Building Flow and Transport Models with Electrical Resistivity Tomography Data

Wednesday, 26 July 2017: 11:15 AM
Paul Brest West (Munger Conference Center)
Ian Gottschalk1, Thomas Hermans2, Rosemary J Knight1, Jef Caers1, David Alexander Cameron1, Julia Regnery3 and John E McCray3, (1)Stanford University, Stanford, CA, United States, (2)Université de Liège, Liege, Belgium, (3)Colorado School of Mines, Civil and Environmental Engineering, Golden, CO, United States
Aquifer recharge and recovery (ARR) is the process of enhancing natural groundwater recharge and recovering water for later use by constructing engineered conveyances. Insufficient understanding of lithological heterogeneity at ARR sites often hinders attempts to predict where and how quickly infiltrating water will flow in the subsurface, which can adversely affect the quality and quantity of available water at the ARR site. In this study, we explored the use of electrical resistivity tomography (ERT) to assist in characterizing lithological heterogeneity at an ARR site, so as to incorporate the geophysical data into a flow and transport model. Ideally, ERT data are collocated with physical borehole lithology measurements, so as to determine the petrophysical relationship between the measured electrical resistivity and lithology. At the studied ARR site however, as is often the case in practice, non-collocated borehole lithology data and ERT data complicate the petrophysical transformation. We developed a framework for integrating non-collocated ERT data into stochastic lithology simulations by two separate methods: 1) a spatial bootstrapping method, and 2) a maximum likelihood estimation to fit lognormal distributions to ERT histograms. We simulated equiprobable lithological models at the ARR site, using each of the two described methods. By comparing measured breakthrough times of tracer tests at the ARR site to groundwater flow simulation results using simulated lithology models, we created an ensemble of simulated flow models which are compatible with our data, while reflecting the depositional environment of the field site. We assessed the efficacy of the proposed petrophysical transforms, as well as the usefulness of the ERT data, in characterizing lithological and hydrogeological heterogeneities in the subsurface. This framework can provide the basis for the integration of geophysical data with other forms of data for a variety of Critical Zone applications.