Spectral induced polarization of clay-sand mixtures: Experiments and modeling

Okay, G.; Leroy, Philippe; Ghorbani, Ahmad; Cosenza, Philippe; Camerlynck, Christian; Cabrera, Justo; Florsch, Nicolás; Revil, A.

doi:10.1190/geo2013-0347.1

Cited by 69 publications

(71 citation statements)

References 66 publications

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“…Leroy et al (2008) considered that the diffuse layer is continuous at the scale of the porous medium and hence does not polarize because particles are in contact with each other Okay et al 2014; Fig. 2).…”

Section: Complex Conductivity Model Of the Particlementioning

confidence: 99%

“…Tangential electromigration and counter-balancing diffusion fluxes, j Se + and j Sd + , respectively, occur in the Stern layer. No exchange of ions is considered between the Stern and diffuse layers (modified, from Okay et al 2014). Figure 3.…”

Section: Complex Conductivity Model Of the Particlementioning

confidence: 99%

“…related to transport properties, Revil et al 2005;Jougnot et al 2009). While the real part of complex conductivity is sensitive to electromigration processes in the pore space and at the mineral surface, its imaginary part is sensitive to polarization processes at the mineral surface (Weller et al 2013;Okay et al 2014). 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).…”

Section: Introductionmentioning

confidence: 99%

“…While the real part of complex conductivity is sensitive to electromigration processes in the pore space and at the mineral surface, its imaginary part is sensitive to polarization processes at the mineral surface (Weller et al 2013;Okay et al 2014). 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).…”

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: Complex Conductivity Model Of the Particlementioning

confidence: 99%

Section: Complex Conductivity Model Of the Particlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…With such a definition, the (apparent) formation factor is not a textural parameter of porous media but would depend on the salinity or total dissolved content (TDS) of the pore water. Surface conductivity in the electrical double layer surrounding the mineral grains needs to be accounted for (e.g., Waxman and Smits [1968], Vinegar and Waxman [1984], Okay et al [2014]). Surface conductivity is often neglected in hydrogeophysics as being an important contributor to the overall electrical conductivity of siliciclastic materials while its role has been recognized for many decades in soil sciences (e.g., Shainberg et al [1980], Friedman [2005] and references therein) and in well logging analysis (e.g., Waxman and Smits [1968]).…”

Section: Introductionmentioning

confidence: 99%

Complex conductivity of soils

Revil

Coperey

Shao

et al. 2017

Water Resources Research

138

191

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The complex conductivity of soils remains poorly known despite the growing importance of this method in hydrogeophysics. In order to fill this gap of knowledge, we investigate the complex conductivity of 71 soils samples (including four peat samples) and one clean sand in the frequency range 0.1 Hz to 45 kHz. The soil samples are saturated with six different NaCl brines with conductivities (0.031, 0.53, 1.15, 5.7, 14.7, and 22 S m−1, NaCl, 25°C) in order to determine their intrinsic formation factor and surface conductivity. This data set is used to test the predictions of the dynamic Stern polarization model of porous media in terms of relationship between the quadrature conductivity and the surface conductivity. We also investigate the relationship between the normalized chargeability (the difference of in‐phase conductivity between two frequencies) and the quadrature conductivity at the geometric mean frequency. This data set confirms the relationships between the surface conductivity, the quadrature conductivity, and the normalized chargeability. The normalized chargeability depends linearly on the cation exchange capacity and specific surface area while the chargeability shows no dependence on these parameters. These new data and the dynamic Stern layer polarization model are observed to be mutually consistent. Traditionally, in hydrogeophysics, surface conductivity is neglected in the analysis of resistivity data. The relationships we have developed can be used in field conditions to avoid neglecting surface conductivity in the interpretation of DC resistivity tomograms. We also investigate the effects of temperature and saturation and, here again, the dynamic Stern layer predictions and the experimental observations are mutually consistent.

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Predicting permeability from the characteristic relaxation time and intrinsic formation factor of complex conductivity spectra

Revil

Binley

Mejus

et al. 2015

Water Resources Research

103

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Low-frequency quadrature conductivity spectra of siliclastic materials exhibit typically a characteristic relaxation time, which either corresponds to the peak frequency of the phase or the quadrature conductivity or a typical corner frequency, at which the quadrature conductivity starts to decrease rapidly toward lower frequencies. This characteristic relaxation time can be combined with the (intrinsic) formation factor and a diffusion coefficient to predict the permeability to flow of porous materials at saturation. The intrinsic formation factor can either be determined at several salinities using an electrical conductivity model or at a single salinity using a relationship between the surface and quadrature conductivities. The diffusion coefficient entering into the relationship between the permeability, the characteristic relaxation time, and the formation factor takes only two distinct values for isothermal conditions. For pure silica, the diffusion coefficient of cations, like sodium or potassium, in the Stern layer is equal to the diffusion coefficient of these ions in the bulk pore water, indicating weak sorption of these couterions. For clayey materials and clean sands and sandstones whose surface have been exposed to alumina (possibly iron), the diffusion coefficient of the cations in the Stern layer appears to be 350 times smaller than the diffusion coefficient of the same cations in the pore water. These values are consistent with the values of the ionic mobilities used to determine the amplitude of the low and high-frequency quadrature conductivities and surface conductivity. The database used to test the model comprises a total of 202 samples. Our analysis reveals that permeability prediction with the proposed model is usually within an order of magnitude from the measured value above 0.1 mD. We also discuss the relationship between the different time constants that have been considered in previous works as characteristic relaxation time, including the mean relaxation time obtained from a Debye decomposition of the spectra and the Cole-Cole time constant.

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Spectral induced polarization of clay-sand mixtures: Experiments and modeling

Cited by 69 publications

References 66 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

Complex conductivity of soils

Predicting permeability from the characteristic relaxation time and intrinsic formation factor of complex conductivity spectra

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