1887

Abstract

Summary

Spectral decomposition of seismic data provides valuable attributes for reservoir characterisation. Algorithms for spectral decomposition need to address the challenge of achieving high time-frequency resolution. Here, we introduce the smoothed pseudo Wigner-Ville distribution (SPWD) for the spectral decomposition of seismic data. The cross terms inherent in the Wigner-Ville distribution are efficiently overcome in SPWD. The performance of SPWD is demonstrated on synthetic data and 3D migrated seismic volumes from the Stratton field.

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/content/papers/10.3997/2214-4609.201801365
2018-06-11
2024-04-25
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References

  1. Castagna, J.P, Sun, S. 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]
  2. Chopra, S. and Marfurt, K.J.
    [2005] Seismic attributes — A historical perspective. Geophysics, 70(5), 3SO–28SO.
    [Google Scholar]
  3. Cohen, L.
    [1995] Time-frequency Analysis: Theory and Applications. Prentice-Hall, Inc.
    [Google Scholar]
  4. Flandrin, P.
    [1984] Some Features of Time-Frequency Representations of Multi-Component Signals. IEEE Internal Conference on Acoustic Speech and Signal Processing.
    [Google Scholar]
  5. Hardage, B.A., Levey, R.A., Pendleton, V, Simmons, J. and Edson, R.
    [1994] A 3-D seismic case history evaluating fluvially deposited thin-bed reservoirs in a gas-producing property. Geophysics, 59(11), 1650–1665.
    [Google Scholar]
  6. Nanda, D., Castagna, J.P. and Kouri, D.
    [2013] Spectral decomposition with Heisenberg’s minimum uncertainty wavelets. 83rd Annual International Meeting, SEG, Expanded Abstracts, 3340–3343.
    [Google Scholar]
  7. Nguyen, T. and Castagna, J.P.
    [2010] High resolution seismic reflectivity inversion. Journal of Seismic Exploration, 19, 303–320.
    [Google Scholar]
  8. Partyka, G.A.
    [2001] Seismic Thickness estimation: Three approaches, pros and cons. 71st Annual International Meeting, SEG, Expanded Abstracts, 503–506.
    [Google Scholar]
  9. Partyka, G.A., Gridley, J.M. and Lopez, J.
    [1999] Interpretational applications of spectral decomposition in reservoir characterization. The Leading Edge, 18(3), 353–360.
    [Google Scholar]
  10. Puryear, C.I., Portniaguine, O.N., Cobos, CM. and Castagna, J.P.
    [2012] Constrained least-squares spectral analysis: Application to seismic data. Geophysics, 77(5), V143–V167.
    [Google Scholar]
  11. 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–P25.
    [Google Scholar]
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