Spectral induced polarization of artificial sand–clay mixtures—an experimental study supported by microcomputed tomography

SUMMARY Evaluating sedimentary texture is crucial for managing of aquifers and assessing groundwater quality. Direct methods of aquifer characterization (like pumping tests) are of limited value because of their invasive character and connection to boreholes, which results in scarce data networks. Non-invasive geophysical methods complement the invasive methods. Among geophysical methods, the spectral induced polarization (SIP) method, which allows measuring frequency-dependent complex electrical impedance, is especially promising in this context, because it offers insights into the specific surface area (which is the proxy of the clay content), as well as into textural features of porous media (like pore-size or grain-size distributions). However, SIP data concerning sand-clay mixtures remain scarce. This lack of experimental data, in turn, hinders further development of the IP theory of clayey media. To fill this gap, we carried out a set of experiments to study SIP signatures of artificial sand–clay mixtures with varying clay content, clay types and pore water salinity. The data set includes nine mixtures with kaolinite or bentonite in various contents. We equilibrated each mixture with six NaCl solutions ranging in salinity from 0.1 to 30 g l−1. Based on these measurements, we first obtained the formation factor and the surface conductivity values of the samples. Then, we interpreted this data set in terms of the real and imaginary conductivity, the formation factor, the in-phase surface conductivity and the normalized total chargeability. Finally, based on the Debye decomposition approach, we converted the IP spectra into relaxation time distributions (RTDs) to analyse dominant relaxation times in comparison with microcomputed tomography (μCT) images. We show that the in-phase conductivity of the sand-bentonite mixtures strongly exceeds that of the sand-kaolinite mixtures with the same clay content. We attributed this difference to the higher surface conductivity of the bentonite clays. The quadrature conductivity exhibits a clear dependence on the clay type, its content and the conductivity of the pore water. Our observations reveal that both quadrature conductivity and normalized chargeability increase with kaolinite content. However, for the bentonite samples, these parameters show maxima rather than a gradual trend. We explained this behaviour when comparing RTDs with μCT images. This comparison allowed us to identify elements of texture (sand grains coated with a thin film made of clay, clay aggregates of different sizes, clay ‘bridges’ connecting two grains or multiple grains, etc.), which are responsible for IP of various intensities and different time constants. The size and morphology of these elements depend on the clay content, mineralogy, the clay phase topology and pore water salinity. Ultimately, the combined application of the SIP and μCT methods to a variety of sand-clay mixtures enabled us to differentiate the samples with different clay types, contents and water salinities. We believe that these petrophysical results can serve as the basis for SIP application to detect remotely different clay types and content, and to monitor the water salinity in clayey rocks, soils and sediments.