Stochastic simulation of categorical variables using a classification algorithm and simulated annealing
(1996) Mathematical Geology — Vol. 28, n° 7, p. 909-921 (1996)
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- 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. ntis 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 post-processed facies map.
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Goovaerts, P. (1996). Stochastic simulation of categorical variables using a classification algorithm and simulated annealing. Mathematical Geology, 28(7), 909-921. https://doi.org/10.1007/BF02066008 (Original work published 1996)