Is Cell-to-Cell Scale Variability Necessary in Reservoir Models?

Abstract

Reservoir models typically contain hundreds-of-thousands to millions of grid cells in which petrophysical properties such as porosity and permeability vary on a cell-to-cell basis. However, although the petrophysical properties of rocks do vary on a point-to-point basis, this variability is not equivalent to the cell-to-cell variations in models. We investigate the impact of removing cell-to-cell variability on predictions of fluid flow in reservoir models. We remove cell-to-cell variability from models containing hundreds of thousands of unique porosity and permeability values to yield models containing just a few tens of unique porosity and permeability values grouped into a few internally homogeneous domains. The flow behavior of the original model is used as a reference. We find that the impact of cell-to-cell variability on predicted flow is small. Cell-to-cell variability is not necessary to capture flow in reservoir models; rather, it is the spatially correlated variability in petrophysical properties that is important. Reservoir modelling effort should focus on capturing correlated geologic domains in the most realistic and computationally efficient manner.

Conditioning surface-based geological models to well data using artificial neural networks

“…If heterogeneity exists within these volumes, they are further subdivided by additional surfaces. In these geologically meaningful domains, no further property modelling is required [1,2]. The application of NURBS provides a computationally efficient representation as few control points are required to represent even complex curves and surfaces.…”

“…General trends and similarities with the reference data cannot be observed at well locations However, compared to other methods, the use of parametric, grid-free surface-based models for representing geological heterogeneity of interest such as features which influence the flow conditions has many advantages which outweigh any anticipated computational limitations. Furthermore, the number of surfaces required to model realistic reservoirs using surfaces has been shown by [2] to be low to generate representative models. 2.…”

“…Within these domains, petrophysical properties such as porosity and permeability are homogeneous and further property modelling is not required [1]. Previous work has shown that capturing the geometry, spatial distribution and connectivity of these domains is key to capturing subsurface flow [2]. The use of parametrized surfaces allows modelling of heterogeneity free from any underlying grid, providing greater flexibility for the creation and manipulation of complex, realistic geological geometries at low computational cost.…”

Dynamic mesh optimisation for geothermal reservoir modelling

“…2) which are efficient to construct and manipulate and are widely used in other areas of computer-aided-design (CAD) (Jacquemyn et al, 2019). NURBS surfaces are created automatically and probabilistically by placing control points at specific locations in space to create geologically meaningful geometries (Zhang and Pyrcz, 2006;Jacquemyn et al, 2019;Osman et al, 2020). Multiple surfaces can be created rapidly, and the geometry of each can be varied stochastically based on statistical data describing key aspects of the geometry.…”

“…Here, we identify the domains based on geology, rather than using an arbitrary mesh. It has been shown that capturing correlated variability using such geologically-defined domains is key for capturing flow (Osman et al, 2020).…”