Pluri-Gaussian Simulations

Pluri-Gaussian Simulations (PGS) allow simulating the spatial distribution of categorical variables (like geological Facies or Rock-Types) with a Plurigaussian model. For detailed information about the Pluri-Gaussian Simulation methodology, Please refer to the Technical Reference "PGS".

Simulations can be constrained to honor input data (like Facies observed at wells). In such a case, we talk about Conditional Simulations. Each element of the conditioning categorical variable is called a "Lithotype".

This implementation of the Pluri-Gaussian Simulation methodology is innovative and different than the usual implementations. Traditionally, the variograms of the two underlying Gaussian Random Functions are inferred from the experimental variograms of the lithotypes indicator functions. Here, we calculate directly the variograms of the Gaussian values assigned to each sample where a lithotype is defined. Details about this methodology can be found in:

Nicolas Desassis, Didier Renard, Hélène Beucher, Sylvain Petiteau, Xavier Freulon. A pairwise likelihood approach for the empirical estimation of the underlying variograms in the plurigaussian models. 2015. hal-01213962v2.

This publication can be accessed at the address: https://hal.archives-ouvertes.fr/hal-01213962

The whole Pluri-Gaussian Simulations process can be run by providing information in three successive windows:

• the definition of the input data (categorical variable, conditioning data, Proportions),
• the definition of the geostatistical model (lithotypes rules and variogram of each underlying Gaussian Random Function)
• the definition of the output variables.