Complex conductivity of water-saturated packs of glass beads

Leroy, Philippe; Revil, A.; Kemna, Andreas; Cosenza, Philippe; Ghorbani, Ahmad

doi:10.1016/j.jcis.2007.12.031

Cited by 345 publications

(560 citation statements)

References 58 publications

(97 reference statements)

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“…Their complex conductivity model successfully reproduced the measured phase shift (between injected current and measured electrical potential difference) of glass beads as function of frequency. Leroy et al (2008) showed that two distinct peaks in the imaginary conductivity successfully capture the relaxation times associated with grain size distribution and surface roughness. However, the grain polarization model of Leroy et al (2008) was restricted to monovalent electrolytes and did not consider the effects of the diffuse layer on the Stern layer polarization.…”

Section: Introductionmentioning

confidence: 99%

“…At low frequencies (typically <1 kHz), there are two major phenomena controlling electrical charge transport in porous media containing no metallic particles: the complex conductivity of the bulk pore water associated with the electromigration of charge carriers in the interconnected, liquid-filled pore space and the complex interfacial or surface conductivity associated with the conduction and polarization processes in the EDL (Dukhin & Shilov 1974;Vinegar & Waxman 1984;Delima & Sharma 1992;Lesmes & Morgan 2001;Leroy et al 2008;Revil 2012). Conduction in bulk water strongly depends on the water chemical composition, the pore volume occupied by the liquid and the topology of the pore space (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The magnitude of the imaginary conductivity is controlled by the electrochemical properties and the surface area of the mineral/water interface (Vaudelet et al 2011;Bucker & Hordt 2013;Okay et al 2014). The frequency behaviour of the imaginary conductivity is controlled by relaxation of ions in the EDL, which depends on the size, shape, surface roughness of the particles or pores, and on the ionic surface mobility at the mineral/water interface (Leroy et al 2008;Bucker & Hordt 2013). Wu et al (2010) performed complex conductivity measurements and modelling of calcite precipitation on a column packed with glass beads.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the Cole-Cole model gives no information about the phenomena at the mineral/water interface responsible for the SIP response, such as the surface concentration and diffusivity of the ions in the EDL. Leroy et al (2008) proposed a grain polarization model accounting for the surface site density of adsorbed counter-ions in the Stern layer at the mineral/water interface to describe the complex conductivity response of glass beads at different salinities. Their complex conductivity model considers the electrochemical polarization of the Stern layer following Schwarz theory (Schwarz 1962) and was combined with a triple layer model (TLM) of the silica/water interface.…”

Section: Introductionmentioning

confidence: 99%

“…Their complex conductivity model considers the electrochemical polarization of the Stern layer following Schwarz theory (Schwarz 1962) and was combined with a triple layer model (TLM) of the silica/water interface. The differential effective medium theory (DEM ;Bruggeman 1935;Hanai 1968;Sen et al 1981) was used by Leroy et al (2008) Figure 1. Sketch of our complex conductivity model of the porous medium.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the evolution of complex conductivity during calcite precipitation on glass beads

Leroy

Li²,

Jougnot

et al. 2017

Geophys. J. Int.

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S U M M A R YWhen pH and alkalinity increase, calcite frequently precipitates and hence modifies the petrophysical properties of porous media. The complex conductivity method can be used to directly monitor calcite precipitation in porous media because it is sensitive to the evolution of the mineralogy, pore structure and its connectivity. We have developed a mechanistic grain polarization model considering the electrochemical polarization of the Stern and diffuse layers surrounding calcite particles. Our complex conductivity model depends on the surface charge density of the Stern layer and on the electrical potential at the onset of the diffuse layer, which are computed using a basic Stern model of the calcite/water interface. The complex conductivity measurements of Wu et al. on a column packed with glass beads where calcite precipitation occurs are reproduced by our surface complexation and complex conductivity models. The evolution of the size and shape of calcite particles during the calcite precipitation experiment is estimated by our complex conductivity model. At the early stage of the calcite precipitation experiment, modelled particles sizes increase and calcite particles flatten with time because calcite crystals nucleate at the surface of glass beads and grow into larger calcite grains. At the later stage of the calcite precipitation experiment, modelled sizes and cementation exponents of calcite particles decrease with time because large calcite grains aggregate over multiple glass beads and only small calcite crystals polarize.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the evolution of complex conductivity during calcite precipitation on glass beads

Leroy

Li²,

Jougnot

et al. 2017

Geophys. J. Int.

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

Slater

Barrash

Montrey

et al. 2014

Water Resources Research

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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.

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Geophysical signatures of disseminated iron minerals: A proxy for understanding subsurface biophysicochemical processes

Aal

Atekwana

Revil

2014

J. Geophys. Res. Biogeosci.

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Previous studies have linked biogeophysical signatures to the presence of iron minerals resulting from distinct biophysicochemical processes. Utilizing geophysical methods as a proxy of such biophysicochemical processes requires an understanding of the geophysical signature of the different iron minerals. Laboratory experiments were conducted to investigate the complex conductivity and magnetic susceptibility signatures of five iron minerals disseminated in saturated porous media under variable iron mineral content and grain size. Both pyrite and magnetite show high quadrature and inphase conductivities compared to hematite, goethite, and siderite, whereas magnetite was the highly magnetic mineral dominating the magnetic susceptibility measurements. The quadrature conductivity spectra of both pyrite and magnetite exhibit a well-defined characteristic relaxation peak below 10 kHz, not observed with the other iron minerals. The quadrature conductivity and magnetic susceptibility of individual and a mixture of iron minerals are dominated and linearly proportional to the mass fraction of the highly conductive (pyrite and magnetite) and magnetic (magnetite) iron minerals, respectively. The quadrature conductivity magnitude increased with decreasing grain size diameter of magnetite and pyrite with a progressive shift of the characteristic relaxation peak toward higher frequencies. The quadrature conductivity response of a mixture of different grain sizes of iron minerals is shown to be additive, whereas magnetic susceptibility measurements were insensitive to the variation in grain size diameters (1-0.075 mm). The integration of complex conductivity and magnetic susceptibility measurements can therefore provide a complimentary tool for the successful investigation of in situ biophysicochemical processes resulting in biotransformation or secondary iron mineral precipitation.

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Complex conductivity of water-saturated packs of glass beads

Cited by 345 publications

References 58 publications

Modeling the evolution of complex conductivity during calcite precipitation on glass beads

Modeling the evolution of complex conductivity during calcite precipitation on glass beads

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

Geophysical signatures of disseminated iron minerals: A proxy for understanding subsurface biophysicochemical processes

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