Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics

• Published in: Mathematical geology Vol. 34; no. 1; pp. 1 - 21
• Main Author: Strebelle, Sebastien
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer 01.01.2002; Springer Nature B.V
• Subjects: Earth sciences; Earth, ocean, space; Exact sciences and technology; Geological structures; Geology; Hydrocarbons; Mathematical models; Reservoirs; Sedimentary rocks; Statistics; Stratigraphy; Training; geostatistics; reservoir rocks; algorithms; hydrocarbons; reservoirs; variograms; digital simulation; channel geometry; image analysis; heterogeneity; fluvial environment; sand; stochastic models; numerical models; geometry; statistical analysis; meanders
• ISSN: 0882-8121; 1874-8961; 1573-8868; 1874-8953
• DOI: 10.1023/A:1014009426274

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.[PUBLICATION ABSTRACT]