Can Geostatistical Models Represent Nature’s Variability? An Analysis Using Flume Experiments

One of the difficulties in multi-point geostatistics (MPS) is the definition of the training image (TI). In the context of uncertainty modeling, the construction of a set of TIs is desirable, but the number of TIs and the characteristics that should to be varied in the TI are not well understood. In this research, we explore the question of the definition of the TIs using tank experiments. A set of snapshots of delta deposits seen in the tank are used to explore the variability of the system over time and to see if MPS can reproduce the variability of the set of images using only a few, well-selected images that are taken as TI. Preliminary methodologies are explored to select representative images, where the variation of the deposits over time is studied. Our results show that MPS was able to reproduce the variability in the full set of images, hence the variability of the studied system. Analyzing the characteristics of the selected images is a first step forward in the attempt to define TIs. This study only present preliminary investigations and more general answers will be explored.