1887

Abstract

Summary

A new data assimilation algorithm for joint litho-petro-elastic inversion using nonlinear Zeoppritz reflectivity operators together with sequential filtering and running in place technique is presented. The sequential filtering approach is an attractive choice as integration of rock physics with seismic for multi-parameter estimation can become quickly ill posed. Data assimilation techniques integrate data and model in a manner that track the uncertainties, and can avoid the potential difficulties associated with multi-parameter nonlinear inversion which minimize a single cost function.

The single loop LPE inversion scheme invert seismic data into lithology, elastic and petrophysical parameters through the assimilation process. The lithology and petrophysical parameters are treated as latent variables. The method is open for integration of other measurements and naturally provides ways to address data and prior model consistencies. I demonstrate the application on real data set from offshore Australia.

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/content/papers/10.3997/2214-4609.201801326
2018-06-11
2024-03-19
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References

  1. Bachrach, R.
    , 2006, Joint estimation of porosity and saturation using stochastic rock physics modeling, Geophysics, O53–O63.
    [Google Scholar]
  2. Bachrach, R and Paydayesh, M
    , 2017, Application of sequential and Kalman filters for seismic-geomechanics reservoir monitoring, SEG 88th annual meeting with Expended abstracts.
    [Google Scholar]
  3. Bishop, CM.
    , 2006, Pattern recognition and Machine learning, Springer, New York.
    [Google Scholar]
  4. Gunning, J
    , 2017, EM algorithms and convex-envelope approximations in joint facies/elastic inversion for AVO and FWI, 79th EAGE Conference and Exhibition.
    [Google Scholar]
  5. Kemper, M. and Gunning, J.
    , 2014, Joint impedance and facies inversion-seismic inversion redefinedFirst Break32 (9), 89–95.
    [Google Scholar]
  6. Rimstad, K, Avseth, P. and More, H.
    , 2012, Hierarchical Bayesian lithology/fluid prediction: A North Sea case study, Geophysics, 77B69–B85.
    [Google Scholar]
  7. Kalnay, E. and Yang, S.C.
    , 2010, Accelerating the spin-up of Ensemble Kalman Filtering, Q Journal of royal meteorological society, 1644–1651.
    [Google Scholar]
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