging while drilling. PINN incorporates the governing equation into the loss function and transforms the problem of solving partial differential equation into optimization problem. PINN realizes the solution of partial differential equation. In numerical examples, the scattered field is obtained by PINN method. The influence of sampling method, activation function and network architecture on the accuracy of PINN results are studied. The PINN method is used to calculate the response of electromagnetic wave resistivity logging while drilling in high resistivity and intrusive formation models. The numerical results show that the response of electromagnetic wave logging while drilling based on PINN simulation is consistent with the finite element results. This method can be used to accurately solve the response of electromagnetic wave resistivity logging while drilling. |
| format | Article |
| id | doaj-art-9be3e694e5ec4062910f083f1cdd6d8d |
| institution | OA Journals |
| issn | 1004-1338 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Well Logging Technology |
| record_format | Article |
| series | Cejing jishu |
| spelling | doaj-art-9be3e694e5ec4062910f083f1cdd6d8d2025-08-20T02:33:23ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382023-12-0147665366110.16489/j.issn.1004-1338.2023.06.0021004-1338(2023)06-0653-09Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural NetworkLIU Yang0WANG Jian1XU Delong2China State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaChina State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaChina State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaIn order to simulate the response of electromagnetic wave resistivity logging while drilling efficiently in complex media and accelerate the inversion of logging data, the physical-informed neural network (PINN) is used to simulate the response of electromagnetic wave resistivity logging while drilling. PINN incorporates the governing equation into the loss function and transforms the problem of solving partial differential equation into optimization problem. PINN realizes the solution of partial differential equation. In numerical examples, the scattered field is obtained by PINN method. The influence of sampling method, activation function and network architecture on the accuracy of PINN results are studied. The PINN method is used to calculate the response of electromagnetic wave resistivity logging while drilling in high resistivity and intrusive formation models. The numerical results show that the response of electromagnetic wave logging while drilling based on PINN simulation is consistent with the finite element results. This method can be used to accurately solve the response of electromagnetic wave resistivity logging while drilling.https://www.cnpcwlt.com/#/digest?ArticleID=5538physics-informed neural networkelectromagnetic wave resistivity logging while drillinglogging responseelectromagnetic field simulation |
| spellingShingle | LIU Yang WANG Jian XU Delong Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network Cejing jishu physics-informed neural network electromagnetic wave resistivity logging while drilling logging response electromagnetic field simulation |
| title | Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network |
| title_full | Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network |
| title_fullStr | Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network |
| title_full_unstemmed | Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network |
| title_short | Simulation of Electromagnetic Wave Resistivity Logging While Drilling Based on the Physical-Informed Neural Network |
| title_sort | simulation of electromagnetic wave resistivity logging while drilling based on the physical informed neural network |
| topic | physics-informed neural network electromagnetic wave resistivity logging while drilling logging response electromagnetic field simulation |
| url | https://www.cnpcwlt.com/#/digest?ArticleID=5538 |
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