ALBERT

Description: Regridding geological models to a higher resolution for flow simulation is an important problem in geostatistical modeling. For practical reasons, over a large area, models can only be built at a relatively coarse resolution. Subsequently, the resolution of specified regions of interest must be increased before upscaling for flow modeling. The construction of a high-resolution model of the entire reservoir at the beginning of the evaluation may be impractical because of computational and time constraints. It is standard practice to implement nearest neighbor interpolation to increase the resolution of models. Although it is a simple practical solution, nearest neighbor interpolation introduces spatial continuity artifacts that are often unrealistic. This paper proposes an automatic stochastic regridding approach based on simulation. The simulation is conditioned to the initial coarse resolution model/realization. The process includes the extraction of specified regions of interest, definition of corresponding local variography, and implementation of Sequential Gaussian Simulation (SGS) and/or Sequential Indicator Simulation (SIS) to characterize continuous and categorical variables, respectively. In each specified region, the local variography can be defined by either implementing automatic fitting algorithms or assigning the global variography initially used to build the coarse resolution model. The regridding process is automated. The advantage of this approach over the conventional nearest neighbor interpolation is in the improvement in the realistic spatial variability features of small scale geologic heterogeneity. The benefits of obtaining a proper regridded model are discussed in a case study of a fluvial reservoir in the McMurray formation. One of the main reasons for generating high resolution models is in the appropriate characterization of small scale impermeable geobodies such as remnant shales. The coarse resolution models are not able to properly characterize the small scale geologic features of the shales; more amount of information is required to characterize smaller scale features. The metric of performance considered is the effective vertical permeability. The automated stochastic regridding workflow described in this paper is available on a Fortran platform with additional scripting which will be distributed upon request. Note that the terms "regridding" and "stochastic regridding" are used interchangeably and both refer to the proposed workflow of modeling at higher resolution.

Description: Blocking facies information to a constant length prior to three-dimensional (3D) modelling is necessary with current 3D geostatistical modelling techniques. The high-resolution information from core and well logging must be upscaled to unify the scale to a target scale considered in building the 3D numerical models. A downside is the inevitable loss of information when the majority facies is assigned to each upscaled interval. The loss of such information could become problematic when dealing with small shale barriers in the middle of the reservoir or at the boundary of the facies transitions. This paper addresses the information loss by retaining as much information as possible in the upscaling process and proposes a metric to account for small-scale information that is mixed during the process: such a metric is referred to as facies mixing measure (FMM). Retaining more information in the upscaling process and utilizing that information to better model petrophysical properties is an important contribution. FMM is calculated during the upscaling step and is treated as a secondary property during petrophysical property modelling. Cross-validation with two different datasets demonstrates improvements in porosity estimation.