Electrical‐hydraulic relationships observed for unconsolidated sediments in the presence of a cobble framework

Abstract

Mechanistic models now exist to predict hydraulic conductivity (K) from the spectral-induced polarization (SIP) response of granular media. We examined the predictions of such a model on unconsolidated coarse fluvial sediments and compared them to those obtained with a modified Kozeny-Carman (KC) model. Samples were retrieved from the Boise Hydrogeophysical Research Site (BHRS), located on a gravel bar adjacent to the Boise River, Idaho. A sample holder (0.102 m diameter and 0.12 m in length) was designed to include the cobble framework in reconstituted samples representing the primary stratigraphic units defined based on porosity variation at this site. SIP (0.001-1000 Hz) and K (from Darcy tests) measurements were recorded for 12 samples, with SIP measurements made as a function of pore fluid conductivity (3-300 mS/m), grain size distribution (GSD), and total porosity. K prediction with the KC model was improved after discounting of the cobble framework and multiplying by the tortuosity resulting from matrix ''capillaries'' around the cobbles, resulting in estimates within a factor of 5 of the measurements. K prediction with a mechanistic SIP model based on Stern layer polarization (SLP model) that requires an estimate of the GSD also required discounting for the cobble framework to obtain estimates within 0.5 orders of magnitude of the measurements. Similarly, the SLP model overpredicts the measured imaginary conductivity (r 00 ) unless the cobble framework is discounted, which then results in estimates of r 00 within a factor of 2 of the measurements. This can be explained by the fact that the cobbles polarize at frequencies well below the minimum measurement frequency (0.001 Hz). The SLP model for K prediction parameterized in terms of the formation factor and imaginary conductivity performed well for the 10 samples with a cobble framework without modification as the imaginary conductivity directly senses the matrix grain size characteristics, whereas the formation factor captures the porosity reduction and tortuosity resulting from the presence of the cobble framework (capillary tortuosity). Our findings suggest that the estimation of contrasts in K in coarse sediments may be achievable through measurements of electrical properties after appropriate consideration of the cobble fraction.

Spectral‐induced polarization measurements on sieved sands and the relationship to permeability

“…[], Slater et al . [], and Xu [], to test expressions of the kind given by equations and . Some of these data are from samples which are described as being sands and gravels which might be considered equivalent to the samples used in the present study, though some have significantly higher formation factors.…”

“…Water Resources Research 10.1002/2015WR018087 Weller et al [2015] used data from a variety of sources, including Weller et al [2013], Slater et al [2014], and Xu [2014], to test expressions of the kind given by equations (24) and (25). Some of these data are from samples which are described as being sands and gravels which might be considered equivalent to the samples used in the present study, though some have significantly higher formation factors.…”

Physical Explanation of Archie's Porosity Exponent in Granular Materials: A Process‐Based, Pore‐Scale Numerical Study

“…A better understanding of the physical significance of m will directly help geophysicists determine the appropriate values for m and F. In hydrocarbon explorations, an accurate m can significantly improve the reserve calculation from well-logging data (e.g., Glover, 2009;Shankar & Riedel, 2011). In hydrogeophysics, a correct F value is the key to characterizing permeability with either the characteristic pore sizebased model (Johnson et al, 1986) or the Kozeny-Carman equation incorporating tortuosity (e.g., Slater et al, 2014). In solid Earth studies, the estimation of porosity and fluid budget in subduction zone from resistivity data will also benefit significantly from a physical understanding of m (Naif et al, 2016).…”

Estimation of sediment texture from spectral induced polarisation data using cluster and principal component analysis

“…• textural properties, such as a representative grain or pore size and the grain or pore size distribution (Lesmes and Morgan 2001;Robinson and Friedman 2001;Scott and Barker 2003;Slater and Glaser 2003;Tong et al 2006;Kruschwitz et al 2010;Revil and Florsch 2010;Volkmann and Klitzsch 2010;Revil et al 2013;Slater et al 2014); • surface properties, such as the cation exchange capacity (Revil et al 2013), the specific surface area (Kruschwitz et al 2010;Weller et al 2010), and the amount of clay (Slater, Ntarlagiannis, and Wishart 2006;Boadu and Seabrook 2006;Oh, Kim, and Park 2007;Breede et al 2012); • fluid properties, such as the total dissolved solids or electrical conductivity (Lesmes and Frye 2001;Hördt and Milde 2012;Kavian, Slob, and Mulder 2012a;Ponziani et al 2011;Revil and Skold 2011;Skold, Revil, and Vaudelet 2011;Weller et al 2011;Weller and Slater 2012) and the saturation degree (Ulrich and Slater 2004;Oh et al 2007;Ghorbani et al 2009;Gomaa 2009;Jougnot et al 2010;Ponziani et al 2011;Breede et al 2012;Kavian et al 2012a,b;Grunat, Slater and Wehrer 2013); • organic fraction properties (Ponziani et al 2012), including also the presence of bacteria (Abdel Aal et al 2010) or contaminants …”

“…; Revil, Florsch, and Camerlynck ; Slater et al . ); surface properties, such as the cation exchange capacity (Revil et al . ), the specific surface area (Kruschwitz et al .…”