Understanding and Quantifying Controls of Arsenic Mobility during Deepwell Re-injection of CSG Waters

Wednesday, 17 December 2014: 11:35 AM
Bhasker Rathi1,2, Henning Prommer1,2, Michael Donn2, James A Davis3, Adam J Siade1 and Michael Berg4, (1)University of Western Australia, School of Earth and Environment, Crawley, WA, Australia, (2)CSIRO Land and Water, Perth, Australia, (3)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (4)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Water Resources and Drinking Water, Duebendorf, Switzerland
In Australia, the injection of reverse-osmosis treated production water from coal seams into the surrounding, deep aquifers may provide the most viable method to dispose of large quantities of production water. The geochemical disequilibrium between the injectant water composition and the target aquifer can potentially drive a range of water-sediment interactions that must be clearly understood and quantified in order to anticipate and manage future water quality changes at both the local and regional scale.

In this study, we use a multi-scale geochemical characterisation of a proposed reinjection site in combination with geochemical/reactive transport modeling to understand and predict the long-term fate of arsenic; and explore means for suitably mitigating an undesired increase of naturally occurring arsenic concentrations. We use a series of arsenic sorption experiments with the aquifer material from an injection trial site in Queensland, Australia to quantify As sorption/desorption from mineral surfaces in response to changes in site-specific geochemical conditions.

Batch experiments with arsenite were performed under anoxic conditions to replicate the highly reducing in-situ conditions. The results showed significant arsenic mobility at pH >8. Competitive sorption effects with phosphate and the impact of varying temperatures were also tested in batch mode. A site-specific general composite (GC) surface complexation model (SCM) was derived through inverse geochemical modeling, i.e., selection of appropriate surface complexation reactions and optimization of sorption constants. The SCM was subsequently tested and further improved during the interpretation of data from column flow-through experiments and from a field injection trial. Eventually the uncertainty associated with estimates of sorption constants was addressed and the effects of this uncertainty on field-scale model predictions were analyzed.