Complex conductivity measurements

Börner, F.

doi:10.1007/978-3-540-88405-7_4

Cited by 33 publications

(100 citation statements)

References 45 publications

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“…This parametric model provides usually a fairly good fit to experimental data in the frequency range 0.1 Hertz-100 Hertz (e.g., [1992]). Experimental data shows that b << 1 [Van Voorhis et al, 1973, Börner, 1992. Note that this model is also similar to the model of…”

Section: Appendix a Frequency Effect Versus Phasesupporting

confidence: 78%

“…in Vinegar and Waxman [1984] (their equation 8) and Börner [1992] (his equation 5), respectively. Since the phase is usually small and the conductivity is dominated by the inphase conductivity, we can also derive the expression used in the main text…”

Section: Appendix a Frequency Effect Versus Phasementioning

confidence: 99%

“…where n  denotes the amplitude factor, p is a frequency exponent, and n  the angular frequency normalized to 1 s -1 [Börner, 1992]. In order to determine the amplitude and the phase, we start with equation (A6),…”

Section: Börnermentioning

confidence: 99%

“…d. Chargeability versus specific surface area. For b) and c), the sandstone data are from Börner [1992] where the normalized chargeability are obtained using his quadrature conductivity data at 0.1 S m -1 using  = 8. Saturation dependence of the conductivity and phase spectra at low salinity (each curve corresponds to a distinct core sample).…”

Section: Börnermentioning

confidence: 99%

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Complex conductivity of soils

Revil

Coperey

Shao

et al. 2017

Water Resources Research

130

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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Section: Appendix a Frequency Effect Versus Phasesupporting

confidence: 78%

Section: Appendix a Frequency Effect Versus Phasementioning

confidence: 99%

Section: Börnermentioning

confidence: 99%

Section: Börnermentioning

confidence: 99%

See 2 more Smart Citations

Complex conductivity of soils

Revil

Coperey

Shao

et al. 2017

Water Resources Research

130

191

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show abstract

“…Gomez [22] reports a cementation exponent in the range of 1.8 -2.1. Similarly, Revil [23] presented a cementation exponent between 1.4 -2, while Borner reports 2 samples of Fontainebleau with a cementation factor of 1.6 -1.7 [24]. For spatial characteristics, two pencil type samples, 120 mm in length and with a diameter of 6.7 mm (or ca.…”

Section: Image Analysismentioning

confidence: 98%

Characterization of Fontainebleau Sandstone: Quartz Overgrowth and its Impact on Pore-Throat Framework

Saadi¹,

Wolf²,

Kruijsdijk³

2017

J Pet Environ Biotechnol

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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

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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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Complex conductivity measurements

Cited by 33 publications

References 45 publications

Complex conductivity of soils

Complex conductivity of soils

Characterization of Fontainebleau Sandstone: Quartz Overgrowth and its Impact on Pore-Throat Framework

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

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