1887

Abstract

Summary

Continuous wavelet transform (CWT) as a seismic time-frequency analysis technique with multi-resolution characteristics has been widely used in seismic interpretation, such as hydrocarbon detection. With the increased degree of oil and gas exploration, exploration targets are gradually shifting to the complex oil and gas reservoirs such as lithologic and structural-lithologic reservoirs. In this case, CWT method has become increasingly unable to meet the accuracy and resolution requirements of the hydrocarbon detection due to Heisenberg uncertainty principle. Therefore, we study an improved CWT based on reassignment method. The basic idea of this method is to reassign the time-frequency energy of each point in a CWT spectrum to a new coordinate nearer to the actual time-frequency location. Via this process, the reassigned spectrum is much more concentrated than the CWT spectrum and becomes a sparse representation of the signal. The synthetic data and field data processing results show that the high-resolution spectrum decomposition method based on the reassigned continuous wavelet transform (RCWT) can more accurately depict the time-frequency characteristics of the signal, and can well meet the accuracy and resolution requirements of hydrocarbon detection of complex structural-lithologic reservoir.

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/content/papers/10.3997/2214-4609.201901300
2019-06-03
2020-07-07
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References

  1. Allen, J. B
    . [1982] Application of the short-time Fourier transform to speech processing and spectral analysis. Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP, 1012–1015.
    [Google Scholar]
  2. Castagna, J. P., Sun, S. J. and Siegfried, R. W
    . [2003] Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22(2), 120–127.
    [Google Scholar]
  3. Flandrin, P., Auger, F. and Chassande-Mottin, E
    . [2003] Time-frequency reassignment: From principles to algorithms. Applications in Time-Frequency Signal Processing, 179–203.
    [Google Scholar]
  4. Kodera, K., Villedary, C. D. and Gendrin, R
    . [1976] A new method for the numerical analysis of non-stationary signals. Physics of the Earth and Planetary Interiors, 12(2), 142–150.
    [Google Scholar]
  5. Marfurt, K. J. and Kirlin, R. L
    . [2001] Narrow-band spectral analysis and thin-bed tuning. Geophysics, 66, 1274–83.
    [Google Scholar]
  6. Partyka, G., Gridley, J. and Lopez, J
    . [1999] Interpretational application of spectral decomposition in reservoir characterization. The Leading Edge, 18(3), 353–360.
    [Google Scholar]
  7. Portniaguine, O. and Castagna, J
    . [2004] Inverse spectral decomposition. 74th Annual International Meeting, SEG, Expanded Abstracts, 1786–1789.
    [Google Scholar]
  8. Rioul, O. and Flandrin, P
    . [1992] Time-scale energy distributions: a general class extending wavelet transforms. Signal Processing IEEE Transactions on, 40(7), 1746–1757.
    [Google Scholar]
  9. Sinha, S., Routh, P. S., Anno, P. D. and Castagna, J. P
    . [2005] Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, 70(6), P19–25.
    [Google Scholar]
  10. Stockwell, R. G., Mansinha, L. and Lowe, R. P
    . [1996] Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44(4), 998–1001.
    [Google Scholar]
  11. Wu, X. Y. and Liu, T. Y
    . [2010] Seismic spectral decomposition and analysis based on Wigner–Ville distribution for sandstone reservoir characterization in West Sichuan depression. Journal of Geophysics and Engineering, 7(2), 126–134.
    [Google Scholar]
  12. Zhang, X. W., Han, L. G., Wang, Y. and Shan, G. Y
    . [2010] A fast matching pursuits algorithm of seismic spectral decomposition and its application. Geophysical Prospecting for Petroleum, 49(1), 1–6.
    [Google Scholar]
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