Abstract
An important step of reservoir characterization is the stochastic modeling of the geometry of lithofacies which control large-scale heterogeneities of petrophysical properties. Although multiple realizations are necessary to appreciate the uncertainty in the spatial distribution of facies, a common short cut consists of retaining the first realization drawn. This paper presents an alternative to this potentially hazardous selection: (1) a categorical map is generated by allocating a single facies to each grid node according to the local probabilities of occurrence of the facies, and (2) the map then is post-processed using a steepest descent-type algorithm so as to improve reproduction of spatial continuity and transition probabilities between facies. The procedure is illustrated using a synthetic dataset. A waterflood simulation shows that retaining a single realization would yield, in average, larger errors in production forecasts (water cuts and recovered oil) than the single postprocessed facies map.
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References
Deutsch, C. V., and Cockerham, P., 1994, Practical considerations in the application of simulated annealing to stochastic simulation: Math. Geology, v. 26, no. 1, p. 67–82.
Deutsch, C. V., and Journel, A. G., 1992a, GSLIB: Geostatistical Software Library and user's guide: Oxford Univ. Press, New York, 340 p.
Deutsch, C. V., and Journel, A. G., 1992b, Annealing techniques applied to the integration of geological and engineering data: Stanford Center for Reservoir Forecasting. Stanford University, unpubl. Ann. Rept. No. 5, 120 p.
Doyen, P., Guidish, T., and de Buyl, M., 1989, Seismic discrimination of lithology in sand/shale reservoirs: a Bayesian approach: Proc. 59th annual SEG meeting, Dallas.
ECLIPSE 100 Reference Manual, 1991, Intera ECL Petroleum Technologies, Highlands Farm, Greys Road, Henley-on-Thames, Oxfordshire, England, 646 p.
Farmer, C., 1988. The generation of stochastic fields of reservoir parameters with specified geostatistical distributions,in Edwards, S., and King, P., eds., Mathematics in oil production: Clarendon Press, Oxford, p. 235–252.
Geman, S., and Geman, D., 1984, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images: IEEE Trans. Pattern Anal. Machine Intell. PAMI, v. 6, no. 6, p. 721–741.
Goovaerts, P., 1993, Comparison of coIK, IK, and mIK performances for modeling conditional probabilities of categorical variables,in Dimitrakopoulos, R., ed., Geostatistics for the next century: Kluwer Acad. Publ., Dordrecht, The Netherlands, p. 18–29.
Goovaerts, P., 1994, Prediction and stochastic modelling of facies types using classification algorithms and simulated annealing. Stanford Center for Reservoir Forecasting, Stanford University, unpubl. Ann. Rept. No. 7, 74 p.
Goovaerts, P., and Journel, A. G., 1996, Accounting for local probabilities in stochastic modeling of facies data: SPE Paper Number 29230, preprint.
Haldorsen, H., and Damsleth, E., 1990, Stochastic modeling: Jour. Petroleum Technology, v. 42, no. 4, p. 404–412.
Murray, C. J., 1992, Indicator simulation of petrophysical rock types,in Soares, A., ed., Geostatistics Tróia '92, Quantitative geology and geostatistics: Kluwer Acad. Publ., Dordrecht, The Netherlands, p. 399–411.
Soares, A., 1992, Geostatistical estimation of multi-phase structures: Math. Geology, v. 24, no. 2, p. 149–160.
Srivastava, R. M., 1992, Iterative models for spatial simulation: Stanford Center for Reservoir Forecasting, Stanford University, unpub. Ann. Rept. No. 5, 24 p.
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Goovaerts, P. Stochastic simulation of categorical variables using a classification algorithm and simulated annealing. Math Geol 28, 909–921 (1996). https://doi.org/10.1007/BF02066008
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DOI: https://doi.org/10.1007/BF02066008