1887

Abstract

Summary

We consider the problem of predicting the spatial distribution of lithology/fluid classes from observed seismic data. We formulate the problem in a Bayesian setting and argue that the best choice of prior for this problem is a Markov mesh model. To obtain a flexible prior we formulate a general class of Markov mesh models and a corresponding hyper-prior for the model parameters of the Markov mesh model. We discuss three different strategies for how to combine the hierarchical Markov mesh prior, a training image and a likelihood model for the observed seismic data, to obtain predictions of the lithology/fluid classes. We present results from a case study for a seismic section from a North Sea reservoir. In particular the results show larger connectivity in the lithology/fluid classes when using our flexible Markov mesh prior, compared to what one gets with a simpler, manually specified Markov random field prior.

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/content/papers/10.3997/2214-4609.201902237
2019-09-02
2020-06-04
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References

  1. Abend, K., Harley, T. and Kanal, L.
    [1965] Classification of binary random patterns. IEEE Transactions on Information Theory, 11, 538–544.
    [Google Scholar]
  2. Arnesen, P. and Tjelmeland, H.
    [2017] Prior specification of neighbourhood and interaction structure in binary Markov random fields. Statistics and Computing, 27, 737–756.
    [Google Scholar]
  3. Lang, X. and Grana, D.
    [2017] Geostatistical inversion of prestack seismic data for the joint estimation of facies and impedances using stochastic sampling from Gaussian mixture posterior distribution. Geophysics, 82, M55-M65.
    [Google Scholar]
  4. Luo, X. and Tjelmeland, H.
    [2019] Prior specification for binary Markov mesh models. Statistics and Computing, 29, 367–389.
    [Google Scholar]
  5. Strebelle, S.
    [2002] Conditional simulation of complex geolgical structures using multiple-point statistics. Mathematical Geology, 34, 1–21.
    [Google Scholar]
  6. Tjelmeland, H., Luo, X. and Fjeldstad, T.
    [2019] A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image. Geophysical Prospecting. To appear. Available from doi: 10.1111/1365‑2478.12753.
    https://doi.org/10.1111/1365-2478.12753. [Google Scholar]
  7. Zhang, T., Pedersen, S.I., Knudby, C. and McCormick, D.
    [2012] Memory-efficient categorical multipoint statistics algorithms based on compact search trees. Mathematical Geosciences, 44, 863–879.
    [Google Scholar]
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