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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zou, Youlong Xie, Ranhong |
| Copyright Year | 2015 |
| Abstract | Nuclear magnetic resonance (NMR) technique has been widely used to reservoir evaluation and core analysis in oil industry. Rapid and stable inversion of NMR data is very important for NMR logging application. A rapid data compression method with high compression ratio can effectively improve the inversion speed of NMR data. This paper compared and analyzed the window averaging (WA) and singular value decomposition (SVD) methods for NMR data compression. The numerical results show that the WA method has the features of low compression ratio, low computational complexity, and less time-consuming; the SVD method has the features of high compression ratio, large amount of calculation, and more time-consuming. Combining the advantages of these two compression methods, this paper proposed a novel compression method which had achieved good application effects in NMR data compression. The novel method can not only ensure the high compression ratio, but also effectively reduces the time-consuming and possesses more prominent advantages in multi-dimensional NMR data compression. |
| Starting Page | 389 |
| Ending Page | 401 |
| Page Count | 13 |
| File Format | |
| ISSN | 14200597 |
| Journal | Computational Geosciences |
| Volume Number | 19 |
| Issue Number | 2 |
| e-ISSN | 15731499 |
| Language | English |
| Publisher | Springer International Publishing |
| Publisher Date | 2015-04-09 |
| Publisher Place | Cham |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | NMR logging Data compression Window averaging method Singular value decomposition method Joint compression method Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics Soil Science & Conservation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics |
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