Monitoring High Velocity Salt Tracer via 4D Electrical Resistivity Tomography – Possibility for Salt Tracer Tomography

Abstract:

Hydrogeological testing in a tomographic sequence as shown by the use of hydraulic tomography, allows an improvement of the spatial resolution of subsurface parameters. In this regard, recent studies show increasing interest in tracer tomography which involves sequential and spatially separated tracer injections and the measurement of their corresponding tracer breakthrough at different locations and depths. Such concentration measurements however require large experimental efforts and can be simplified by geophysical tracer monitoring techniques such as electrical resistivity. In this study, we present the use of 4-D, cross-hole electrical resistivity tomography (ERT) for monitoring salt tracer experiments in high velocity flow fields. For our study, we utilized a set up that enables the conduction of salt tracer experiments with complete recovery within 84 hours over a transport distance of 16 m. This allows the repetition of the experiments with different injection depths for a tomographic salt tracer testing. For ERT monitoring, we designed modular borehole electrodes for repeated usage in a flexible manner. We also assess the use of a high speed resistivity data acquisition mode for field scale tracer monitoring ensuring high spatial and temporal resolution without sacrificing data accuracy. We applied our approach at the Lauswiesen test site, Tübingen, Germany. In our 10 m × 10 m tracer monitoring domain with 16 borehole electrodes, we acquired 4650 data points in less than 18 minutes for each monitoring cycle. Inversion results show that the tracer could be successfully imaged using this approach. The results show that repeated salt tracer tests can be efficiently monitored at a high resolution with ERT which gives the possibility for salt tracer tomography at field scale. Our results also provide a data base for extending current hydrogeophysical inversion approaches to field scale data.