Geomodelling aims at representing as well as possible the reservoir heterogeneities in terms of lithofacies and petrophysical variables. Consequently, the modelling methods and their optimization play an important role for a refined and adequate modelling. When modelling, the issues to tackle are, among others: the level of realism of the facies simulations, the conditioning of simulations to wells data and the spatial behavior (local anisotropy integration for instance). Several simulations methods are available either in geological modelling or in petrophysical modelling. All those methods have their pros and cons. Multiple-points Statistics simulations (MPS), at the edge between pixel-based and object-based methods, may look more promising but needs reliable and consistent prior information for the facies distribution and relationships (geological training image). This paper aims at combining several algorithms in order to benefit from their advantages and therefore optimize the modelling. The following workflow is proposed: first, a process-based algorithm for Meandering Channels simulations is run in order to generate realistic facies simulations. This geological information is then used as a training image for MPS simulations, allowing efficient data conditioning. Finally, locally varying anisotropies are integrated for ensuring a better continuity of porosity simulations in the main direction of each channel.


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