Cleaning categorical variable (lithofacies) realizations with maximum a-posteriori selection
Categorical variable images frequently present unrealistic small scale variations (noise) that are an artefact of measurement error or the geostatistical simulation method. In addition to their unpleasing appearance, these variations may have an impact on subsequent petrophysical property modeling and flow simulation. Thus, there is a need to clean such realizations without altering their desirable features (reproduction of local data and large scale spatial structure). This paper presents a straightforward algorithm and program for such cleaning. Existing algorithms such as erosion/dilation, quantile-transformation, or iterative simulated annealing-based approaches for image cleaning have significant limitations when dealing with more than two categories. The key idea behind the proposed method is to retain at each location the most probable lithofacies type based on the surrounding lithofacies types, the proximity to conditioning data, and any mismatch from the global target proportion. A number of examples are presented. FORTRAN source code for the proposed algorithm, available at the IAMG web site, is documented.
geostatistical realizations indicator simulation image processing