Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling |
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Authors: | Yupeng Li Clayton V Deutsch |
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Institution: | (1) University of Alberta, Edmonton, AB, Canada |
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Abstract: | In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a
conditional probability distribution. The multivariate probability distribution of all the grouped locations including the
unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the
iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm,
the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using
lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles
along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate
probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order
marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated
with estimation and simulation example. |
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Keywords: | |
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