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Abstract

Summary

We present a new iterative geostatistical seismic inversion algorithm that allows retrieving: density; P-wave velocity; S-wave velocity and facies models. This novel procedure is based on two key main ideas: stochastic sequential simulation and co-simulation as the perturbation technique of the model parameter space; and a genetic algorithm that act as a global optimizer to converge the iterative procedure towards an objective function, the mismatch between recorded and synthetic pre-stack seismic data. At the end of each iteration, the triplet of elastic traces that jointly produce synthetic gathers with the highest correlation coefficient are the basis for generating the new elastic models of the next iteration. The iterative procedure finishes when the global correlation between recorded and inverted seismic data is above a certain threshold. All the elastic models simulated during the iterative procedure honor the marginal and joint distributions of P-wave velocity, S-wave velocity and density as estimated from the available well-log data. We successfully tested this new algorithm on a real pre-stack seismic dataset acquired over a deep-water turbidite oil reservoir. The results show a good convergence between real and synthetic seismic and the retrieved high resolution petro-elastic models agree with previous studies performed with commercial inversion algorithms.

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/content/papers/10.3997/2214-4609.201413177
2015-06-01
2024-04-28

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References

  1. Avseth, P., Mukerji, T., & Mavko, G.
    (2005). Quantitative seismic interpretation. Cambrige University Press.
     [Google Scholar]
  2. Azevedo, L., Nunes, R., Soares, A., & Neto, G. S.
    (2013)a. Stochastic Seismic AVO Inversion. 75th EAGE Conference & Exhibition, (June2013), 10–13.
     [Google Scholar]
  3. Azevedo, L., Nunes, R., Soares, A., Neto, G. S., & Guerreiro, L.
    (2013)b. Stochastic direct facies seismic AVO inversion. In SEG Annual Meeting.
     [Google Scholar]
  4. Bortoli, L., Alabert, F., Haas, A., & Journel, A.
    (1992). Constraining stochastic images to seismic data. In Geostatistics Troia (Vol. Vol 1., pp. 325–338).
     [Google Scholar]
  5. Bosch, M., Mukerji, T., & González, E. F.
    (2010). Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75(5), 75A165. doi:10.1190/1.3478209
     https://doi.org/10.1190/1.3478209 [Google Scholar]
  6. Gonzalez, E. F., & Mukerji, T.
    (2007). Seismic Inversion Using Multi-Point Geostatistics and Rock Physics. Petroleum Geostatistics, EAGE, Cascais, Portugal, (September2007), 10–14.
     [Google Scholar]
  7. Grana, D., & Della Rossa, E.
    (2010). Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics, 75(3), O21–O37. doi:10.1190/1.3386676
     https://doi.org/10.1190/1.3386676 [Google Scholar]
  8. Haas, A., & 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., & Soares, A.
    (2010). Direct Sequential Co-simulation with Joint Probability Distributions. Mathematical Geosciences, 42(3), 269–292. doi:10.1007/s11004‑010‑9265‑x
     https://doi.org/10.1007/s11004-010-9265-x [Google Scholar]
  10. Nunes, R., Soares, A., Schwedersky, G., Dillon, L., Guerreiro, L., Caetano, H., … Leon, F.
    (2012). Geostatistical Inversion of Prestack Seismic Data. In Ninth International Geostatistics Congress (pp. 1–8). Oslo, Norway
     [Google Scholar]
  11. Sen, M. K., & Stoffa, P. L.
    (1991). Nonlinear one dimensional seismic waveform inversion using simulated annealing. Geophysics, 56(10), 1624–1638. doi:10.1190/1.1442973
     https://doi.org/10.1190/1.1442973 [Google Scholar]
  12. Shuey, R. T.
    (1985). A simplification of the Zoeppritz equations. Geophysics, 50(4), 609–614. doi:10.1190/1.1441936
     https://doi.org/10.1190/1.1441936 [Google Scholar]
  13. Soares, A.
    (2001). Direct sequential simulation and cosimulation. Mathematical Geology, 33(8), 911–926.
     [Google Scholar]
  14. Soares, A., Diet, J., & Guerreiro, L.
    (2007). Stochastic Inversion with a Global Perturbation Method. Petroleum Geostatistics, EAGE, Cascais, Portugal, (September2007), 10–14
     [Google Scholar]
  15. Tarantola, A.
    (2005). Inverse Problem Theory (p. 342). SIAM.
     [Google Scholar]
  16. Tompkins, M. J., Fernández Martínez, J. L., Alumbaugh, D. L., & Mukerji, T.
    (2011). Scalable uncertainty estimation for nonlinear inverse problems using parameter reduction, constraint mapping, and geometric sampling: Marine controlled-source. Geophysics, 76(4), 263–281.
     [Google Scholar]
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Geostatistical Seismic AVO Inversion Directly for Facies - Real Case Application
Conference Proceedings 2015, 1 (2015); https://doi.org/10.3997/2214-4609.201413177
/content/papers/10.3997/2214-4609.201413177
/content/papers/10.3997/2214-4609.201413177
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