Parameterization of training images for aquifer 3-D facies modeling, integrating geological interpretations and statistical inference

We develop a stochastic approach to construct channelized 3D geological models constrained to borehole measurements as well as geological interpretation. The methodology is based on simple 2D geologist-provided sketches of fluvial depositional elements, which are extruded in the 3rd dimension. Multiple-point geostatistics (MPS) is used to impair horizontal variability to the structures by introducing geometrical transformation parameters. The sketches provided by the geologist are used as elementary training images, whose statistical information is expanded through randomized transformations. We demonstrate the applicability of the approach by applying it to modeling a fluvial valley filling sequence in the Maules Creek catchment, Australia. The facies models are constrained to borehole logs, spatial information borrowed from an analogue and local orientations derived from the present-day stream networks. The connectivity in the 3D facies models is evaluated using statistical measures and transport simulations. Comparison with a statistically equivalent variogram-based model shows that our approach is more suited for building 3D facies models that contain structures specific to the channelized environment and which have a significant influence on the transport processes.