Abstract
To improve the de-noising effects of low signal to noise ratio (SNR) nuclear magnetic resonance (NMR) log data, improve the calculating precision of the porosity parameters of the reservoirs, this paper attempts to apply the wavelet packet domain adaptive filtering algorithm to de-noise the NMR log data. First of all, the algorithm is interpreted in detail. And then, the de-noise off phenomenon is analyzed in the de-noising process of simulant and NMR echo data using the wavelet (packet) domain adaptive filtering algorithm. The factors affecting the occasion of the phenomenon are studied systematically, and a variable order processing scheme is proposed to eliminate the influence of the existence of the de-noise off phenomenon on the inversed T2 spectra. As a result, the effectiveness of the algorithm is verified by the application in the numerical simulation and NMR log data, respectively. The results indicated that, comparing with wavelet domain adaptive filtering algorithm, wavelet packet domain adaptive filtering algorithm is more suitable for low SNR-NMR echo data de-noising.
Similar content being viewed by others
References
C. M. Edwards, S. Chen, Society of Petrophysicists and Well Log Analysts 37th Annual Logging Symposium. RR (1996)
O.A. Ahmed, M.M. Fahmy, IEEE T. Med. Imaging. 20, 1018–1025 (2001)
R.H. Xie, Y.B. Wu, K. Liu, L.Z. Xiao, J. Geophys. Eng. (2014). doi:10.1088/1742-2132/11/3/035003
N. Serban, Comput. Stat. Data. An. 54, 1051–1065 (2010)
Q.M. Xie, L.Z. Xiao, L.J. Cheng, J.F. Liu, H.Y. Li, F. Deng, Appl. Magn. Reson. 44, 1381–1391 (2013)
L.Z. Xiao, Q.M. Xie, R.H. Xie, W.G. Pan, Chinese J. Geophys. 56, 3943–3952 (2013). (in Chinese)
Y.B. Wu, R.H. Xie, L.Z. Xiao, Adv. Mater. Res. 588, 814–817 (2012)
M. Dentino, J. Mccool, B. Widrow, P IEEE. 66, 1658–1659 (1978)
S. Narayan, A.M. Peterson, M.J. Narasimha, IEEE T. ASSP. 31, 609–615 (1983)
N. Ahmed, T. Natarajan, K.R. Rao, IEEE T. Comput. 100, 90–93 (1974)
K.R. Rao, N. Ahmed, IEEE Int. Conf. ASSP. 1, 136–140 (1976)
E.R. Ferrara, IEEE T. ASSP. 28, 474–475 (1980)
W. K. Jenkins, J. R. Kreidle, IEEE Int. Symp. Circuits Syst., 875–878 (1986)
S. Hosur, A.H. Tewfik, IEEE Int. Conf. ASSP. 3, 508–510 (1993)
N. Erdol, F. Basbug, IEEE T. Signal Process. 44, 2163–2171 (1996)
T. Aboulnasr, K. Mayyas, IEEE T. Signal Process. 45, 631–639 (1997)
S. Hosur, A.H. Tewfik, IEEE T. Signal Process. 45, 617–630 (1997)
M. V. Wickerhauser, INRIA lectures on wavelet packet algorithms (Lions P-L ed, France, 1991), pp. 31–99
R.R. Coifman, Y. Meyer, V. Wickerhauser, Wavelet analysis and signal processing (Jones and Bartlett, Boston, 1992), pp. 153–178
S.G. Mallat, T. Am. Math. Soc. 315, 69–87 (1989)
S.G. Mallat, IEEE T. Pattern Anal. 11, 674–693 (1989)
S.G. Mallat, IEEE T. ASSP. 37, 2091–2110 (1989)
B. Widrow, M.E. Hoff, Adaptive switching circuits (MIT Press, USA, 1988), pp. 123–134
G.R. Coates, L.Z. Xiao, M.G. Prammer, N.M.R. Logging, Principles and Applications (Gulf Professional Publishing, Texas, 2000), p. 51
Acknowledgments
This work is supported by the National Natural Science Foundation of China—China National Petroleum Corporation Petrochemical Engineering United Fund (U1262114) and the National Natural Science Foundation of China (41272163).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Meng, X., Xie, R. & Liu, M. NMR Log Data De-noising Method Based on a Variable Order Wavelet Packet Domain Adaptive Filtering. Appl Magn Reson 46, 1265–1282 (2015). https://doi.org/10.1007/s00723-015-0715-y
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00723-015-0715-y