Geostatistical Modeling of Facies

  • Y. Z. Ma


Because facies are nominal variables, their modeling methods are different from the modeling methods for continuous variables. Kriging and stochastic simulation methods presented in Chaps.  16 and  17 cannot be directly used for construction of a facies model; they can be modified for facies modeling, or totally different methods are used. Although facies are often modeled before modeling petrophysical variables, modeling methods for continuous variables were presented in the earlier chapters because it is easier to understand facies modeling methods after understanding kriging and stochastic simulation for continuous variables. This chapter presents several facies modeling methods, including indicator kriging, sequential indicator simulation and its variations, object-based modeling, truncated Gaussian and plurigaussian simulations, and simulation using multipoint statistics.


  1. Armstrong, M., Galli, A. G., Le Loc’h, G., Geffroy, F., & Eschard, R. (2003). Plurigaussian simulations in geosciences. Berlin: Springer.CrossRefGoogle Scholar
  2. Cao, R., Ma, Y. Z., & Gomez, E. (2014). Geostatistical applications in petroleum reservoir modeling. SAIMM, 114.Google Scholar
  3. Clement, R., et al. (1990). A computer program for evaluation of fluvial reservoirs. In North Sea oil and gas reservoirs-II. Dordrecht: Springer.Google Scholar
  4. Daly, C., & Caers, J. (2010). Multi-point geostatistics – An introductory overview. First Break, 28, 39–47.CrossRefGoogle Scholar
  5. Datta, K., Yaser, M., Gomez, E., Ma, Z., Filak, J. M., Al-Nasheet, A., & Ortegon, L. D. (2019). Capturing multiscale heterogeneity in paralic reservoir characterization: A study in Greater Burgan Field, Kuwait. AAPG Memoir 118, Tulsa, OK, USA.Google Scholar
  6. Deutsch, C. V., & Journel, A. G. (1992). Geostatistical software library and user’s guide (340p.). Oxford: Oxford University PressGoogle Scholar
  7. Deveugle, P. E. K., et al. (2014). A comparative study of reservoir modeling techniques and their impact on predicted performance of fluvial-dominated deltaic reservoirs. AAPG Bulletin, 98(4), 729–763.CrossRefGoogle Scholar
  8. Doyen, P. M., Psaila, D. E., & Strandenes, S. (1994). Bayesian sequential indicator simulation of channel sands from 3-D seismic data in the Oseberg field, Norwegian North Sea. SPE-28382-MS, SPE ATCE, New Orleans.Google Scholar
  9. Dubrule, O. (2017). Indicator variogram models: Do we have much choice? Mathematical Geosciences, 49, 441–465. Scholar
  10. Falivene, O. P., Arbues, A., Gardiner, G., Pickup, J. A. M., & Cabrera, L. (2006). Best practice stochastic facies modeling from a channel-fill turbidite sandstone analog. AAPG Bulletin, 90(7), 1003–1029.CrossRefGoogle Scholar
  11. Fletcher, S. (2017). Data assimilation for the geosciences: From theory to application. Amsterdam: Elsevier.CrossRefGoogle Scholar
  12. Guardiano, F., & Srivastava, R. (1993). Multivariate geostatistics: Beyond bivariate moments. In A. Soares (Ed.), Geostatistics Troia 1992 (pp. 133–144). Dordrecht: Kluwer.Google Scholar
  13. Holden, L., et al. (1997). Modeling of fluvial reservoirs with object models. AAPG Computer Applications in Geology, 3.Google Scholar
  14. Hu, L. Y., & Chugunov, T. (2008). Multiple point geostatistics for modelling subsurface heterogeneity: A comprehensive review. Water Resources Research, 44, W11413.Google Scholar
  15. Lantuejoul, C. (2002). Geostatistical simulation: Models and algorithms. Berlin: Springer.CrossRefGoogle Scholar
  16. Liu, Y., Harding, A., Abriel, W., & Strebelle, S. (2004). Multiple-point simulation integrating wells, three-dimensional seismic data, and geology. AAPG Bulletin, 88, 905–921.CrossRefGoogle Scholar
  17. Ma, Y. Z. (2009). Propensity and probability in depositional facies analysis and modeling. Mathematical Geosciences, 41, 737–760.CrossRefGoogle Scholar
  18. Ma, Y. Z. (2010). Error types in reservoir characterization and management. Journal of Petroleum Science and Engineering, 72(3–4), 290–301. Scholar
  19. Ma, Y. Z., Seto, A., & Gomez, E. (2008). Frequentist meets spatialist: A marriage made in reservoir characterization and modeling. SPE-115836-MS, SPE ATCE, Denver, CO, USA.Google Scholar
  20. Ma, Y. Z., Seto, A., & Gomez, E. (2009). Depositional facies analysis and modeling of Judy Creek reef complex of the late Devonian swan hills, Alberta, Canada. AAPG Bulletin, 93(9), 1235–1256. Scholar
  21. Ma, Y. Z., Seto, A., & Gomez, E. (2011). Coupling spatial and frequency uncertainty analysis in reservoir modeling: Example of Judy Creek reef complex in San Hills, Albert Canada. AAPG Memoir, 96, 159–173.Google Scholar
  22. MacDonald, A. C., Berg, J. I., & Holden, L. (1995). Constraining a stochastic model of channel geometries using seismic data. EAGE 57th Conference and Technical Exhibition.Google Scholar
  23. Macé, L., & Márquez, D. (2017). Modeling of a complex depositional system using MPS method conditioned to hard data and secondary soft probabilistic information. Society of Petroleum Engineers.
  24. Mariethoz, G., & Caers, J. (2015). Multiple-point geostatistics. Chichester: Wiley Blackwell.Google Scholar
  25. Massonnat, G. J. (1999). Breaking of a paradigm: Geology can provide 3D complex probability fields for stochastic facies modeling. SPE-56652-MS, ATCE, Houston, TX, USA.Google Scholar
  26. Matheron, G. (1973). The intrinsic random functions and their applications. Advances in Applied Probbility, 5, 439–468.MathSciNetCrossRefGoogle Scholar
  27. Matheron, G. (1989). Estimating and choosing – An essay on probability in practice. Berlin: Springer.CrossRefGoogle Scholar
  28. Matheron, G., et al. (1987). Conditional simulation of the geometry of fluvio-deltaic reservoirs. SPE-16753-MS. SPE ATCE, Dallas.Google Scholar
  29. Mustapha, H., & Dimitrakopoulos, R. (2010). Higher-order stochastic simulation of complex spatially distributed natural phenomena. Mathematical Geoscience, 42, 457–485.CrossRefGoogle Scholar
  30. Papoulis, A. (1965). Probability, random variables and stochastic processes (583p.). New York: McGraw-Hill.Google Scholar
  31. Pranter, M. J., & Sommer, N. K. (2011). Static connectivity of fluvial sandstones in a lower coastal-plain setting: An example from the upper cretaceous lower Williams fork formation, Piceance Basin, Colorado. AAPG Bulletin, 95, 899–923. Scholar
  32. Strebelle, S. (2002). Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology, 34, 1–22.MathSciNetCrossRefGoogle Scholar
  33. Tolosana-Delgado, R., Pawlowsky-Glahn, V., & Ecozcue, J. (2008). Indicator kriging without order relation violations. Mathematical Geoscience, 40, 327–347.MathSciNetCrossRefGoogle Scholar
  34. Zhang, T. (2015). MPS-driven digital rock modeling and upscaling. Mathematical Geoscience, 47, 937–954.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Y. Z. Ma
    • 1
  1. 1.SchlumbergerDenverUSA

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