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Abstract

Summary

A significant enhancement for structural framework analysis of 3-D seismic data is introduced to detect faults & fractures at higher spatial resolution than previously possible through application of a dynamic time-warping (DTW) algorithm. DTW allows for better detection of structural edges (seismic volume discontinuities), because it capitalizes on the detailed comparison of two seismic traces by dynamically finding a path along a cross sample distance matrix to obtain a best match, i.e., the algorithm adjusts cross sample correlation by providing a new, warped time shift function whereby the algorithm is constrained to proceed (1) without creating gaps, (2) with all elements remaining adjacent, and, (3) sample indices monotonously increasing. The new discontinuity analysis algorithm is tested on an unconventional dataset from Eastern Ciscaucasia (Russia). Two different fault/fracture detection algorithms are compared: one of them represents the DTW method, the other involves a widely-used Variance attribute. For benchmarking purposes, both computed attribute cubes serve as an input for subsequent ANT-Tracking to delineate fault and fracture trends. The case study unequivocally demonstrates that better spatial resolution is obtained with application of the DTW method.

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/content/papers/10.3997/2214-4609.201901171
2019-06-03
2019-12-07
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References

  1. Chopra, S. and Marfurt, K.J.
    [2007]. Volumetric curvature attributes for fault/fracture characterization. First Break, 25(7), 35–46.
    [Google Scholar]
  2. Dalley, R.M., Gevers, E.C.A., Stampfli, G.M., Davies, D.J., Gastaldi, C.N., Ruijtenberg, P.A., and Vermeer, G.J.O.
    [1989]. Dip and azimuth displays for 3D seismic interpretation. First Break, 7(3), 86–95.
    [Google Scholar]
  3. Flynn, P.J. and Jain, K.J.
    [1989]. On reliable curvature estimation. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 110–116.
    [Google Scholar]
  4. Juang, B. H.
    [1984]. “On the hidden Markov model and dynamic time warping for speech recognition #x2014; A unified view”. AT&T Bell Laboratories Technical Journal. 63 (7): 1213–1243. doi:10.1002/j.1538‑7305.1984.tb00034.x. ISSN 0748-612X.
    https://doi.org/10.1002/j.1538-7305.1984.tb00034.x [Google Scholar]
  5. Ma, Y.Z., Holditch, S.A.
    [2016]. Unconventional oil and gas resources handbook: evaluation and development. Elsevier Press, 2016. 536 p.
    [Google Scholar]
  6. Marfurt, K.J.
    [2006]. Robust estimates of 3D reflector dip and azimuth. Geophysics, 71(4), July-August2006; P29–P40.
    [Google Scholar]
  7. Priezzhev, I.I., and A.Scollard
    , [2012]. Fault and fracture detection based on seismic surface orthogonal decomposition, 74th Conference & Exhibition, EAGE, Extended Abstracts, W041.
    [Google Scholar]
  8. , [2013]. Fracture detection through seismic cube orthogonal decomposition- SEG 2013, Houston
    [Google Scholar]
  9. Roberts, A.
    [2001]. Curvature attributes and their application to 3D interpreted horizons. First Break, 19(2), 85–100.
    [Google Scholar]
  10. Sakoe, H., Chiba, S.
    [1978]. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing. 26 (1): 43–49. doi:10.1109/tassp.1978.1163055.
    https://doi.org/10.1109/tassp.1978.1163055 [Google Scholar]
  11. VernikL.
    [2016]. Seismic petrophysics in quantitative interpretation. Investigation in Geophysics № 18. SEG Press, 2016. 213 p.
    [Google Scholar]
  12. ZobackM.D.
    [2007]. Reservoir geomechanics. Cambridge Press, 2007. 461 p.
    [Google Scholar]
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