1887
Volume 50, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

ABSTRACT

Seismic wavelet estimation is the bedrock of seismic-well tying and seismic inversion but remains a challenge. Huge amounts of effort have been expended on seismic wavelet estimation and determining the amplitude and phase spectrum is a time-consuming task. In this article, we develop a workflow to determine automatically the constant phase of an estimated wavelet. This workflow begins with statistical wavelet estimation and seismic-well tying. We then extract a new seismic wavelet with a constant phase by using the well and seismic data together. To obtain the best phase for the extracted wavelet using well and seismic data, we rotate the phase of the wavelet by a user-defined increment and perform automatic seismic-well tying for each phase-rotated wavelet. The phase that reaches the maximum correlation coefficient between the synthetic and seismic data is regarded as the best phase for wavelets in each iteration. We next update the time–depth relation using the best seismic-well tie (the maximum correlation coefficient). We repeat the wavelet estimation using well and seismic data, phase rotation, automatic seismic-well tying and time–depth updating until the difference between wavelets, and time–depth relationships, in the current and previous iterations is below a user-defined threshold.

© 2019 Australian Society of Exploration Geophysics
Loading

Article metrics loading...

/content/journals/10.1080/08123985.2019.1595577
2019-05-04
2026-01-13

  • From This Site

    /content/journals/10.1080/08123985.2019.1595577
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Buland, A., and H. Omre 2003 Jonint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model. Geophysical Prospecting51: 531–50. doi: 10.1046/j.1365‑2478.2003.00390.x
     https://doi.org/10.1046/j.1365-2478.2003.00390.x [Google Scholar]
  2. Edgar, J., and M. van der Baan 2011 How reliable is statistical wavelet estimation?Geophysics76 no. 4: V59–68. doi: 10.1190/1.3587220
     https://doi.org/10.1190/1.3587220 [Google Scholar]
  3. Hale, D. 2013 Dynamic warping of seismic images. Geophysics78 no. 2: S105–15. doi: 10.1190/geo2012‑0327.1
     https://doi.org/10.1190/geo2012-0327.1 [Google Scholar]
  4. Hampson, D. 2007 Theory of the Strata program: Technical report, CGG Hampson-Russell.
  5. Herrera, R.H., and M. van der Baan 2012 Guided seismic-to-well tying based on dynamic time warping, SEG Expanded Abstracts.
  6. Herrera, R.H., S. Fomel, and M. van der Baan 2014 Automatic approaches for seismic to well tying. Interpretation2 no. 2: SD9–17. doi: 10.1190/INT‑2013‑0130.1
     https://doi.org/10.1190/INT-2013-0130.1 [Google Scholar]
  7. Levy, S., and D.W. Oldenburg 1987 Automatic phase correction of common-midpoint stacked data. Geophysics52: 51–9. doi: 10.1190/1.1442240
     https://doi.org/10.1190/1.1442240 [Google Scholar]
  8. Müller, M. 2007 Dynamic time warping in information retrieval for music and motion. Springer, Berlin.
  9. Muñoz, A., and D. Hale 2012 Automatically tying well logs to seismic data, CWP-725.
  10. Muñoz, A., and D. Hale 2015 Automatic simultaneous multiple well ties. Geophysics80 no. 5: IM45–51. doi: 10.1190/geo2014‑0449.1
     https://doi.org/10.1190/geo2014-0449.1 [Google Scholar]
  11. Nyman, D.C., M.J. Parry, and R.D. Knight 1987 Seismic wavelet estimation using well control, SEG Expanded Abstracts, 211–13.
  12. Richard, V., and J. Brac 1988 Wavelet analysis using well log information. SEG Expanded Abstracts, 946–9.
  13. Sakoe, H., and S. Chiba 1978 Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing26: 43–9. doi: 10.1109/TASSP.1978.1163055
     https://doi.org/10.1109/TASSP.1978.1163055 [Google Scholar]
  14. van der Baan, M. 2008 Time-varying wavelet estimation and deconvolution by kurtosis maximization. Geophysics73 no. 2: V11–18. doi: 10.1190/1.2831936
     https://doi.org/10.1190/1.2831936 [Google Scholar]
  15. Walden, A.T., and R.E. White 1984 On errors of fit and accuracy in matching synthetic seismograms and seismic traces. Geophysical Prospecting32: 871–91. doi: 10.1111/j.1365‑2478.1984.tb00744.x
     https://doi.org/10.1111/j.1365-2478.1984.tb00744.x [Google Scholar]
  16. White, R.E. 1988 Maximum kurtosis phase correction. Geophysical Journal International95: 371–89. doi: 10.1111/j.1365‑246X.1988.tb00475.x
     https://doi.org/10.1111/j.1365-246X.1988.tb00475.x [Google Scholar]
  17. White, R., and R. Simm 2003 Tutorial: Good practice in well ties. EAGE first break21: 75–83.
     [Google Scholar]
  18. Wiggins, R. 1978 Minimum entropy deconvolution. Geoexploration16: 21–35. doi: 10.1016/0016‑7142(78)90005‑4
     https://doi.org/10.1016/0016-7142(78)90005-4 [Google Scholar]
  19. Wu, X., and G. Caumon 2017 Simultaneous multiple well-seismic ties using flattened synthetic and real seismograms. Geophysics82 no. 1: IM13–20. doi: 10.1190/geo2016‑0295.1
     https://doi.org/10.1190/geo2016-0295.1 [Google Scholar]
  20. Yilmaz, Ö 2001 Seismic data analysis: processing, inversion, and interpretation of seismic data. SEG, Tulsa.
/content/journals/10.1080/08123985.2019.1595577
Loading
The determination of the constant phase of seismic wavelet using automatic seismic-well tying
Exploration Geophysics 50, 245 (2019); https://doi.org/10.1080/08123985.2019.1595577
/content/journals/10.1080/08123985.2019.1595577
/content/journals/10.1080/08123985.2019.1595577
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): DTW; phase; Seismic-well tie; wavelet

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