Mathematical Geology

, Volume 34, Issue 1, pp 1–21 | Cite as

Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics

  • Sebastien Strebelle


In many earth sciences applications, the geological objects or structures to be reproduced are curvilinear, e.g., sand channels in a clastic reservoir. Their modeling requires multiple-point statistics involving jointly three or more points at a time, much beyond the traditional two-point variogram statistics. Actual data from the field being modeled, particularly if it is subsurface, are rarely enough to allow inference of such multiple-point statistics. The approach proposed in this paper consists of borrowing the required multiple-point statistics from training images depicting the expected patterns of geological heterogeneities. Several training images can be used, reflecting different scales of variability and styles of heterogeneities. The multiple-point statistics inferred from these training image(s) are exported to the geostatistical numerical model where they are anchored to the actual data, both hard and soft, in a sequential simulation mode. The algorithm and code developed are tested for the simulation of a fluvial hydrocarbon reservoir with meandering channels. The methodology proposed appears to be simple (multiple-point statistics are scanned directly from training images), general (any type of random geometry can be considered), and fast enough to handle large 3D simulation grids.

geostatistics stochastic simulation training image random geometry 


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Copyright information

© International Association for Mathematical Geology 2002

Authors and Affiliations

  • Sebastien Strebelle
    • 1
  1. 1.Department of Geological and Environmental SciencesStanford UniversityStanford

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