1887

Abstract

Summary

Thin interbed refer to a layer combination of different lithology less than tuning thickness. Due to the tunable interference of seismic waves between thin layers, the seismic wave field is complex, and it is difficult to effectively characterize the development and transverse distribution of thin interbeds. In this paper, to solve the problem, based on the framework of Bayesian theory, firstly the logging information was converted to the geological statistics prior information in the spectral inversion, the spectrum of seismic data was expanded, then wave impedance inversion data was obtained from the spectrum expanded data using Bayesian wave impedance inversion method so as to achieve effective characterization of thin interbeds. The method was applied to thin interbedded sandstone oil reservoirs developed in Guantao formation in Miaoxi area of Bohai oilfield, a lot of thin sand bodies which can not be described by conventional data with thickness of 5m were successfully characterized by this method, providing a high-precision data support for exploration and development in this region.

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/content/papers/10.3997/2214-4609.201900736
2019-06-03
2024-03-28
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References

  1. RickerN
    . [1953]. Wavelet Contraction,Wavelet Expansion,and the Control of Seismic Resolution. Geophysics, 18(4), 769.
    [Google Scholar]
  2. WidessM B
    . [1973]. How thin is a thin bed?Geophysics, 38(6), 1176–1180.
    [Google Scholar]
  3. HiltermanF J
    . [1975].Amplitudes of SeismicWAVES—AQuickLook.Geophysics, 40(5).
    [Google Scholar]
  4. KoefoedO
    . [1980].The linear properties of thin layers,with an application to synthetic seismograms over coal seams. Geophysics, 45(8):1254–1268.
    [Google Scholar]
  5. KallweitR S
    . [1982].The limits of resolution of zero-phase wavelets. Geophysics, 47(7), 1035.
    [Google Scholar]
  6. NeidellN S
    . [1979].Stratigraphic modeling and interpretation:geophysical principles and techniques. AAPG Department of Education.
    [Google Scholar]
  7. PartykaG A,GridleyJ A,LopezJ A
    . [1999].Interpretational applications of spectral decomposition in reservoir characterization.The Leading Edge, 18(3), 353–360.
    [Google Scholar]
  8. PuryearC I, CastagnaJ P
    . [2008]. Layer-thickness determination and stratigraphic interpretation using spectral inversion: Theory and application. Geophysics, 73(2), R37–R48.
    [Google Scholar]
  9. ChopraS,CastagnaJ P,XuY
    . [2009].Thin-bed reflectivity inversion and some applications.The First Break, 27(5), 17–24.
    [Google Scholar]
  10. YuanS Y,WangS X,YuY C
    . [2009]. Ill-posed analysis for spectral inversion.SEG Technical Program Expanded Abstracts, 28, 2447–2451.
    [Google Scholar]
  11. YuanS Y,WangS X
    . [2013]. Spectral sparse Bayesian learning reflectivity inversion.Geophysical Prospecting, 61(4), 735–746.
    [Google Scholar]
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