Relevance of computational rock physics

Abstract

To validate the transport (fluid and electrical) and elastic properties computed on CT scan pore-scale volumes of natural rock, we first contrast these values to physical laboratory measurements. We find that computational and physical data obtained on the same rock material source often differ from each other. This mismatch, however, does not preclude the validity of either of the data type — it only implies that expecting a direct match between the effective properties of two volumes of very different sizes taken from the same heterogeneous material is generally incorrect. To address this situation, instead of directly comparing data points generated by different methods of measurement, we compare trends formed by such data points. These trends include permeability versus porosity; electrical formation factor versus porosity; and elastic moduli (elastic-wave velocity) versus porosity. In the physical laboratory, these trends are generated by measuring a significant number of samples. In contrast, in the computational laboratory, these trends are often hidden inside a very small digital sample and can be derived by subsampling it. Hence, we base our validation paradigm on the assumption that if these computational trends match relevant physical trends and/or theoretical rock physics transforms, the computational results are correct. We present examples of such validation for clastic and carbonate samples, including drill cuttings.

Conditional reconstruction: An alternative strategy in digital rock physics

“…Figure 1 shows a graphical flowchart of the proposed method. According to this figure, the first step of standard DRP (Dvorkin et al, 2011;Andrä et al, 2013aAndrä et al, , 2013b) is replaced by three phases (Figure 1a.I-a.III), but the second (Figure 1b) and third ( Figure 1c) steps are as standard DRP.…”

“…However, using this method does not to let some external patterns to appear in the simulation. An optimal solution, which we used in this study, is to use a large 2D image and divide it to subimages with a size of 128 × 128 pixels (the same as producing subsamples in Dvorkin et al, 2011). This approach has the advantage of using several direct 2D images with the real rock structures that can exhibit the heterogeneity and complexity in the sample more realistically.…”

“…Besides, using very large 3D images of different samples can increase the computational time dramatically. For this reason, Dvorkin et al (2011) and Dvorkin and Derzhi (2012) show that different small proportions of a large sample can represent a trend that is valid over a range of scales. In the laboratory, these trends are produced by measuring a significant number of samples.…”

“…Modern microcomputed tomography (μCT) scanners produce 3D high-resolution images of micropore structures of rock samples, which can be used in digital rock physics (DRP). Usually, a standard DRP is based on three steps (Dvorkin et al, 2011;Andrä et al, 2013aAndrä et al, , 2013bBlunt et al, 2013): (1) high-resolution 3D imaging, (2) image segmentation using image processing algorithms to delineate and separate pore space and mineral phases, and (3) simulation of physical properties, such as fluid flow, electrical current flow, and elastic deformation to quantify permeability, resistivity, and elastic-moduli/elastic-wave, respectively. Based on the reported comparisons between the rock properties produced by DRP methods and laboratory measurements, they indicate that DRP results are valid enough to be used in the petroleum industry (Dvorkin and Derzhi, 2012;Lopez et al, 2012;Madonna et al, 2012;Ceron et al, 2013;Ringstad et al, 2013;Jouini et al, 2014;Liubis and Harith, 2014).…”

Applications of digital core analysis and hydraulic flow units in petrophysical characterization

“…X-ray CT can achieve wider field of view and very high resolution in 3D space (Ge et al, 2015). Despite the lower resolution in comparison with µxCT and SEM images, the thin section images have other advantages such as a wider range of view, saved time and cheaper availability for petrophysical studies, such as pore microstructure (Desbois et al, 2011;Rabbani et al, 2014a;Borazjani et al, 2016;Gundogar et al, 2016;Rabbani et al, 2016;Xiao et al, 2016), mineral recognition and classification (Hofmann et al, 2013;Asmussen et al, 2015;Izadi et al, 2015;Izadi et al, 2017b), specific surface area (Rabbani and Jamshidi, 2014;Rabbani et al, 2014b), elastic modulus (Arns et al, 2002;Dvorkin et al, 2011;Madonna et al, 2012;Saxena and Mavko, 2016), rock type determination (Mynarczuk, 2010;Mynarczuk et al, 2013;Ge et al, 2015;Mollajan et al, 2016), pore-grain analysis (Rabbani and Jamshidi, 2014;Rabbani et al, 2014b;Song et al, 2016), flowing property (Peng et al, 2016;Wang et al, 2016b). Using thin section images, the interconnected pore structure can be marked out visually as they are filled by color epoxy resin.…”