Direct geoelectrical evidence of mass transfer at the laboratory scale

Swanson, Ryan D. and Singha, Kamini and Day-Lewis, Frederick D. and Binley, Andrew and Keating, Kristina and Haggerty, Roy (2012) Direct geoelectrical evidence of mass transfer at the laboratory scale. Water Resources Research, 48 (10). ISSN 0043-1397

Abstract

Previous field-scale experimental data and numerical modeling suggest that the dual-domain mass transfer (DDMT) of electrolytic tracers has an observable geoelectrical signature. Here we present controlled laboratory experiments confirming the electrical signature of DDMT and demonstrate the use of time-lapse electrical measurements in conjunction with concentration measurements to estimate the parameters controlling DDMT, i.e., the mobile and immobile porosity and rate at which solute exchanges between mobile and immobile domains. We conducted column tracer tests on unconsolidated quartz sand and a material with a high secondary porosity: the zeolite clinoptilolite. During NaCl tracer tests we collected nearly colocated bulk direct-current electrical conductivity (sigma(b)) and fluid conductivity (sigma(f)) measurements. Our results for the zeolite show (1) extensive tailing and (2) a hysteretic relation between sigma(f) and sigma(b), thus providing evidence of mass transfer not observed within the quartz sand. To identify best-fit parameters and evaluate parameter sensitivity, we performed over 2700 simulations of sigma(f), varying the immobile and mobile domain and mass transfer rate. We emphasized the fit to late-time tailing by minimizing the Box-Cox power transformed root-mean square error between the observed and simulated sigma(f). Low-field proton nuclear magnetic resonance (NMR) measurements provide an independent quantification of the volumes of the mobile and immobile domains. The best-fit parameters based on sigma(f) match the NMR measurements of the immobile and mobile domain porosities and provide the first direct electrical evidence for DDMT. Our results underscore the potential of using electrical measurements for DDMT parameter inference.