Homogenization Techniques in Digital Rock Physics

Abstract

The first part of the thesis explores the process of homogenization, particularly for the permeability properties of rock samples. The Hill-Mandel postulate of energy consistency throughout the transitioning of scales is revisited since traditional homogenization methods rely on applying specific boundary conditions to enforce energy consistency. However, it is shown that the applied boundary conditions influence the effective physical parameter and provide upper or lower bound estimations. Recently, it is shown that these boundary conditions influence a layer near the boundary of the sample and that homogenization applied on the subsample away from this boundary layer is not affected by the boundary conditions.

The research focuses on energy consistency by studying the evolution of the energy within the intrinsic subsample, away from the boundaries. With the help of Finite Element simulations of Stokes-flow through idealized structures, the energy of the fluid is traced without the influences of grain properties on the energy dissipation. By plotting the ratio of the energy dissipation of the macro- and micro-scale, it is shown that the energy consistency is not found within small subsamples. Yet, with a growing subsample, energy consistency is achieved naturally, without the enforcement of boundary conditions. As a result, it is concluded that the energy consistency is found at the Representative Elementary Volume (REV), which is a similar requirement as for traditional homogenization methods. The study of the natural energy consistency in idealized microstructures is extended to real microstructures, which include more natural heterogeneities, such as grain properties. It is shown that energy consistency is also found with the natural heterogeneities included, albeit with a slower convergence.

For the homogenization of the permeability of a sample, the energy ratio is now known to be unitary, which can be used as an accurate indicator to determine the size of the REV.

The second part of the thesis explores the process of determining the size of the REV, which is a common, yet essential practice in Digital Rock Physics. Currently, this is an extensive exercise, involving many and large-size simulations to trace the convergence of the physical property and requires a lot of computational resources and time. Numerical-statistical studies have shown that the convergence of the REV visualizes in a cone-like shape. By plotting the convergence for both the permeability and the energy dissipation ratio for idealized microstructures, this study analyses the shape and evolution of the cone of convergence. From this, the generic evolution law of the convergence is determined.

It is shown that the asymmetrical convergence cone is described with a log-normal distribution, with a stable mean throughout the evolution of the cone and a variance for each sample size. The evolution of the variance is described with the law of large numbers, taking into account a reference value. This makes it possible to determine the size of the REV. Since the statistical method applies, information about the error of the fit, the error of the determined homogenized property, and the error of the size of the REV is provided.

The study is extended to real microstructures to validate whether the determined evolution law applies when natural heterogeneities are included. It is shown that the evolution law still accurately describes the REV's convergence. Therefore, the REV's size of real rock samples can also be determined. Even when the REV is not included within the sample, the evolution law can provide an estimate of the size of the REV or the homogenized property.

By using the cone of convergence, it is not necessary to run simulations on the full sample to find the REV, which is computationally expensive, instead running a number of simulations on small subsamples is sufficient, which saves both time and computational resources. It also unlocks the possibility to find the REV for high-resolution samples, as splitting the sample into subsamples allows for smaller simulations.