Applied Magnetic Resonance

, Volume 50, Issue 1–3, pp 73–101 | Cite as

A Hybrid Method for NMR Data Compression Based on Window Averaging (WA) and Principal Component Analysis (PCA)

  • Jiangfeng Guo
  • Ranhong XieEmail author
  • Huanhuan Liu
Original Paper


Prior to the advent of nuclear magnetic resonance (NMR) data inversion, a common approach for handling the large amount of raw echo data collected by NMR logging was data compression for improving the inversion speed. A fast compression method with a high compression ratio is required for processing NMR logging data. In this paper, we proposed a hybrid method to compress NMR data based on the window averaging (WA) and principal component analysis (PCA) methods. The proposed method was compared with the WA method and the PCA method in terms of the compression times of simulated one-, two-, and three-dimensional NMR data, the inversion times of compressed echo data, and the accuracy of NMR maps created with and without compression. We processed NMR log data and compared the inversion results with different compression methods. The results indicated that the proposed method with a high compression speed and a high compression ratio can be used for NMR data compression, and its accuracy depended on the precompressed echo number, and it is obvious that the method have practical applications for NMR data processing, especially for multi-dimensional NMR.



This project was funded by the National Natural Science Foundation of China (Grant No. 41674126), and China National Key Scientific and Technological Project for Oil & Gas and Coalbed Methane Development (Grant No. 2016ZX05031-001). The authors would like to thank the editors and reviewers for their constructive comments and suggestions.


  1. 1.
    G.R. Coates, L.Z. Xiao, M.G. Prammer, in NMR Logging Principles and Applications (Haliburton Energy Services Sea Gulf Press, Houston, 1999), pp. 1–28Google Scholar
  2. 2.
    K.J. Dunn, D.J. Bergman, G.A. LaTorraca, in Nuclear Magnetic Resonance: Petrophysical and Logging Applications (Pergamon, New York, 2002), pp. 1–10Google Scholar
  3. 3.
    M.D. Hürlimann, L. Venkataramanan, C. Flaum, J. Chem. Phys. 117, 10223–10232 (2002)ADSCrossRefGoogle Scholar
  4. 4.
    N. Heaton, H.N. Bachman, C.C. Minh, E. Decoster, J. Lavigne, J. White, R. Carmona, Petrophysics 49, 172–186 (2008)Google Scholar
  5. 5.
    J. Guo, R. Xie, Y. Zou, Y. Ding, J. Geophys. Eng. 13, 285–294 (2016)CrossRefGoogle Scholar
  6. 6.
    J.F. Guo, R.H. Xie, Y.L. Zou, Chin. J. Geophys. 59, 2703–2712 (2016). (in Chinese) Google Scholar
  7. 7.
    J. Guo, R. Xie, J. Nat. Gas Sci. Eng. 37, 502–511 (2017)CrossRefGoogle Scholar
  8. 8.
    M.G. Prammer, SPE Annual Technical Conference and Exhibition (Society of Petroleum Engineers, New Orleans, 1994), pp. 55–64.
  9. 9.
    A. Sezginer, Determining bound and unbound fluid volumes using nuclear magnetic resonance pulse sequences: U.S. Patent 5,363,041[P] (1994)Google Scholar
  10. 10.
    R. Sigal, Petrophysics 43, 38–46 (2002)Google Scholar
  11. 11.
    N.J. Heaton, C.C. Minh, J. Kovats, U. Guru, SPE Annual Technical Conference and Exhibition (Society of Petroleum Engineers, Houston, 2004), pp. 1–11.
  12. 12.
    L. Zhu, C. Zhang, Y. Wei, X. Zhou, Y. Huang, C. Zhang, Interpretation 5, T341–T350 (2017)CrossRefGoogle Scholar
  13. 13.
    R. Freedman, Method and apparatus for compressing data produced from a well tool in a wellbore prior to transmitting the compressed data uphole to a surface apparatus: U.S. Patent 5,381,092[P] (1995)Google Scholar
  14. 14.
    K.J. Dunn, G.A. LaTorraca, J. Magn. Reson. 140, 153–161 (1999)ADSCrossRefGoogle Scholar
  15. 15.
    Y.Q. Song, L. Venkataramanan, M.D. Hürlimann, M. Flaum, P. Frulla, C. Straley, J. Magn. Reson. 154, 261–268 (2002)ADSCrossRefGoogle Scholar
  16. 16.
    L. Venkataramanan, Y.Q. Song, M.D. Hürlimann, IEEE Trans. Signal Process. 50, 1017–1026 (2002)ADSMathSciNetCrossRefGoogle Scholar
  17. 17.
    J. Mitchell, T.C. Chandrasekera, L.F. Gladden, Prog. Nucl. Magn. Reson. Spectrosc. 62, 34–50 (2012)CrossRefGoogle Scholar
  18. 18.
    Y. Zou, R. Xie, Comput. Geosci. 19, 389–401 (2015)CrossRefGoogle Scholar
  19. 19.
    R. Bai, A. Cloninger, W. Czaja, P.J. Basser, J. Magn. Reson. 255, 88–99 (2015)ADSCrossRefGoogle Scholar
  20. 20.
    Y. Ding, R. Xie, Y. Zou, J. Guo, Appl. Magn. Reson. 47, 297–307 (2016)CrossRefGoogle Scholar
  21. 21.
    J.P. Butler, J.A. Reeds, S.V. Dawson, SIAM J. Numer. Anal. 18, 381–397 (1981)ADSMathSciNetCrossRefGoogle Scholar
  22. 22.
    É. Chouzenoux, S. Moussaoui, J. Idier, F. Mariette, IEEE Trans. Signal Process. 58, 6040–6051 (2010)ADSMathSciNetCrossRefGoogle Scholar
  23. 23.
    Y. Zou, R. Xie, Y. Ding, A. Arad, Geophysics 81, D1–D8 (2016)CrossRefGoogle Scholar
  24. 24.
    J. Guo, R. Xie, Y. Zou, G. Jin, L. Gao, C. Xu, Geophysics (2018). CrossRefGoogle Scholar
  25. 25.
    M. Prange, Y.Q. Song, J. Magn. Reson. 196, 54–60 (2009)ADSCrossRefGoogle Scholar
  26. 26.
    R. Salazar-Tio, B. Sun, Petrophysics 51, 208–218 (2010)Google Scholar
  27. 27.
    G.H. Golub, M. Heath, G. Wahba, Technometrics 21, 215–223 (1977)CrossRefGoogle Scholar
  28. 28.
    Y.L. Zou, R.H. Xie, A. Arad, Petrol. Sci. 38, 237–246 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of Petroleum (Beijing)BeijingChina
  2. 2.Key Laboratory of Earth Prospecting and Information TechnologyChina University of Petroleum (Beijing)BeijingChina
  3. 3.Huabei BranchChina Petroleum Logging CO. LTD.RenqiuChina

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