On the specific polarizability of sands and sand-clay mixtures

Weller, Andreas; Slater, Lee; Huisman, Johan Alexander; Esser, Odilia; Haegel, Franz‐Hubert

doi:10.1190/geo2014-0509.1

Cited by 33 publications

(41 citation statements)

References 16 publications

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“…We observed the same trend for mica‐rich sands in our data. For quartz‐clean sands, Weller et al () observed a lower

C_{p}

value (5.8 × 10 −12 S), which in our case is replicated by the glacial sands showing lower

C_{p}

values, although they are not as uniform as the laboratory sands. Overall, the estimated

σ_{max}^{''}

deviates from

{C_{p} S}_{p o r}

less than 1 order of magnitude for most of the samples, suggesting that estimates of permeability within 1 order of magnitude can be expected from IP data in this study.…”

Section: Resultssupporting

confidence: 73%

“…The relationships derived from their laboratory measurements for different

C_{p}

values are shown for reference in Figure d. Weller et al () found that sand mixed with muscovite and illite, has a higher

C_{p}

value (24 × 10 −12 S) than the common value of

C_{p}

(10 × 10 −12 s) suggested by Weller et al (). We observed the same trend for mica‐rich sands in our data.…”

Section: Resultsmentioning

confidence: 93%

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Permeability Estimation Directly From Logging‐While‐Drilling Induced Polarization Data

Fiandaca

Maurya

Balbarini

et al. 2018

Water Resources Research

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In this study, we present the prediction of permeability from time domain spectral induced polarization (IP) data, measured in boreholes on undisturbed formations using the El‐log logging‐while‐drilling technique. We collected El‐log data and hydraulic properties on unconsolidated Quaternary and Miocene deposits in boreholes at three locations at a field site in Denmark, characterized by different electrical water conductivity and chemistry. The high vertical resolution of the El‐log technique matches the lithological variability at the site, minimizing ambiguity in the interpretation originating from resolution issues. The permeability values were computed from IP data using a laboratory‐derived empirical relationship presented in a recent study for saturated unconsolidated sediments, without any further calibration. A very good correlation, within 1 order of magnitude, was found between the IP‐derived permeability estimates and those derived using grain size analyses and slug tests, with similar depth trends and permeability contrasts. Furthermore, the effect of water conductivity on the IP‐derived permeability estimations was found negligible in comparison to the permeability uncertainties estimated from the inversion and the laboratory‐derived empirical relationship.

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“…We observed the same trend for mica‐rich sands in our data. For quartz‐clean sands, Weller et al () observed a lower

C_{p}

value (5.8 × 10 −12 S), which in our case is replicated by the glacial sands showing lower

C_{p}

values, although they are not as uniform as the laboratory sands. Overall, the estimated

σ_{max}^{''}

deviates from

{C_{p} S}_{p o r}

less than 1 order of magnitude for most of the samples, suggesting that estimates of permeability within 1 order of magnitude can be expected from IP data in this study.…”

Section: Resultssupporting

confidence: 73%

“…The relationships derived from their laboratory measurements for different

C_{p}

values are shown for reference in Figure d. Weller et al () found that sand mixed with muscovite and illite, has a higher

C_{p}

value (24 × 10 −12 S) than the common value of

C_{p}

(10 × 10 −12 s) suggested by Weller et al (). We observed the same trend for mica‐rich sands in our data.…”

Section: Resultsmentioning

confidence: 93%

Permeability Estimation Directly From Logging‐While‐Drilling Induced Polarization Data

Fiandaca

Maurya

Balbarini

et al. 2018

Water Resources Research

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“…Weller et al . () show that interlayer spaces in swelling clay minerals may contribute to the polarization in sand–clay mixtures, where small amounts of clay minerals (up to 1.9 weight %) are dispersed in the sample. The distribution of clay minerals in the sample (connected or dispersed) seems indeed to be a critical factor regarding whether these clay minerals will contribute to polarization or not (Osterman et al .…”

Section: Discussionmentioning

confidence: 99%

“…It is also suggested that the presence of clay minerals may cause an increase in polarization effects, in addition to an increased surface conductivity (Vinegar and Waxman ; Weller et al . ; Leroy et al . ).…”

Section: Introductionmentioning

confidence: 99%

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Influence of smectite and salinity on the imaginary and surface conductivity of volcanic rocks

Levy

Weller

Gibert

2019

Near Surface Geophysics

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We investigate the complex conductivity behaviour of natural volcanic rocks containing variable amounts of smectite in multi‐salinity experiments. We compare the results with relationships established for sandstones. Considering only samples with little volume of metallic particles, we observe similar and small phase‐angles at low frequency for all samples at all salinities (less than 25 mrad at 1 Hz). Yet, a wide range of cation exchange capacity, porosity and formation factor is covered by the sample set: 0.5–50 meq/100 g, 4–40% and 18–780, respectively. Our results show that, in the absence of metallic particles, the ratio between imaginary conductivity and surface conductivity is significantly lower for altered volcanic rocks than for sandstones and decreases with the smectite content. These observations indicate that an increased smectite content causes more conduction and less polarization, which could be explained by the onset of a continuous conduction pathway throughout connected interfoliar spaces of smectite. Due to this pathway, cations from the pore fluid may penetrate the solid lattice, for example through connected smectite aggregates clogging the fracture network, thus preventing polarization. We also observe that the relationship between imaginary conductivity and surface conductivity, at one salinity or over the whole salinity range, is not more significant than the relationship between the imaginary conductivity and the total real conductivity. Therefore, we suggest that the imaginary conductivity cannot be used to discriminate the contributions from smectite and pore water to the total conductivity of altered volcanic rocks.

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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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On the specific polarizability of sands and sand-clay mixtures

Cited by 33 publications

References 16 publications

Permeability Estimation Directly From Logging‐While‐Drilling Induced Polarization Data

Permeability Estimation Directly From Logging‐While‐Drilling Induced Polarization Data

Influence of smectite and salinity on the imaginary and surface conductivity of volcanic rocks

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

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