PCTO-SIM: Multiple-point geostatistical modeling using parallel conditional texture optimization

Mohammadreza Pourfard

doi:10.1016/j.cageo.2016.12.012

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PCTO-SIM: Multiple-point geostatistical modeling using parallel conditional texture optimization

2017-05-01

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Mohammadreza Pourfard *

Mohammad Javad Abdollahifard

Karim Faez

Seyed Ahmad Motamedi

Tahmineh Hosseinian

DOI：10.1016/j.cageo.2016.12.012

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Abstract

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Multiple-point Geostatistics is a well-known general statistical framework by which complex geological phenomena have been modeled efficiently. Pixel-based and patch-based are two major categories of these methods. In this paper, the optimization-based category is used which has a dual concept in texture synthesis as texture optimization. Our extended version of texture optimization uses the energy concept to model geological phenomena. While honoring the hard point, the minimization of our proposed cost function forces simulation grid pixels to be as similar as possible to training images. Our algorithm has a self-enrichment capability and creates a richer training database from a sparser one through mixing the information of all surrounding patches of the simulation nodes. Therefore, it preserves pattern continuity in both continuous and categorical variables very well. It also shows a fuzzy result in its every realization similar to the expected result of multi realizations of other statistical models. While the main core of most previous Multiple-point Geostatistics methods is sequential, the parallel main core of our algorithm enabled it to use GPU efficiently to reduce the CPU time. One new validation method for MPS has also been proposed in this paper.

Keywords:

Big data

Conditional multiple-point simulations

Constrained optimization

Constrained texture synthesis

Energy minimization

GPU

Parallel processing

Self-enrichment

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