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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Well Logging Technology
2023-12-01
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| Series: | Cejing jishu |
| Subjects: | |
| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5537 |
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| Summary: | 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. |
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| ISSN: | 1004-1338 |