Advertisement

Singular Spectrum-Based Filtering to Enhance the Resolution of Seismic Attributes

  • R. K. Tiwari
  • R. Rekapalli
Chapter
  • 16 Downloads

Abstract

In the previous chapter, we have discussed the singular spectrum-based de-noising, filtering and data gap filling algorithms to enhance signal to noise ratio (S/N) of 2D and 3D seismic data for better interpretation of subsurface geological structures. Quantitative interpretation of 2D seismic sections, 3D volumes and horizon time structures could be further improved by performing seismic attributes analyses. Seismic attributes have been in practice for more than five decades for accurate structural and stratigraphic interpretation of seismic data (Taner et al. 1979; Chopra and Marfurt 2005, 2007; van Hoek et al. 2010; Ha et al. 2017). There are several classifications of seismic attributes (Brown 1996; Chen and Sidney 1997a, b; Barnes 1999; Chopra and Marfurt 2005; Subrahmanyam and Rao 2008). Among these, there are two fundamental categories namely: (i) geometric and (ii) physical attributes (Tarner et al. 1994). Geometric attributes such as dip, azimuth and continuity/similarity are used to identify the geometrical characteristics of subsurface strata. Physical attributes have been used to study the lithology of the subsurface by analyzing amplitude, phase, and frequency content of the seismic data. The attributes computed from the arrival times of seismic waves reflected from subsurface boundaries provide structural information, whereas the attributes computed from the seismic amplitudes from the subsurface boundaries are more sensitive to stratigraphy. Chopra and Marfurt (2005, 2007) have discussed on several classification schemes of seismic attributes, which readers can refer for more details on attribute classification. Although the attributes provide the robust and reliable information on structural and physical nature of subsurface material, the noise present in the data significantly misleads the attribute-based interpretations. In view of the importance of seismic attributes and their response to the noise present in the data, the robustness of singular spectrum-based filtering methods was demonstrated for better resolution in seismic attribute analysis. Here, in this chapter, we discuss application of SSA-based TSSSA and MSSA methods to 2D and 3D post stack seismic data to deal with interpretation of subsurface features. The chapter is thus divided into the following:

References

  1. Al-Dossary, S. and Marfurt, K.J. (2006). 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics, 71(5), 41–51.ADSCrossRefGoogle Scholar
  2. Barnes, A.E. (Ed.). (2016). Handbook of poststack seismic attributes. Society of Exploration Geophysicists.Google Scholar
  3. Barnes, A.E. (1999). Seismic attributes past, present, and future. In: SEG Technical Program Expanded Abstracts 1999 (pp. 892–895). Society of Exploration Geophysicists.Google Scholar
  4. Brown, A.R. (1996). Seismic attributes and their classification. The leading edge, 15(10), 1090–1090.Google Scholar
  5. Chadwick, A., Williams, G., Delepine, N., Clochard, V., Labat, K., Sturton, S., Buddensiek, M.-L., Dillen, M., Nickel, M., Lima, A.L., Arts, R., Neele, F. and Rossi, G. (2010). Quantitative analysis of time-lapse seismic monitoring data at the Sleipner CO2 storage operation. The Leading Edge, 29, 170–177.  https://doi.org/10.1190/1.3304820.CrossRefGoogle Scholar
  6. Chadwick, R.A., Arts, R., Eiken, O., Kirby, G.A., Lindeberg, E. and Zweigel, P. (2004). 4D seismic imaging of an injected CO2 plume at the Sleipner Field, central North Sea. In: R.J. Davies (ed.), 3D seismic technology: Application to the exploration of sedimentary basins. London, UK. Geological Society of London Memoir, 29, 311–320.Google Scholar
  7. Chen, Q. and Sidney, S. (1997a). Seismic attribute technology for reservoir forecasting and monitoring. The Leading Edge, 16(5), 445–448.Google Scholar
  8. Chen, Q. and Sidney, S. (1997b). Advances in seismic attribute technology. In: :SEG Technical Program Expanded Abstracts 1997 (pp. 730–733). Society of Exploration Geophysicists.Google Scholar
  9. Chopra, S. and Marfurt, K.J. (2007). Volumetric curvature attributes add value to 3D seismic data interpretation. The Leading Edge, 26(7), 856–867.CrossRefGoogle Scholar
  10. Chopra, S. and Marfurt, K.J. (2005). Seismic attributes—A historical perspective. Geophysics, 70(5), 3SO–28SO.Google Scholar
  11. Fomel, S. and Jin, L. (2009). Time-lapse image registration using the local similarity attribute. Geophysics, 74(2), A7–A11.Google Scholar
  12. Ha, T., Wallet, B. and Marfurt, K. (2017). Seismic interpretation of the Exmouth Plateau, North Carnarvon Basin, Australia: An application of data conditioning, seismic attributes, and self-organizing map on 2D data. In: SEG Technical Program Expanded Abstracts 2017 (pp. 2117–2121). Society of Exploration Geophysicists.Google Scholar
  13. Michelena, R.J., González SM, E. and Capello de P, M. (1998). Similarity analysis: a new tool to summarize seismic attributes information. The Leading Edge, 17(4), 545–548.Google Scholar
  14. Rekapalli, R. and Tiwari, R.K. (2015b). Singular Spectrum Based Algorithms for denoising 2D and 3D Seismic Data. Journal of Geophysics, 3, 129–137.Google Scholar
  15. Rekapalli, R., Priyadarshi, Shubham K. and Tiwari, R.K. (2015b). Assessment of Singular Spectrum and Wavelet based denoising schemes in generalized inversion based seismic wavelet estimation. Society of Petroleum Geophysics, Jaipur-India,  https://doi.org/10.13140/RG.2.1.4344.2640.
  16. Rekapalli, R., Tiwari, R.K. and Sen, M.K. (2016). Fault identification by diffraction separation from seismic reflection data using time slice SSA based algorithm. In: SEG Technical Program. Expanded Abstracts 2016, 3920–3924.Google Scholar
  17. Rekapalli, R., Tiwari, R.K., Sen, M.K. and N. Vedanthi (2015a). Denoising of 3D seismic data using multichannel singular spectrum based time slice and horizon processing. In: R.V. Schneider (Ed.), SEG Technical Program. Expanded Abstracts 2015, 4708–4713, Society of Exploration Geophysicists.Google Scholar
  18. Roberts, A. (2001). Curvature attributes and their application to 3D interpreted horizons. First Break, 19(2), 85–100.  https://doi.org/10.1046/j.0263-5046.2001.00142.x.MathSciNetCrossRefGoogle Scholar
  19. Subrahmanyam, D. and Rao, P.H. (2008). Seismic attributes–A review. In: 7th international conference & exposition on petroleum geophysics, Hyderabad (pp. 398–404).Google Scholar
  20. Taner, M.T., Koehler, F. and Sheriff, R.E. (1979). Complex seismic trace analysis. Geophysics, 44(6), 1041–1063.Google Scholar
  21. Tingdahl, K.M., Bril, A.H. and de Groot, P.F. (2001). Improving seismic chimney detection using directional attributes. Journal of Petroleum Science and Engineering, 29(3–4), 205–211.Google Scholar
  22. van Hoek, T., Gesbert, S. and Pickens, J. (2010). Geometric attributes for seismic stratigraphic interpretation. The Leading Edge, 29(9), 1056–1065.Google Scholar

Copyright information

© Capital Publishing Company 2020

Authors and Affiliations

  • R. K. Tiwari
    • 1
  • R. Rekapalli
    • 1
  1. 1.CSIR-NGRIHyderabadIndia

Personalised recommendations