Stochastic Simulation of Lithofacies: an Improved Sequential Indicator Approach

Reservoir characterization demands the estimation or simulation of important reservoir parameters such as permeability and porosity. Often, reservoir geology is accounted for via continuous values of permeability or porosity, without explicit reference to the reservoir facies. The approach proposed starts by modeling the spatial distribution of the reservoir facies and, only then, conditions the generation of permeability/porosity values to the simulated facies geometry. It allows accounting for spatial relationships between different lithologies (covariances and crosscovariances), and uses such relations to estimate (or simulate) the most probable lithology at any specific location. The ARCO data set is used to build stochastic simulations of six different lithofacies over a particular vertical section, considering only three conditioning wells out of ten actually available. Each stochastic simulation is a lithological reservoir image which reproduces the patterns of continuity of the lithofacies considered and honor the data values at the conditioning well locations. Repeated generation of such lithofacies images, allows assesment of the geological heterogeneities impact on the oil recovery.