1887

Abstract

Summary

The prediction of the spatial distribution of geological facies based on well log measurements and geophysical data is generally treated as a stochastic sampling or optimization problem. Approaches based on Hidden Markov Models provide satisfactory results in terms of data mismatch but the geological realism of the predicted facies model depends on the prior assumptions related to facies proportions and sequence patterns. We propose here an innovative approach based on Recursive Neural Network and we compare the results to the convolutional Hidden Markov Model approach based on first-order and higher-order Markov chains. An example of application is presented with a quantification of the accuracy of the modeling results and fitness of the probability estimates.

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/content/papers/10.3997/2214-4609.201902275
2019-09-02
2020-03-30
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References

  1. ConnollyP. A. and HughesM. J.
    2016. Stochastic inversion by matching to large numbers of pseudo-wells. Geophysics81 (2), M7-M22.
    [Google Scholar]
  2. de FigueiredoL., GranaD., RoisenbergM. and RodriguesB.
    2019a. Gaussian Mixture McMC method for linear seismic inversion. Geophysics84(3), 1–53.
    [Google Scholar]
  3. 2019b. Multimodal McMC method for non-linear petrophysical seismic inversion. Geophysics, under review.
    [Google Scholar]
  4. DoyenP.
    2007. Seismic reservoir characterization. EAGE.
    [Google Scholar]
  5. FjeldstadT. and GranaD.
    2018. Joint probabilistic petrophysics-seismic inversion based on Gaussian mixture and Markov chain prior models. Geophysics83(1), R31-R42.
    [Google Scholar]
  6. GollerC. and KuchlerA.
    1996. Learning task-dependent distributed representations by backpropagation through structure. In Proceedings of International Conference on Neural Networks1, 347–352.
    [Google Scholar]
  7. GranaD., FjeldstadT. and OmreH.
    2017. Bayesian Gaussian mixture linear inversion for geophysical inverse problems. Mathematical Geosciences49(4), 493–515.
    [Google Scholar]
  8. MozafariA., GomesH., LeãoW., JannyS., GagnéC.
    2019. Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks. Machine Learning, under review.
    [Google Scholar]
  9. GuoC., PleissG., SunY. and WeinbergerK.Q.
    2017. On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning70, 1321–1330.
    [Google Scholar]
  10. SakH., SeniorA. and BeaufaysF.
    2014. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Fifteenth annual conference of the international speech communication association.
    [Google Scholar]
  11. TalaricoE.
    2018. Seismic to facies inversion using convolved hidden Markov model. Master’s thesis, Pontifical Catholic University of Rio de Janeiro.
    [Google Scholar]
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