Abstract
Geophysical methods show promise for detecting the spatial variability of subsurface clay content and its effect on subsurface hydraulic properties. We have developed a laboratory study that examines the influence of clay content and distribution on the relationships between hydraulic conductivity [Formula: see text] and the physical and geophysical properties of the media. Two geophysical methods are investigated: spectral induced polarization (SIP) and nuclear magnetic resonance (NMR). We used synthetic sediment mixtures of sand and up to 10% kaolinite clay by mass; the clay was homogeneously mixed or was present as large (approximately 5 mm) clusters distributed through the sample. The [Formula: see text] varies moderately well (normalized root-mean-square error [[Formula: see text]rms error] = 0.393) with the pore-volume normalized surface area [Formula: see text] a proxy measure of clay content, in the homogeneous samples and poorly ([Formula: see text]rms error = 0.507) when the clustered samples are included in the fit. The SIP parameters indicate moderately good to excellent fits with [Formula: see text] for homogeneous samples ([Formula: see text]rms error = 0.0783–0.139) and moderately good to good fits for clustered samples ([Formula: see text]rms error = 0.140–0.336), and the coefficients describing the polarizability of the samples depend on the clay distribution. NMR parameters vary moderately well with [Formula: see text] in the homogeneous samples ([Formula: see text]rms error = 0.341–0.412) and poorly ([Formula: see text]rms error = 1.08–6.04) in the clustered samples. The SIP parameters vary moderately well with [Formula: see text] ([Formula: see text]rms error = 0.301–0.466); however, the relationship between the SIP parameters and [Formula: see text] is compromised by the nonnegligible polarization of the clay clusters. NMR parameters indicate good to excellent fits with [Formula: see text] ([Formula: see text]rms error = 0.0789–0.116). For SIP and NMR, fitting homogeneous and clustered samples together does not compromise the fit quality. These results suggest that the geophysical measurements are better predictors of [Formula: see text] in heterogeneous porous media than bulk measures of pore geometry such as [Formula: see text].
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