1887
Volume 60, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We invert prestack seismic amplitude data to find rock properties of a vertical profile of the earth. In particular we focus on lithology, porosity and fluid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full profile such as the probability that the vertical profile contains hydrocarbons and volume distributions of hydrocarbons. In a standard point wise approach, these quantities can not be assessed. We formulate the problem in a Bayesian framework, and model the vertical dependency using spatial statistics. The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reflectivity to the seismic. A Markov chain Monte Carlo (MCMC) algorithm is used to generate multiple realizations that honours both the seismic data and the prior beliefs and respects the additional constraints imposed by the vertical dependencies. Convergence plots are used to provide quality check of the algorithm and to compare it with a similar method. The implementation has been tested on three different data sets offshore Norway, among these one profile has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantification within three hours. The inversion result is consistent with the observed well data. In the case example we show that the seismic amplitudes make a significant impact on the inversion result even if the data have a moderate well tie, and that this is due to the vertical dependency imposed on the lithology fluid classes in our model. The vertical correlation in elastic parameters mainly influences the upside potential of the volume distribution.

The approach is best suited to evaluate a few selected vertical profiles since the MCMC algorithm is computer demanding.

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2011-10-25
2024-04-23
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References

  1. AkiK. and RichardsP. G.1980. Quantitative seismology: Theory and methods . W. H. Freeman and Company.
    [Google Scholar]
  2. AvsethP., MukerjiT. and MavkoG.2005. Quantitative seismic interpretation ‐applying rock physics tools to reduce interpretation risk . Cambridge University Press.
    [Google Scholar]
  3. BoschM., CaraL., RodriguesJ., AlonsoNavarro A. and DíazM.2007. A Monte Carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes. Geophysics72(6), O29–O39.
    [Google Scholar]
  4. BoschM., CarvajalC., RodriguesJ., TorresA., AldanaM. and SierraJ.2009. Petrophysical seismic inversion conditioned to well‐log data: Methods and application to a gas reservoir. Geophysics74(2), O1–O15.
    [Google Scholar]
  5. BulandA. and OmreH.2003. Bayesian linearized AVO inversion. Geophysics68, 185–198.
    [Google Scholar]
  6. BulandA., KolbjørnsenO., HaugeR., SkjævelandØ. and DuffautK.2008. Bayesian lithology and fluid prediction from seismic prestack data. Geophysics73(3), C13–C21.
    [Google Scholar]
  7. CastagnaJ. P., BatzleM. L. and KanT. K.1993. Rock physics ‐ the link between rock properties and avo response, in CastagnaJ. P. , and BackusM. M., Eds., Offset‐dependent reflectivity ‐ Theory and practice of AVO analysis: Soc. Expl. Geophys.
    [Google Scholar]
  8. ContrerasA., Torres‐VerdinC., ChestersW., KvienK. and GlobeM.2005. Joint stochastic inversion of petrophysical logs and 3D pre‐stack seismic data to assess the spatial continuity of fluid units away from wells: Application to a Gulf‐of‐Mexico deepwater hydrocarbon reservoir. SPWLA 46th Annual Logging Symposium46, 1–15.
    [Google Scholar]
  9. DuijndamA. J. W.1988a. Bayesian estimation in seismic inversion. Part I: Principles. Geophysical Prospecting36, 878–898.
    [Google Scholar]
  10. DuijndamA. J. W.1988b. Bayesian estimation in seismic inversion. Part II: Uncertainty analysis. Geophysical Prospecting36, 899–918.
    [Google Scholar]
  11. GunningJ. and GlinskyM. E.2004. Delivery: an open‐source model‐based bayesian seismic inversion program. Computers & Geosciences30(6), 619–636.
    [Google Scholar]
  12. HaasA. and DubruleO.1994. Geostatistical inversion‐a sequential method of stochastic reservoir modelling constrained by seismie data. First Break12(11), 561–569.
    [Google Scholar]
  13. HammerH. and TjelmelandH.2011. Approximate forward‐backward algorithm for a switching linear Gaussian model – with application to seismic inversion. Journal Computational Statistics & Data Analysis55(1), 154–167.
    [Google Scholar]
  14. KjønsbergH., HaugeR., KolbjørnsenO. and BulandA.2010. Bayesian monte carlo method for seismic predrill prospect assessment. Geophysics75(2), O9–O19.
    [Google Scholar]
  15. KrumbeinY. and DaceyM.1969. Markov chains and embedded markov chains in geology. Mathematical Geology1(1), 79–96.
    [Google Scholar]
  16. LarsenA. L., UlvmoenM., OmreH. and BulandA.2006. Bayesian lithology/fluid prediction and simulation on the basis of a Markov‐chain prior model. Geophysics71(5), R69–R78.
    [Google Scholar]
  17. LiuJ. S.2001. Monte carlo strategies in scientific computing . Springer, Berlin .
    [Google Scholar]
  18. MavkoG. and MukerjiT.1998. A rock physics strategy for quantifying uncertainty in common hydrocarbon indicators. Geophysics63(1), 1997–2008.
    [Google Scholar]
  19. MerlettiG. and Torres‐VerdinC.2006. Accurate detection and spatial delineation of thin‐sand sedimentary sequences via joint stochastic inversion of well logs and 3D pre‐stack seismic amplitude data: SPE, no. SPE 102444, 1–17.
    [Google Scholar]
  20. ScalesJ. and TenorioL.2001. Prior information and uncertainty in inverse problems. Geophysics66(2), 389–397.
    [Google Scholar]
  21. ScottA. L.2002. Bayesian methods for hidden Markov models: Recursive compution in the 21st century. Journal of the American Statistical Association97, 337–351.
    [Google Scholar]
  22. Torres‐VerdinC., VictoriaM., MerlettiG. and PendrelJ.1999. Trace‐based and geostatistical inversion of 3‐D seismic data for thin‐sand delineation: An application in San Jorge Basin, Argentina: The leading edge, pages 1070–1077.
    [Google Scholar]
  23. UlrychJ., SacchiM. and WoodburyA.2001. A bayes tour of inversion: A tutorial. Geophysics66(1), 55–69.
    [Google Scholar]
  24. UlvmoenM. and HammerH.2009. Bayesian lithology/fluid inversion – comparison of two algorithms: Computational Geosciences (accepted).
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): inversion; noise; numerical study; rock physics; seismics

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