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Abstract

Summary

We focus on Bayesian inversion, exemplified by that of predicting reservoir variables conditional on seismic amplitude data. Our interest is to study local approaches for obtaining approximate samples from the posterior distribution. The posterior is defined via prior samples and a likelihood model for the geophysical observations. This likelihood model

involves a non-linear expectation term, commonly termed the forward model. In this paper we conduct a comparison of a local rejection sampler and a local ensemble transform Kalman filter for this challenge. Seismic AVO data from the top-reservoir formation of the Alvheim field in the North Sea are used in the study.

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/content/papers/10.3997/2214-4609.202335060
2023-11-27
2026-04-13

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References

  1. Avseth, P., Mukerji, T., & Mavko, G. (2010). Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk. Cambridge university press.
     [Google Scholar]
  2. Eidsvik, J., Avseth, P., Omre, H., Mukerji, T., & Mavko, G. (2004). Stochastic reservoir characterization using prestack seismic data.Geophysics, 69(4), 978–993.
     [Google Scholar]
  3. Gineste, M., Eidsvik, J., & Zheng, Y. (2020). Ensemble-based seismic inversion for a stratified medium.Geophysics, 85(1), R29–R39.
     [Google Scholar]
  4. Grana, D., Azevedo, L., De Figueiredo, L., Connolly, P., & Mukerji, T. (2022). Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples.Geophysics, 87(5), M199–M216.
     [Google Scholar]
  5. Hansen, T. M. (2021). Efficient probabilistic inversion using the rejection sampler—exemplified on airborne em data.Geophysical Journal International, 224(1), 543–557.
     [Google Scholar]
  6. Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform kalman filter.Physica D: Nonlinear Phenomena, 230(1–2), 112–126.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202335060
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Comparison of Sampling Methods with Local Conditioning to Seismic Data
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335060
/content/papers/10.3997/2214-4609.202335060
/content/papers/10.3997/2214-4609.202335060
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