1887
Volume 64, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

In tight gas sands, the signal‐to‐noise ratio of nuclear magnetic resonance log data is usually low, which limits the application of nuclear magnetic resonance logs in this type of reservoir. This project uses the method of wavelet‐domain adaptive filtering to denoise the nuclear magnetic resonance log data from tight gas sands. The principles of the maximum correlation coefficient and the minimum root mean square error are used to decide on the optimal basis function for wavelet transformation. The feasibility and the effectiveness of this method are verified by analysing the numerical simulation results and core experimental data. Compared with the wavelet thresholding denoise method, this adaptive filtering method is more effective in noise filtering, which can improve the signal‐to‐noise ratio of nuclear magnetic resonance data and the inversion precision of transverse relaxation time spectrum. The application of this method to nuclear magnetic resonance logs shows that this method not only can improve the accuracy of nuclear magnetic resonance porosity but also can enhance the recognition ability of tight gas sands in nuclear magnetic resonance logs.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12333
2015-10-20
2024-04-28
Loading full text...

Full text loading...

References

  1. AghayanA., SiahKoohiH.R. and RaissiH.T.2012. Seismic denoising using adaptive Wiener filter in redundant‐lifting wavelet domain. Istanbul International Geophysical Conference and Oil & Gas Exhibition, pp. 1–4. Society of Exploration Geophysicists and The Chamber of Geophysical Engineers of Turkey.
    [Google Scholar]
  2. AkkurtR., GuilloryA.J., VinegarH.J. and TutunjianP.N.1995. NMR logging of natural gas reservoirs, paper N. In: 36th Annual Logging Symposium Transactions. Society of Professional Well Log Analysts.
    [Google Scholar]
  3. Al‐MahrooqiS., MookerjeeA., WaltonW., ScholtenS., ArcherR., Al‐BusaidiJ.et al. 2011. Well logging and formation evaluation challenges in the deepest well in the Sultanate of Oman (HPHT tight sand reservoirs). Middle East Unconventional Gas Conference and Exhibition, Muscat, Oman, SPE 142415.
  4. AttallahS.2006. The wavelet transform‐domain LMS adaptive filter with partial subband‐coefficient updating. IEEE Transactions on Circuits and Systems: Express Briefs53, 8–12.
    [Google Scholar]
  5. CastilloP., OuL. and PrasadM.2012. Petrophysical description of tight gas sands. SEG Las Vegas Annual Meeting.
  6. CoatesG.R., XiaoL.Z. and PrammerM.G.1999. NMR Logging Principles & Applications. Halliburton Energy Services.
    [Google Scholar]
  7. CobasJ., TahocesP.G., Martin‐PastorM., PenedoM. and JavierS.2004. Wavelet‐based ultra‐high compression of multi‐dimensional NMR data sets. Journal of Magnetic Resonance168, 288–295.
    [Google Scholar]
  8. DentinoM., McCoolJ. and WidrowB.1978. Adaptive filtering in the frequency domain. Proceedings of the IEEE66, 1658–1659.
    [Google Scholar]
  9. DjermouneE.H., TomczakM. and BrieD.2014. NMR data analysis: a time‐domain parametric approach using adaptive subband decomposition. Oil & Gas Science and Technology–Revue d'IFP Energies nouvelles69, 229–244.
    [Google Scholar]
  10. DonohoD.L. and JohnstoneI.M.1994. Ideal spatial adaptation via wavelet shrinkage. Biometrika81, 425–455.
    [Google Scholar]
  11. DunnK.J., BergmanD.J. and LaTorracaG.A.2002. Nuclear Magnetic Resonance: Petrophysical and Logging Applications. Elsevier Science.
    [Google Scholar]
  12. ErdolN. and BasbugF.1996. Wavelet transform based adaptive filters: analysis and new results. IEEE Transactions on Signal Processing44, 2163–2171.
    [Google Scholar]
  13. GaoY.B., ZhaD.F. and JiangJ.L.2008. Selecting criteria and application of optimal wavelet basis regarding stable‐distribution signals. Commutations Technology41, 185–187.
    [Google Scholar]
