Conditioning a Process-Based Fluvial Model Using a Non-Stationary Multiple-Point Statistics Approach

This paper presents the application of a non-stationary multiple-point (MP) simulation method to conditioning a process-based fluvial model. The process-based model mimics the depositional process of a meandering channel system and provides training images for the MP simulation. The MP simulation aims at reproducing the geometrical features of a principal variable (e.g., geological facies) while honoring a spatial trend represented by an auxiliary variable (e.g., facies proportion). This method performs the inference of the MP statistics directly from a training image of the principal variable and a corresponding training image of the auxiliary variable. It differs from the existing non-stationary MP simulation methods by accounting for the support of the auxiliary variable. Simulation examples extracted from horizontal and vertical sections of a 3D model show good performance of the proposed method both in reproducing the geometrical features of the principal training image and in honoring the auxiliary data. The extension of the method to 3D is straightforward although the computational efficiency needs to be improved.