ABSTRACT
An alternative to a deterministic approach is a stochastic one. In that case grains and/or pores are analyzed in terms of their size, shape and orientation. Based on these data corresponding distribution functions and correlations are obtained. This information is finally used to set-up stochastic equivalent models. Due to the stochastic (random) nature an arbitrary
number of equivalent deterministic models can be created. Such a procedure allows to determine robustness and sensitivity of models. Figures 3 shows an example of grain shape and size characterization proposed by Heilbronner & Barett (2013). Based on this proposed scheme, the following procedure was developed. First a huge number of clumps or clusters is created in a fully random fashion based on a pre-defined number of spheres as illustrated in Figure 4. Then, the parameters according to Heilbronner & Barett (2013) are used to classify all these clumps or clusters. Then, they are compared with grain shapes of the rock and those clumps and clusters, respectively, are selected which fit into the classification. Finally, they are adjusted in size and placed into the model specimen. Afterwards a correction in terms of adequate pore space distribution is necessary.
