Fast FILTERSIM Simulation with Score-based Distance |
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Authors: | Jianbing Wu Tuanfeng Zhang André Journel |
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Institution: | (1) Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA;(2) Schlumberger-Doll Research, Cambridge, MA 02139, USA |
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Abstract: | FILTERSIM is a pattern-based multiple-point geostatistical algorithm for modeling both continuous and categorical variables.
It first groups all the patterns from a training image into a set of pattern classes using their filter scores. At each simulation
location, FILTERSIM identifies the training pattern class closest to the local conditioning data event, then samples a training
pattern from that prototype class and pastes it onto the simulation grid. In the original FILTERSIM algorithm, the selection
of the closest pattern class is based on the pixel-wise distance between the prototype of each training pattern class and
the local conditioning data event. Hence, FILTERSIM is computationally intensive for 3D simulations, especially with a large
and pattern-rich training image. In this paper, a novel approach is proposed to accelerate the simulation process by replacing
that pixel-wise distance calculation with a filter score comparison, which is the difference between the filter score of local
conditioning data event and that of each pattern prototype. This score-based distance calculation significantly reduces the
CPU consumption due to the tremendous data dimension reduction. The results show that this new score based-distance calculation
can speed up FILTERSIM simulation by a factor up to 10 in 3D applications. |
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Keywords: | Multiple-point simulation Geostatistics Reservoir modeling Classification Data conditioning Training image |
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