1887

Abstract

Summary

In geostatistical seismic inversion methods the model perturbation and update is performed by stochastic sequential simulation and co-simulation algorithms in a regular Cartesian grid and using a global variogram model to describe the spatial continuity pattern of the subsurface petro-elastic property. These approaches do not capture heterogeneous small-scale features being hard to be reproduced when dealing to highly non-stationary geological environments. This work integrates local anisotropy steering volumes to describe local anisotropies within iterative geostatistical seismic inversion methods. The incorporation of local structural and spatial information allow to obtain more consistent spatial distribution of rock properties while avoids any transformation of inversion grid during simulation process by traditional geostatistical simulation techniques. The proposed methodology of this work was successfully applied to synthetic and real application examples.

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/content/papers/10.3997/2214-4609.201902244
2019-09-02
2024-04-19
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References

  1. Azevedo, L. and Soares, A. [2017] Geostatistical methods for reservoir geophysics. Springer.
    [Google Scholar]
  2. Azevedo, L., Nunes, R., Soares, A., Neto, G.S. and Martins, T.S. [2018] Geostatistical seismic Amplitude-versus-angle inversion, Geophysical Prospecting, 66(S1), 116–131.
    [Google Scholar]
  3. Bosch, M., Mukerji, T. and Gonzalez, E. F. [2010] Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. In Geophysics, 75(5): A165-A176, doi: 10.1190/1.3478209.
    https://doi.org/10.1190/1.3478209. [Google Scholar]
  4. Bortoli, L. J., Alabert, F., Haas, A., and Journel, A. G. [1992] Constraining stochastic images to seismic data. Geostatistics Troia, 1, 325-337, doi: 10.1007/978‑94‑011‑1739‑527.
    https://doi.org/10.1007/978-94-011-1739-5 27. [Google Scholar]
  5. Caeiro, M., Demyanov, V. and Soares, A. [2015] Optimized History Matching with Direct Sequential Image Transforming for Non-Stationary Reservoirs. Mathematical Geosciences, doi: 10.1007/s11004‑015‑9591‑0.
    https://doi.org/10.1007/s11004-015-9591-0. [Google Scholar]
  6. Chopra, S. and Marfurt, K.J. [2005] Seismic attributes - A historical perspective. Geophysics, Vol.70; No.5, 3SO-28SO.
    [Google Scholar]
  7. Doyen, P. [2007] Seismic reservoir characterization: an earth modelling perspective. Constraints. EAGE.
    [Google Scholar]
  8. Haas, A., and Dubrule, O. [1994] Geostatistical inversion - A sequential method of stochastic reservoir modeling constrained by seismic data. First Break, 12, 561-569.
    [Google Scholar]
  9. Horta, A., Caeiro, M. H., Nunes, R., and Soares, A. [2010] Simulation of Continuous Variables at Meander Structures: Application to Contaminated Sediments of a Lagoon, geoENV VII - Geostatistics for Environmental Applications, 161-172. doi: 10.1007/978‑90‑481‑2322‑3_15.
    https://doi.org/10.1007/978-90-481-2322-3_15 [Google Scholar]
  10. Lillah, M. and Boisvert, J. B. [2015] Inference of Locally Varying Anisotropy Fields from Diverse Data Sources. Computers & Geosciences, 82. doi: 10.1016/j.cageo.2015.05.015.
    https://doi.org/10.1016/j.cageo.2015.05.015. [Google Scholar]
  11. Martin, R., Machuca-Mory, D. F., Leuangthong, O. and Boisvert, J. B. [2018] Non-stationary Geostatistical Modeling: A Case Study Comparing LVA Estimation Frameworks. Natural Resources Research. doi: 10.1007/s11053‑018‑9384‑5.
    https://doi.org/10.1007/s11053-018-9384-5. [Google Scholar]
  12. Soares, A. [2001] Direct sequential simulation and co-simulation. Mathematical Geology, 33, 911-926, doi: 10.1023/A:1012246006212.
    https://doi.org/10.1023/A: 1012246006212. [Google Scholar]
  13. Soares, A., Diet, J.D. and Guerreiro, L. [2007] Stochastic Inversion with a Global Perturbation Method. Petroleum Geostatistics, EAGE, Cascais, Portugal (September 2007): 10–14.
    [Google Scholar]
  14. Tarantola, A. [2005] Inverse Problem Theory. SIAM.
    [Google Scholar]
  15. Vargas-Guzman, J. A., and Vargas-Murillo, B. [2017] Functional Decomposition Kriging for Embedding Stochastic Anisotropy Simulations. Geostatistics Valencia 2016. doi: 10.1007/978‑3‑319‑46819‑82.
    https://doi.org/10.1007/978-3-319-46819-8 2. [Google Scholar]
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