Osterman, Gordon and Sugand, Madhuri and Keating, Kristina and Binley, Andrew and Slater, Lee (2019) Effect of clay content and distribution on hydraulic and geophysical properties of synthetic sand-clay mixtures. Geophysics, 84 (4). E239-E253. ISSN 0016-8033
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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 K 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 K varies moderately well (normalized root-mean-square error [Nrms error] = 0.393) with the pore-volume normalized surface area S-por a proxy measure of clay content, in the homogeneous samples and poorly (Nrms error = 0.507) when the clustered samples are included in the fit. The SIP parameters indicate moderately good to excellent fits with S-por for homogeneous samples (Nrms error = 0.0783-0.139) and moderately good to good fits for clustered samples (Nrms 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 S-por in the homogeneous samples (Nrms error = 0.341-0.412) and poorly (Nrms error = 1.08-6.04) in the clustered samples. The SIP parameters vary moderately well with K (Nrms error = 0.301-0.466); however, the relationship between the SIP parameters and K is compromised by the nonnegligible polarization of the clay clusters. NMR parameters indicate good to excellent fits with K (Nrms 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 K in heterogeneous porous media than bulk measures of pore geometry such as S-por.