Original Paper

Computational Geosciences

, Volume 16, Issue 3, pp 779-797

First online:

Multiple-point geostatistical modeling based on the cross-correlation functions

  • Pejman TahmasebiAffiliated withDepartment of Mining, Metallurgy and Petroleum Engineering, Amir Kabir University of Technology
  • , Ardeshir HezarkhaniAffiliated withDepartment of Mining, Metallurgy and Petroleum Engineering, Amir Kabir University of Technology
  • , Muhammad SahimiAffiliated withMork Family Department of Chemical Engineering and Materials Science, University of Southern California Email author 

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An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements.


Multiple-point geostatistics Cross-correlation Training image Conditional simulation Adaptive recursive template splitting