Abstract - Pixel-based multiple-point statistical (MPS) modelling is an appealing
geostatistical modelling technique as it easily honours well data and allows use
of geologically-derived training images to reproduce the desired heterogeneity. A
variety of different training image types are often proposed for use inMPSmodelling,
including object-based, surface-based and process-based models. The purpose of the
training image is to provide a description of the geological heterogeneities including
sand geometries, stacking patterns, facies distributions, depositional architecture and
connectivity. It is, however, well known that pixel-based MPS modelling has difficulty
reproducing facies connectivity, and this study investigates the performance
of a widely-available industrial SNESIM algorithm at reproducing the connectivity
in a geometrically-representative, idealized deep-water reservoir sequence, using
different gridding strategies and training images. The findings indicate that irrespective
of the sand connectivity represented in the training image, the MPS models
have a percolation threshold that is the same as the well-established 27% percolation
threshold of random object-based models. A more successful approach for generating
poorly connected pixel-based MPS models at high net:gross ratios has been
identified. In this workflow, a geometrical transformation is applied to the training
image prior to modelling, and the inverse transformation is applied to the resultant
MPS model. The transformation is controlled by a compression factor which defines
how non-random the geological system is, in terms of its connectivity.
In: Avalos Sotomayor, S.A., Ortiz, J.M., Srivastava, R.M. (eds) Geostatistics Toronto 2021. GEOSTATS 2021. Springer Proceedings in Earth and Environmental Sciences . Springer, Cham. 111-117, doi: https://doi.org/10.1007/978-3-031-19845-8_8, 2023.