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Applied Magnetic Resonance

, Volume 47, Issue 3, pp 297–307 | Cite as

NMR Data Compression Method Based on Principal Component Analysis

  • Yejiao Ding
  • Ranhong Xie
  • Youlong Zou
  • Jiangfeng Guo
Article

Abstract

Hundreds of thousands of echo data are collected in nuclear magnetic resonance (NMR) logging. In order to get the formation information, such as porosity, permeability, fluid type, fluid saturation, pore size distribution, etc., those NMR data need to be inversed. Generally, compression is implemented to the gathered significant amounts of NMR echo data before they are inversed to reduce the inversion computation. This paper puts forward a new kind of NMR echo data compression method based on the principle of principal component analysis (PCA). Aiming at losing the minimum information, original echo data were compressed by retaining those who contribute the largest amounts of information for reflecting the formation characteristics, and eliminating those who contribute little or even are redundant. One-dimensional and two-dimensional NMR echo data were simulated, and then compressed, respectively, using the PCA method. The NMR echo data before and after PCA compression were inversed respectively, and the inversion results of compressed and uncompressed were compared. The result showed that the PCA method could be used to compress the NMR echo data without losing much information even under a high compression ratio.

Keywords

Nuclear Magnetic Resonance Inversion Result Kernel Matrix Nuclear Magnetic Resonance Data Principal Component Analysis Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This project was funded by the National Natural Science Foundation of China (41272163) and the National Natural Science Foundation of China–China National Petroleum Corporation Petrochemical Engineering United Fund (U1262114).

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Yejiao Ding
    • 1
  • Ranhong Xie
    • 1
  • Youlong Zou
    • 1
  • Jiangfeng Guo
    • 1
  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of PetroleumBeijingPeople’s Republic of China

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