Multiscale Digital Rock Analysis for Complex Rocks

Abstract

Many properties of complex porous media such as reservoir rocks are strongly affected by heterogeneity at different scales. Complex depositional and diagenetic processes have a strong control on the pore structures, leading to systems with a wide range of pore sizes covering many orders of magnitude in length scales. This poses a significant challenge for digital rock analysis since a single resolution image and associated simulation model cannot capture all the relevant length scales in sufficient detail due to limitations in computer memory and speed. The scale-transgressive effects of heterogeneity must therefore be accounted for through a multiscale digital rock workflow. We introduce a generalized multiscale imaging and pore-scale modelling workflow to derive transport properties of complex rocks having broad pore size distributions. A dry/wet micro-CT imaging sequence is used to spatially map the porosity and the connectivity of resolved and unresolved porous regions. The unresolved porosity regions are classified into different porosity classes or rock types. The resulting 3D rock-type map and the porosity map are combined and transformed into a multiscale pore network model. Resolved pores are treated in a conventional pore network manner while unresolved network elements are treated as a continuum Darcy-type porous medium. Similar to conventional continuum models, each Darcy pore is populated with single and multiphase flow properties. These properties are derived from high-resolution rock-type models constructed from backscatter SEM images and/or high-resolution micro-CT images of sub-samples. The multiscale digital rock workflow is applied to two heterogeneous rock samples: a mixed wet thinly laminated reservoir sandstone and an oil wet reservoir carbonate. Experimentally measured mercury-air primary drainage and oil-water imbibition capillary pressure curves (after ageing to restore wettability) are used to anchor the multiscale pore network model. Waterflood relative permeability is calculated in a blind test and compared with high-quality experimental data. A very encouraging agreement between computed and measured properties is found.

Anchoring Multi‐Scale Models to Micron‐Scale Imaging of Multiphase Flow in Rocks

“…Full details on the method can be found in Ruspini et al. (2021). We extracted two different PNMs of the same sample, based on respectively the porosity‐based sub‐rock typing map and the Pct‐based sub‐rock typing map (Figure 6).…”

“…Note that the sub-resolution imaging workflow may result in barely resolved pores or throats being treated as Darcy regions (Gao et al, 2019), which may lead to a poor approximation of their behavior in the model. Full details on the method can be found in Ruspini et al (2021). We extracted two different PNMs of the same sample, based on respectively the porosity-based sub-rock typing map and the Pct-based sub-rock typing map (Figure 6).…”

“…The simulation took fluid connectivity through wetting layers into account, and assumed a fluid to be connected through a Darcy element as long as the local relative permeability to the fluid was larger than zero. Full details are provided in Ruspini et al (2021).…”

“…An important aspect of the input uncertainty is that only a limited volume of microporosity is typically imaged at the highest resolutions, while there is often significant heterogeneity in its properties. Recent approaches have addressed this information gap by incorporating sub-voxel porosity maps (Ruspini et al, 2016(Ruspini et al, , 2021. The porosity map can be generated based on differential imaging: measuring the calibrated gray value change when the pore space is filled with a high-contrast fluid such as high-concentration potassium iodide or cesium chloride (Boone et al, 2014;Ghous et al, 2007;Lin et al, 2016).…”

The Impact of Sub-Resolution Porosity on Numerical Simulations of Multiphase Flow

“…Digital Rock Physics is made possible by advances in high resolution imaging techniques, notably X-ray Microtomography (XCT) (Baker et al, 2012;Singh et al, 2018;Kohanpur and Valocchi, 2020) and focused ion beam scanning electron microscopy (FIB-SEM) (Cnudde and Boone, 2013;Kelly et al, 2016;Welch et al, 2017;Ruspini et al, 2021). The first method, XCT, involves recording hundreds or thousands of two-dimensional (2-D) X-ray scans through a sample that are then computationally reconstructed to create a 3-D image.…”