Permeability prediction based on induced polarization: Insights from measurements on sandstone and unconsolidated samples spanning a wide permeability range

Abstract

We have compared various induced polarization (IP) models for permeability prediction of the same general form that were all based on two parameters, the first being an electric substitute of effective porosity (the formation factor) and the second being an electric proxy of pore-normalized surface area (the imaginary part of electric conductivity). These models (empirically derived and based on mechanistic formulations) were applied to an extensive database acquired on sandstones and unconsolidated sandy materials. Whereas previous studies on permeability prediction mainly concentrated on either sandstone or unconsolidated sediments, we investigated a database composed of 94 samples including sandstones and unconsolidated material. Most of the samples in the database were saturated with a NaCl solution with an electric conductivity close to [Formula: see text]. Samples with a saturating fluid that deviated from this composition were corrected using recently published relationships describing the IP dependence on the pore fluid composition. In the case of the sandstone samples, the electric formation factor exerts the primary control on permeability, and the imaginary conductivity was found to be of little importance in permeability prediction. The opposite was observed for the unconsolidated samples, in which the imaginary conductivity was the most important term for permeability and the formation factor was found to be of little importance. The findings suggest that only one property (formation factor in the case of sandstone, imaginary conductivity in the case of unconsolidated samples) might be needed for the order of magnitude estimates of permeability from the popular form of the model based on the IP observations examined here. Whereas the formation factor was challenging to reliably estimate in situ, the imaginary conductivity was directly obtainable from an IP measurement. This suggests that field scale, single-frequency, IP-based estimation of permeability would be challenging, and possibly ineffective, in sandstones.

Subsurface imaging of water electrical conductivity, hydraulic permeability and lithology at contaminated sites by induced polarization

“…2). Empirical relations have been suggested in the literature also for the prediction of 610 permeability on consolidated sediments (e.g., Weller et al, 2015) and corrections for thedependence on fluid saturation have been proposed for both bulk conductivity (Archie, 1942) and 612 surface conductivity (e.g., Vinegar and Waxman, 1984;Ulrich and Slater, 2004). Nevertheless, the 613 extension of the proposed approach to unsaturated and/or consolidated media is beyond the scope 614 of this study, which deals with saturated unconsolidated sediments.…”

“…As an alternative approach, non-invasive geophysical 61 methods can be used in combination with drillings for improved 3D characterization of 62 contaminant plumes. 63 ′ determined from multisalinity resistivity measurements and Furthermore, Weller et al (2013) found that the salinity dependency of the real surface 146 conductivity parallels the salinity dependency of the imaginary surface conductivity, which can be 147 expressed as (Weller et al, 2011;Weller et al, 2015): 148 where * is the complex conductivity, 0 is the DC conductivity, 0 is the intrinsic chargeability, 182 is the relaxation time, is the frequency exponent, is the frequency and is the imaginary 183 unit. Using MCMC (Monte Carlo Markov Chain) analysis, Fiandaca et al (2017a) (Fig.…”

The hydrogeologic information in cross-borehole complex conductivity data from an unconsolidated conglomeratic sedimentary aquifer

“…The lack of variability in field scale σ 0 0 limits the estimation of subtle spatial variation of S por , and hence K, using the relationships developed by, for example, Börner and Schön (1991), Börner et al (1996), Lesmes (2002b), andWeller et al (2010). However, to investigate IP-derived estimates of hydraulic conductivity, we draw on the recent relationship empirically derived by Weller et al (2015) from a data set of 22 unconsolidated laboratory samples. They propose the link (equation 24 in Weller et al, 2015) between permeability k (in m 2 ) and complex electrical conductivity (σ and σ 0 0 expressed in mS∕m) as k ¼ 3.47 × 10 −16 σ 1.11 σ 0 02.41 ;…”

“…However, to investigate IP-derived estimates of hydraulic conductivity, we draw on the recent relationship empirically derived by Weller et al (2015) from a data set of 22 unconsolidated laboratory samples. They propose the link (equation 24 in Weller et al, 2015) between permeability k (in m 2 ) and complex electrical conductivity (σ and σ 0 0 expressed in mS∕m) as k ¼ 3.47 × 10 −16 σ 1.11 σ 0 02.41 ;…”

“…The complex conductivity (or induced polarization [IP]) method holds promise for improving quantification of hydrogeologic properties as information on the interconnected pore volume (e.g., ∅, tortuosity) and the mineral-fluid interfacial area (e.g., surface area to pore volume S por and effective grain size) can, in principle, be derived from the complex electrical measurements (Börner et al, 1996;Slater and Lesmes, 2002a;Weller et al, 2013). Recent advances have been made (Slater and Lesmes, 2002b;Revil and Florsch, 2010;Weller et al, 2015) in understanding the link between complex conductivity and hydraulic conductivity, mainly through empirical or semiempirical models (for example, assuming Kozeny-Carman and Archie petrophysics; Archie, 1942;Charbeneau, 1999). However, although inversion tools are now well-established for field application, few studies have attempted to assess the potential of complex conductivity imaging for deriving hydrogeologic information (see, e.g., Hördt et al, 2009;Attwa and Günther, 2013).…”