Skip to main content
SpringerLink
Log in

Maximum likelihood classification for facies inference from reservoir attributes

Application to seismic characterization and reservoir model reconstruction

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

A cluster analysis methodology is developed to recover facies realizations from observed reservoir attributes. A maximum likelihood estimator allows us for identifying the most probable underlying facies using a spatial clustering algorithm. In seismic characterization, this algorithm can yield relevant geological models for subsequent history-matching studies. In history-matching procedures, it provides informative facies maps as well as starting points for further studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gringarten, E., Journel, A.G., Gundeso, R., Yao, T.: Stochastic modelling of a fluvial reservoir: a comparative review of algorithms. J. Pet. Sci. Eng. 21, 95–121 (1998)

    Article  Google Scholar 

  2. Allard, D., Guillot, G.: Clustering geostatistical data. In: Kleingeld, W.J., Krige, D.G. (eds.) Geostatistics 2000 Cape Town vol. 1, pp. 49–63 (1999)

  3. Bornard, R., Allo, F., Col´ou, T., Freudenreich, Y., Caldwell, D.H., Hamman, J.G.: Petrophysical seismic inversion to determine more accurate and precise reservoir properties. In: SPE 94144, SPE Europec/EAGE Conference and Exhibition, Madrid, Spain, 13–16 June 2005

  4. Celeux, G., Govaert, G.: A classification em algorithm for clustering and two stochastic versions. Technical Report 1364, INRIA (1991)

  5. Christie, M.A., Blunt, M.J.: Tenth SPE comparative solution project: a comparison of upscaling techniques. In: SPE 66599, presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 11–14 Feb 2001

  6. Da Veiga, S., Le Ravalec, M.: Rebuilding existing geological models. In: Proceedings of the 72nd EAGE Conference & Exhibition incorporating SPE EUROPEC 2010, SPE 130976, Barcelona, Spain, 14–17 June 2010

  7. de Brito, D.U., de Moraes, R.J., Emerick, A.A.: The Marlim field: incorporating time-lapse seismic in the assisted history matching. In: SPE 137763, SPE Latin American & Caribbean Petroleum Engineering Conference, Lima, Peru (2010)

  8. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc., Ser. B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  9. Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman and Hall/CRC, London (1994)

    Google Scholar 

  10. Fernandez, P.J., Ferrari, P.A., Grynberg, S.: Perfectly random sampling of truncated multinormal distributions. Adv. Appl. Probab. 39, 973–990 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Floris, F.J.T., Bush, M.D., Cuypers, M., Roggero, F., Syversveen, A.-R.: Methods for quantifying the uncertainty of production forecasts: a comparative study. Pet. Geosci. 7, 87–S96 (2001). doi:10.1144/petgeo.7.S.S87

    Article  Google Scholar 

  12. Galli, A., Beucher, H., Le Loc’h, G., Doligez, B.: The pros and the cons of the truncated Gaussian method. In: Armstrong, M., Dowd, P.A. (eds.) Geostatistical Simulations, pp. 217–233. Kluwer Academic, Dordrecht (1994)

    Google Scholar 

  13. Goovaerts, P.: Geostatistics for Natural Resources Evaluation. Oxford University Press, New York (1997)

    Google Scholar 

  14. Han, M., Zhao, Y., Li, G., Reynolds, A.C.: Application of EM algorithms for seismic facies classification. Comput. Geosci (2010). doi:10.1007/s10596-010-9212-4

    Google Scholar 

  15. Hu, L.Y.: Gradual deformation and iterative calibration of gaussian-related stochastic models. Math. Geol. 32(1), 87–108 (2000)

    Article  Google Scholar 

  16. Journel, A.: Geostatistics for reservoir characterization. In: SPE ATCE, New Orleans, USA, SPE 20750, 23–26 Sept 1990

  17. Lahiri, S.N.: Resampling Methods for Dependent Data. Springer, New York (2003)

    MATH  Google Scholar 

  18. Le, G., Loc’h, Galli, A.: Truncated plurigaussian method: theoretical and practical point of view. In: Baafi, E.Y., Schofield, N.A. (eds) Geostatistics Wollongong ’96, vol. 1, pp. 211–222. Kluwer Academic, Dordrecht (1996)

    Google Scholar 

  19. Le Ravalec-Dupin, M., Hu, L.-Y., Roggero, F.: Rebuilding and recalibrating an existing reservoir model to dynamic data. Earth Sci Front 15(1), 176–186 (2008)

    Article  Google Scholar 

  20. Le Ravalec-Dupin, M., Noetinger, B., Hu, L.-Y., Blanc, G.: Conditioning to dynamic data: an improved zonation approach. Pet. Geosci. 7, S9–S16 (2001)

    Article  Google Scholar 

  21. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  22. Nelsen, R.B.: An Introduction to Copulas. Lecture Notes in Statistics, vol. 139. Springer, New York (1999)

    MATH  Google Scholar 

  23. Nordman, D.J., Caragea, P.C.: Point and interval estimation of variogram models using spatial empirical likelihood. J. Am. Stat. Assoc. 103(481), 350–361 (2008)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Reservoir Engineering Department, IFP Energies nouvelles, 1–4 avenue du Bois-Preau, 92852, Rueil-Malmaison Cedex, France

    Sebastien Da Veiga & Mickaele Le Ravalec

Authors
  1. Sebastien Da Veiga
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mickaele Le Ravalec
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastien Da Veiga.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Da Veiga, S., Le Ravalec, M. Maximum likelihood classification for facies inference from reservoir attributes. Comput Geosci 16, 709–722 (2012). https://doi.org/10.1007/s10596-012-9283-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-012-9283-5

Keywords

Search

Navigation