Porosity and permeability calculations in a biolithite using x-ray tomography images.

Successful characterization of carbonates starts from being able to properly describe them using globally defined classification standards such as (Folk, 1959; Dunham & Ham, 1962), etc. Key indices of rock quality and reservoir performance are usually directly or indirectly related to porosity and permeability which are reflections of the internal fabric of the rock. Carbonates are more susceptible to physicochemical reactions after deposition, albeit over a long period of time, these can alter the internal architecture of the rock creating complex pore networks and connectivity relationships. Anisotropy - the directional dependency on measurement of a property, e.g., permeability makes establishing a relationship with porosity even more complex – bearing in mind that anisotropy effect may also differ depending on the scale of measurement. The ability to reconstruct representative models of the pore network is a key factor in understanding the behaviour of any complex reservoir rock when subjected to flow/dynamic conditions. This work emphasizes on the importance of a consistent workflow, also discussed by (Aaron & Teng-Leong, 2021), during reservoir description, in addition to generating pore network models that have representative geometrical and topological characteristics at a microstructural scale. This enables better quantification of some reservoir parameters useful in solving problems related to fluid flow in porous media (Jiang, et al., 2010).
X-ray computed tomography (X-ray CT) technique is a non-destructive method which can be used for studying the internal architecture of core plug samples of complex rocks such as carbonates, unconventional tight sandstones/shales, etc. The technology and knowledge base for analyses of 3D digital images has also advanced significantly over the years making it easier to apply this methodology into workflows for evaluation of complex reservoirs (Mazurkiewicz & Mlynarczuk, 2013; Anselmetti, et al., 1998, etc). The ability to also process digital images at different scales enable better understanding of spatial continuity of porosity and permeability therefore producing different useful realizations of representative pore network models. However, the process of analysing digital images like many other processes also has its own uncertainties which can easily be obscured during evaluation and final interpretations can be significantly affected negatively. In this biolithite from an outcrop in Northern Greece, digital images of three test plugs with different orientations (B11, B23 & B33) taken from a core sample, including a high-resolution version, (B23HR) were given (Figure 1a). Porosity and permeability were calculated using pre-processed 3D digital images. Effective porosity was derived by binarization of these digital images and calculated as a fraction of the total area of objects to non-objects within a specified region of interest. Based on grey values, colour tables, and spatial-geometrical relationship of pore spaces, two major contributors to porosity have been classified in this material (Figure 1b). The smaller percentage of total pores are attributed to moldic macroporosity while the larger percentage of pores are attributed to interparticle microporosity. Trends of porosity with x-ray parameters such as integrated density and mean intensity were generated for each plug sample to give insight to the distribution of pores and their spatial/vertical relationships with each other (Figure 1c). Permeability in three orthogonal directions was calculated using “Pore Analysis Tools” software application (Jiang, et al., 2010). Strong anisotropy, seen in B23 is controlled by preferential orientation of some pores (Figure 1d), the origin being under investigation. The Kz component had the highest magnitude in all three samples and the B33 sample generally had lower Kz compared to the other samples (Figure 1e). The unique fabric of this rock is such that the calculated vertical plug permeabilities, within a very small separation distance (in the same core) are different – hence heterogeneity and anisotropy is predicted.
Scope for further work exists by incorporating results from other techniques; mercury injection capillary pressure (MICP), nitrogen gas adsorption, scanning electron microscope (SEM) analysis and corresponding mineralogical interpretation from thin section analysis, etc.