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Exploring Multiple-Point Realization Quality Through Connected Geobodies


Multiple-point simulations (MPS) are booming stochastic simulation methods due to their ability to better take into account higher-order statistical structures than classic variogram-based approaches. They borrow the multiple-point statistics not from the data but from an external representation of the expected Geology. However, MPS realizations reproduce more or less accurately the structures of that external representation depending on the method and parameters used. This issue is even more worrying when dealing with a high number of realizations: it provides some poor quality realizations to the following reservoir analysis steps, leading to incorrect flow simulations. In this work, we propose a general methodology to objectively assess the quality of a realization and distinguish unsatisfying ones in terms of structure reproduction, saving their useless treatment. This quality assessment is based on several indicators used to compare the realizations with a reference image, corresponding to the training image when using MPS. In addition to classic indicators such as the facies proportions, we propose to check parameters linked to the connected geobodies. Among them, some describe the spatial repartition of the connected geobodies (e.g. density, proportion of crossing geobodies,…) while others give information on their global shape (e.g. volume, box volume ratio,…) or on their topology though a skeleton extraction. To facilitate the quality analysis, we rely on the computation of dissimilarities between the images based on those indicators. The dissimilarities are analyzed using a heat map and a two-dimensional representation based on multidimensional scaling. The methodology is then applied on a synthetic case and associated realizations made with different simulation methods. Whereas multidimensional scaling is a powerful visualization tool, it induces some errors in the representation of the dissimilarities and should be only used for a first-order analysis. Details considering the relationship between the realizations and the methods should be preferably analyzed on the heat map as it represents directly the dissimilarities. If the visualization and analysis process of the dissimilarities is quite satisfying, further work should be done to improve the indicator capacity to capture the realizations characteristic.