Imaging subsurface migration of dissolved CO2 in a shallow aquifer using 3-D time-lapse electrical resistivity tomography
Abstract
Contamination of groundwater by leaking CO2 is a potential risk of carbon sequestration. With the help of a field experiment in western Denmark, we investigate to what extent surface electrical resistivity tomography (ERT) can detect and image dissolved CO2 in a shallow aquifer. For this purpose, we injected CO2 at a depth of 5 and 10m and monitored its migration using 320 electrodes on a 126m×25m surface grid. A fully automated acquisition system continuously collected data and uploaded it into an online database. The large amount of data allows for time-series analysis using geostatistical techniques for noise estimation and data interpolation to compensate for intermittent instrument failure. We estimate a time-dependent noise level for each ERT configuration, taking data variation and measurement frequency into account.A baseline inversion reveals the geology at the site consisting of aeolian and glacial sands near the surface and marine sands below 10m depth. 3-D time-lapse ERT inversions clearly image the dissolved CO2 plume with decreased electrical resistivity values. We can image the geochemical changes induced by the dissolved CO2 until the end of the acquisition, 120days after the injection start. During these 120days, the CO2 migrates about 25m in the expected groundwater flow direction. Water electrical conductivity (EC) sampling using small screens in 29 wells allows for very good verification of the ERT results. Water EC and ERT results generally agree very well, with the water sampling showing some fine-scale variations that cannot be resolved by the ERT. The ERT images have their strength in outlining the plume's shape in three dimensions and in being able to image the plume outside the well field. These results highlight the potential for imaging dissolved CO2 using non-intrusive surface electrical resistivity tomography. © 2013 Elsevier B.V.
Info
Journal Article, 2014
UN SDG Classification
DK Main Research Area
Science/Technology
