A 3D code for mp simulation of continuous and categorical variables: FILTERSIM

In most petroleum and geoscience studies, the flow is controlled by the connectivity of extreme permeabilities (both high and low) associated with multiple-scale geological patterns. Hence, it is critical to model such patterns in order to predict successfully the flow performance. The new multiple-point geostatistical algorithm (FILTERSIM), which can handle both categorical and continuous variable training images, is implemented in the SGeMS software. The spatial patterns depicted by a training image are first summarized into a few filter scores; then classified into pattern groups in the filter score space. The sequential simulation approach proceeds by identifying each conditioning data event to a closest pattern group using some distance function. A training pattern is then sampled from that group and pasted back onto the simulation grid. The local multiple-point statistics is captured from the training image, and reproduced in the simulation realizations. Hence the complex multiple-scale geological structures can be re-constructed in the simulation grid, conditional to a variety of subsurface data such as well data and seismic survey. This paper gives the implementation details of the FILTERSIM algorithm and various example applications.