Mathematical Geosciences

, Volume 40, Issue 7, pp 773–788 | Cite as

Fast FILTERSIM Simulation with Score-based Distance

  • Jianbing WuEmail author
  • Tuanfeng Zhang
  • André Journel


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.


Multiple-point simulation Geostatistics Reservoir modeling Classification Data conditioning Training image 


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Copyright information

© International Association for Mathematical Geology 2008

Authors and Affiliations

  1. 1.Department of Energy Resources EngineeringStanford UniversityStanfordUSA
  2. 2.Schlumberger-Doll ResearchCambridgeUSA

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