Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging

Abstract

Abstract. Computer X-ray microtomography (µXCT) represents a powerful tool for investigating the physical properties of porous rocks. While calculated porosities determined by this method typically match experimental measurements, computed permeabilities are often overestimated by more than 1 order of magnitude. This effect increases towards smaller pore sizes, as shown in this study, in which nanostructural features related to clay minerals reduce the permeability of tight reservoir sandstone samples. Focussed ion beam scanning electron microscopy (FIB-SEM) tomography was applied to determine the permeability effects of illites at the nanometre scale, and Navier–Stokes equations were applied to calculate the permeability of these domains. With these data, microporous domains (porous voxels) were defined using microtomography images of a tight reservoir sample. The distribution of these domains could be extrapolated by calibration against size distributions measured in FIB-SEM images. For this, we assumed a mean permeability for the dominant clay mineral (illite) in the rock and assigned it to the microporous domains within the structure. The results prove the applicability of our novel approach by combining FIB-SEM with X-ray tomographic rock core scans to achieve a good correspondence between measured and simulated permeabilities. This methodology results in a more accurate representation of reservoir rock permeability in comparison to that estimated purely based on µXCT images.

Transient conduit permeability controlled by a shift between compactant shear and dilatant rupture at Unzen volcano (Japan)

“…Blue and red circles represent data from this study, made parallel and perpendicular to the plane of shear, respectively. Grey circles show porosity data for Unzen from Mueller et al (2005) and Kendrick et al (2021), and open circles show permeability measurement on USDP drill cores from Watanabe et al (2008). Other symbols show data for effusive products at similar dome eruptions.…”

“…The rocks sampled across the shear zone and in the fault gouge at Mount Unzen vary in porosity between 8 % and 27 %; this range is slightly narrower than the porosity range (4-48 %) covered by blocks shed by pyroclastic density currents originating from the domes during the 5-year eruption (see Fig. 8; Kueppers et al, 2005;Coats et al, 2018;Kendrick et al, 2021;Scheu et al, 2007;Mueller et al, 2005). The narrower range exhibited by the spine shear zones may reflect the occurrence of fewer porosity-modifying mechanisms (e.g.…”

“…3), which are more susceptible to closure by increasing effective pressure than equant pores (e.g., Kendrick et al, 2021). Although this process occurs during confinement in both orientations, it only impacts permeability perpendicular to shear direction, and so contributes to enhanced anisotropy of permeability in banded shear fabrics under confinement (Kendrick et al, 2021).…”

“…8; Klug and Cashman, 1996;Mueller et al, 2005;Farquharson et al, 2015) for various types of volcanic rocks (e.g. explosive clasts vs effusive lavas), including the presence of heterogeneous structures (Farquharson et al, 2016c;Kolzenburg et al, 2012;Lamur et al, 2017;Kendrick et al, 2021), and these constraints have been invoked in diverse models to assess how magma permeability may evolve leading to eruption (Burgisser et al, 2019;Edmonds et al, 2003). However, the deformability of magma imposes constant changes to the porous permeable network and to date, only a few studies have measured or assessed the transience of permeability and porosity during magma deformation (Okumura et al, 2010(Okumura et al, , 2012Kendrick et al, 2013;Ashwell et al, 2015;Kennedy et al, 2016), especially in operando (Kushnir et al, 2017;Wadsworth et al, 2017;Wadsworth et al, 2021).…”

Multi-physics evaluation of carbonate-rich source rocks from high-resolution images

“…Thus, DRP can complement challenging laboratory measurements or provide estimated properties when accurate laboratory measurements are not available. [13][14][15][16][17][18][19] Labeled images from image segmentation are assigned physical properties of the known materials for advanced mathematical simulations of physical characteristics such as flow behavior, electrical and thermal conductivity, and elasticity. [13][14][15][16][17]19] The results of these simulations can then be reviewed in the context of the changing structural framework.…”