Progress and Prospect for Machine Learning Applied in NMR Logging
Low-field nuclear magnetic resonance (NMR) technology has been widely used in petroleum engineering, which plays a critical role in reservoir evaluation and production prediction. However, the extremely weak signal and low signal-to-noise ratio (SNR) of low-field NMR leads to overlapping signals in...
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| Format: | Article |
| Language: | zho |
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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=5537 |
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| author | LUO Gang LUO Sihui XIAO Lizhi FU Shaoqing ZHANG Jiawei SHAO Rongbo |
| author_facet | LUO Gang LUO Sihui XIAO Lizhi FU Shaoqing ZHANG Jiawei SHAO Rongbo |
| author_sort | LUO Gang |
| collection | DOAJ |
| description | Low-field nuclear magnetic resonance (NMR) technology has been widely used in petroleum engineering, which plays a critical role in reservoir evaluation and production prediction. However, the extremely weak signal and low signal-to-noise ratio (SNR) of low-field NMR leads to overlapping signals in the NMR relaxation spectra and difficulties in the quantitative evaluation of fluid components. Therefore, it is very important to develop novel and practical NMR data processing methods to improve the application effects of NMR logging technology. With the rapid development of artificial intelligence technology, many scholars have proposed machine learning methods to improve the industry’s productivity. Firstly, this paper summarized the application and development of machine learning used in NMR logging. Secondly, the progress of machine learning methods applied in NMR logging data processing are analyzed, which are divided into three aspects including SNR enhancement, spectra resolution improvement, and quantitative fluid identification improvement. Finally, the future development of machine learning applied NMR logging data processing is summarized and recommended. |
| format | Article |
| id | doaj-art-27a40e4af9bc4a1ea73efc1047373a60 |
| institution | Kabale University |
| 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-27a40e4af9bc4a1ea73efc1047373a602025-08-20T03:47:40ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382023-12-0147664365210.16489/j.issn.1004-1338.2023.06.0011004-1338(2023)06-0643-10Progress and Prospect for Machine Learning Applied in NMR LoggingLUO Gang0LUO Sihui1XIAO Lizhi2FU Shaoqing3ZHANG Jiawei4SHAO Rongbo5National Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaNational Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaNational Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaWell Logging Technology Institute, China National Logging Corporation, Beijing 102206, ChinaNational Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaNational Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaLow-field nuclear magnetic resonance (NMR) technology has been widely used in petroleum engineering, which plays a critical role in reservoir evaluation and production prediction. However, the extremely weak signal and low signal-to-noise ratio (SNR) of low-field NMR leads to overlapping signals in the NMR relaxation spectra and difficulties in the quantitative evaluation of fluid components. Therefore, it is very important to develop novel and practical NMR data processing methods to improve the application effects of NMR logging technology. With the rapid development of artificial intelligence technology, many scholars have proposed machine learning methods to improve the industry’s productivity. Firstly, this paper summarized the application and development of machine learning used in NMR logging. Secondly, the progress of machine learning methods applied in NMR logging data processing are analyzed, which are divided into three aspects including SNR enhancement, spectra resolution improvement, and quantitative fluid identification improvement. Finally, the future development of machine learning applied NMR logging data processing is summarized and recommended.https://www.cnpcwlt.com/#/digest?ArticleID=5537nuclear magnetic resonance loggingmachine learningdeep learningdata processinginterpretation and application |
| spellingShingle | LUO Gang LUO Sihui XIAO Lizhi FU Shaoqing ZHANG Jiawei SHAO Rongbo Progress and Prospect for Machine Learning Applied in NMR Logging Cejing jishu nuclear magnetic resonance logging machine learning deep learning data processing interpretation and application |
| title | Progress and Prospect for Machine Learning Applied in NMR Logging |
| title_full | Progress and Prospect for Machine Learning Applied in NMR Logging |
| title_fullStr | Progress and Prospect for Machine Learning Applied in NMR Logging |
| title_full_unstemmed | Progress and Prospect for Machine Learning Applied in NMR Logging |
| title_short | Progress and Prospect for Machine Learning Applied in NMR Logging |
| title_sort | progress and prospect for machine learning applied in nmr logging |
| topic | nuclear magnetic resonance logging machine learning deep learning data processing interpretation and application |
| url | https://www.cnpcwlt.com/#/digest?ArticleID=5537 |
| work_keys_str_mv | AT luogang progressandprospectformachinelearningappliedinnmrlogging AT luosihui progressandprospectformachinelearningappliedinnmrlogging AT xiaolizhi progressandprospectformachinelearningappliedinnmrlogging AT fushaoqing progressandprospectformachinelearningappliedinnmrlogging AT zhangjiawei progressandprospectformachinelearningappliedinnmrlogging AT shaorongbo progressandprospectformachinelearningappliedinnmrlogging |