1887

Abstract

Summary

“Geophysical parameters’ distribution is an important information to constrain seismic inversion and interpretation. In this work, we present a theory of Collocated CoKriging (CCK) to estimate the distributions of subsurface parameters in an area with limited number of well logs. Due to the complexity of sedimentary facies, it will be a challenging mission to depict a distinct boundary of the interested parameter. Therefore, some modelling methods divide the study area into pieces to distinguish different sedimentary facies.

The modified CCK and a novel modelling strategy are proposed to estimate the distribution of subsurface parameters without dividing the study area by introducing an additional dimension of variables into CCK.

We demonstrate the application of an efficient strategy in estimating P-wave velocity and density with 5 well logs in the China South Sea. The proposed method overcomes the problem of introducing sedimentary facies into the calculation and the error points appear in the velocity model obtained by CCK. The velocity and density models show higher correlation with sedimentary facies. Therefore, the proposed method can be used as a pre-treatment tool to help researchers do a good job of seismic inversion and interpretation.”

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/content/papers/10.3997/2214-4609.201801455
2018-06-11
2024-04-20
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References

  1. Azevedo, L., Nunes, R., Soares, A., Neto, G. S. and Martins, T. S.
    [2017] Geostatistical seismic AVA inversion. Geophysical Prospecting. Accepted Author Manuscript.
    [Google Scholar]
  2. Bosch, M., Carvajal, C., Rodrigues, J., Torres, A., Aldana, M. and Sierra, J.
    [2009] Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir. Geophysics, 74(2): 01–015.
    [Google Scholar]
  3. Bourges, M., Mari, J.-L. and Jeannée, N.
    [2012] A practical review of geostatistical processing applied to geophysical data: methods and applications. Geophysical Prospecting, 60: 400–412.
    [Google Scholar]
  4. Huston, D. C., Huston, H. H. and Johnson, E.
    [2004]. Geostatistical integration of velocity cube and log data to constrain 3D gravity modeling, deepwater Gulf of Mexico. The Leading Edge, 23(9): 842–846.
    [Google Scholar]
  5. Shamsipour, P., Marcotte, D., Chouteau, M. and Keating, P.
    [2010] 3D stochastic inversion of gravity data using cokriging and cosimulation. Geophysics, 75(1): I1–I10.
    [Google Scholar]
  6. Vesnaver, A., Bridle, R., Henry, B., Ley, R., Rowe, R. and Wyllie, A.
    [2006] Geostatistical integration of near-surface geophysical data. Geophysical Prospecting, 54: 763–777.
    [Google Scholar]
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