Robust MPS-Based Modeling via Spectral Analysis

Spatially distributed phenomena typically do not exhibit Gaussian behavior, and consequently methods constrained to traditional two-point covariance statistics cannot correctly represent the spatial connectivity of such phenomena. This necessitates the development of multiple-point statistics (MPS)-based algorithms. However, due to the sparse data available to infer these MPS statistics, one needs to have a training image (TI) to accurately model higher-order statistics. Training images are usually inferred from outcrops and/or conceptual models and are subject to uncertainty that have to be accounted for in MPS algorithms.

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