Reference : Direct multiple-point geostatistical simulation of edge properties for modeling thin irr...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Geological, petroleum & mining engineering
http://hdl.handle.net/2268/74616
Direct multiple-point geostatistical simulation of edge properties for modeling thin irregularly-shaped surfaces
English
Huysmans, Marijke [Katholieke Universiteit Leuven - KUL > Dpt Earth and Environmental Sciences > > >]
Dassargues, Alain mailto [Université de Liège - ULg > Département Argenco : Secteur GEO3 > Hydrogéologie & Géologie de l'environnement >]
Sep-2010
8th International Conference On Geostatistics for Environmental Applications, GeoENV’2010
Cockx, L.
Van Meirvenne, M.
Bogaert, P.
D`Or, D.
16-18
No
8th International Conference On Geostatistics for Environmental Applications, GeoENV’2010
13-15 september 2010
Ghent University
Ghent
Belgium
[en] multiple-point ; geostatistics ; hydrogeology ; hydraulic conductivity ; edge properties ; irregularly shaped surfaces
[en] Thin irregularly-shaped surfaces such as clay drapes often have a major control on flow and transport in heterogeneous porous media. Clay drapes are often complex curvilinear 3-dimensional surfaces and display a very complex spatial distribution. Variogram-based stochastic approaches are often also not able to describe the spatial distribution of clay drapes since complex, curvilinear, continuous and interconnected structures cannot be characterized using only two-point statistics. Multiple-point geostatistics aims to overcome the limitations of the variogram. The premise of multiple-point geostatistics is to move beyond two-point correlations between variables and to obtain (cross) correlation moments at three or more locations at a time using "training images" to characterize the patterns of geological heterogeneity. Multiple-point geostatistics is able to reproduce thin irregularly-shaped surfaces such as clay drapes but is often computationally intensive. To capture the thin surfaces, a small grid cell size should be adopted for the training image. This results in large training images and a large search template size and thus a large CPU and RAM demand (Huysmans and Dassargues, 2009).
Researchers ; Professionals
http://hdl.handle.net/2268/74616

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