  14. GoupillaudP., GrossmanA. and MorletJ.1984. Cycle‐octave and related transforms in seismic signal analysis. Seismic Signal Analysis and Discrimination Ш; 23, 85–102.
    [Google Scholar]
  15. HolditchS.A.2006. Tight gas sands. Journal of Petroleum Technology58, 86–93.
    [Google Scholar]
  16. MallatS.1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence11, 674–693.
    [Google Scholar]
  17. MallatS.2009. A Wavelet Tour of Signal Processing—The Sparse Way, 3rd edn.Academic Press.
    [Google Scholar]
  18. PhamM.Q., ChauxC., DuvalL. and PesquetJ.2014. A primal‐dual proximal algorithm for sparse template‐based adaptive filtering: application to seismic multiple removal. IEEE Transactions on Signal Processing62(16), 4256–4269.
    [Google Scholar]
  19. PrammerM.G., MardonD., CoatesG.R. and MillerM.N.1995. Lithology‐independent gas detection by gradient‐NMR logging. In: SPE Annual Technical Conference Proceedings, SPE‐30562. Society of Petroleum Engineers.
  20. RognerH.H.1997. An assessment of world hydrocarbon resources. Annual Review Energy Environment22, 217–262.
    [Google Scholar]
  21. SacchiM.D. and NaghizadehM.2009. Adaptive linear prediction for random noise attenuation. SEG Houston International Exposition and Annual Meeting, 3347–3351.
  22. SinghB.N. and TiwariA.K.2006. Optimal selection of wavelet basis function applied EGG signal de‐noising. Digital Signal Processing16, 275–287.
    [Google Scholar]
  23. TchambazM.2009. Optimum logging programs in tight sands. EUROPEC/EAGE Annual Conference and Exhibition, Amsterdam, The Netherlands.
  24. VentosaS., Le RoyS., HuardI., PicaA., RabesonH., RicarteP.et al. 2012. Adaptive multiple subtraction with wavelet‐based complex unary Wiener filters. Geophysics77, 183–192.
    [Google Scholar]
  25. WangX.W., DongG.B. and XieG.H.2008. A new de‐noising method of NMR FID signals based on wavelet transform. Nuclear Electronics & Detection Technology28, 364–370.
    [Google Scholar]
  26. WenT., RuiX., Ling‐lingZ. and Xiao‐gangW.2011. Comparison and limitation analysis of approaches for porosity evaluation from NMR and three porosity logs in low permeability gas sands with bad borehole. Middle East Unconventional Gas Conference and Exhibition, Muscat, Oman, SPE 141040.
  27. WidrowB., GloverJrJ.R., McCoolJ.M., KaunitzJ., WilliamsC.S., HearnR.H.et al. 1975. Adaptive noise cancelling: principles and applications. Proceedings of the IEEE63, 1692–1716.
    [Google Scholar]
  28. WidrowB., McCoolJ.M., LarimoreM.G. and JohnsonJrC.R.1976. Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proceedings of the IEEE64, 1151–1162.
    [Google Scholar]
  29. WinklerM., FreemanJ., QuinetE. and CaputiM.2006. Evaluation tight gas reservoirs with NMR—the perception, the reality and how to make it working. 47th Annual Logging Symposium, Veracruz, Mexico, pp. 4–7. Society of Petrophysicists and Well Log Analysts.
  30. WuL., KongL. and ChengJ.J.2011. Signal de‐noising algorithm design in NMR logging based on wavelet transform. Instrument Technique and Sensor10, 71–73.
    [Google Scholar]
  31. XieQ.M., XiaoL.Z. and LiaoG.Z.2010. Application of SURE algorithm to echo train de‐noising in low field NMR logging. Chinese Journal of Geophysics (Chinese Edition) 53, 2776–2783.
    [Google Scholar]
  32. XieR.H., WuY.B., LiuK., LiuM. and XiaoL.Z.2014. De‐noising methods for NMR logging echo signals based on wavelet transform. Journal of Geophysics and Engineering11, 1742–2132.
    [Google Scholar]
  33. XuL., ZhangD., WangK., LiN. and WangX.2007. Baseline wander correction in pulse waveforms using wavelet‐based cascaded adaptive filter. Computers in Biology and Medicine37, 716–731.
    [Google Scholar]
  34. ZhengC. and ZhangY.2007. Low‐field pulsed NMR signal denoising based on wavelet transform. IEEE Signal Processing and Communications Applications15, 1–4.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12333
Loading
/content/journals/10.1111/1365-2478.12333
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error