1887
image of Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Abstract

ABSTRACT

Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12502
2017-04-05
2020-08-10
Loading full text...

Full text loading...

References

  1. AzevedoL., NunesR., SoaresA. and NetoG.2013a. Stochastic seismic AVO inversion. 75th EAGE Conference and Exhibition, London, UK, June 2013.
    [Google Scholar]
  2. AzevedoL., NunesR., CorreiaP., SoaresA., NetoG. and GuerreiroL.2013b. Stochastic direct facies seismic AVO inversion. In: SEG Technical Program, Expanded Abstracts, pp. 2352–2356.
  3. AzevedoL., NunesR., CorreiaP., SoaresA., GuerreiroL. and NetoG.2014. Multidimensional scaling for the evaluation of a geostatistical seismic elastic inversion methodology. Geophysics79, M1–M10.
    [Google Scholar]
  4. BortoliL.J., HaasA.A. and JournelA.G.1992. Constraining stochastic images to seismic data. Geostatistics Tróia1, 325–338.
    [Google Scholar]
  5. BoschM., MukerjiT. and GonzálezE.F.2010. Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review. Geophysics75(5), 75A165.
    [Google Scholar]
  6. BoschettiF., DentithM.C. and ListR.D.1996. Inversion of seismic refraction data using genetic algorithms. Geophysics61(6), 1715–1727.
    [Google Scholar]
  7. BulandA. and OmreH.2003. Bayesian linearized AVO inversion. Geophysics68(1), 185–198.
    [Google Scholar]
  8. BulandA. and El OuairY.2006. Bayesian time‐lapse inversion. Geophysics71(3), R43–R48.
    [Google Scholar]
  9. CaersJ.2011. Modeling Uncertainty in Earth Sciences. UK: Wiley‐Blackwell.
    [Google Scholar]
  10. CaetanoH.2009. Integration of seismic information in reservoir models: global stochastic inversion. PhD thesis, University of Lisbon, Portugal.
    [Google Scholar]
  11. DeutschC.V. and JournelA.G.1998. GSLIB: Geostatistical Software Library and User's Guide. NY: Oxford University Press, 340.
    [Google Scholar]
  12. DoyenP.M.2007. Seismic Reservoir Characterization. EAGE.
    [Google Scholar]
  13. GranaD. and Della RossaE.2010. Probabilistic petrophysical‐properties estimation integrating statistical rock physics with seismic inversion. Geophysics75, O21–O37.
    [Google Scholar]
  14. HaasA. and DubruleO.1994. Geostatistical inversion—A sequential method for stochastic reservoir modeling constrained by seismic data. First Break12, 561–569.
    [Google Scholar]
  15. HortaA. and SoaresA.2010. Direct sequential co‐simulation with joint probability distributions. Mathematical Geosciences42(3), 269–292.
    [Google Scholar]
  16. MaX.2002. Simultaneous inversion of prestack seismic data for rock properties using simulated annealing. Geophysics67, 1877–1885.
    [Google Scholar]
  17. MallickS.1995. Model‐based inversion of amplitude‐variations‐with‐offset data using a genetic algorithm. Geophysics60, 939–954.
    [Google Scholar]
  18. MallickS.1999. Some practical aspects of prestack waveform inversion using a genetic algorithm: an example from the east Texas Woodbine gas sand. Geophysics64, 326–336.
    [Google Scholar]
  19. NunesR., SoaresA., SchwederskyG., DillonL., GuerreiroL., CaetanoH.et al. 2012. Geostatistical inversion of prestack seismic data. Ninth International Geostatistics Congress, Oslo, Norway, 1–8.
    [Google Scholar]
  20. RimstadK. and OmreH.2009. Bayesian lithology/fluid inversion constrained by rock physics depth trends and a Markov random field. 71st EAGE Conference and Exhibition, Amsterdam, The Netherlands, June 2009.
    [Google Scholar]
  21. RussellB.1988. Introduction to Seismic Inversion Methods. Society of Exploration Geophysicists.
    [Google Scholar]
  22. SamsM., MillarI., SatriawanW., SaussusD. and BhattacharyyaS.2011. Integration of geology and geophysics through geostatistical inversion: a case study. First Break29(8), 47–56.
    [Google Scholar]
  23. SenM. and StoffaP.1991. Nonlinear one‐dimensional seismic waveform inversion using simulated annealing. Geophysics56, 1624–1638.
    [Google Scholar]
  24. SoaresA.1998. Sequential indicator simulation with correction for local probabilities. Mathematical Geology30, 761–765.
    [Google Scholar]
  25. SoaresA.2001. Direct sequential simulation and cosimulation. Mathematical Geology33, 911–926.
    [Google Scholar]
  26. SoaresA., DietJ.D. and GuerreiroL.2007. Stochastic inversion with a global perturbation method. EAGE Petroleum Geostatistics, Cascais, Portugal, September 2007.
    [Google Scholar]
  27. StrebelleS.2002. Conditional simulation of complex geological structures using multiple‐point statistics. Mathematical Geology34(1), 1–21.
    [Google Scholar]
  28. TarantolaA.2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics.
    [Google Scholar]
  29. TompkinsM., Fernández MartínezJ., AlumbaughD. and MukerjiT.2011. Scalable uncertainty estimation for nonlinear inverse problems using parameter reduction, constraint mapping, and geometric sampling: marine controlled‐source electromagnetic examples. Geophysics76, F263–F281.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12502
Loading
/content/journals/10.1111/1365-2478.12502
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error