Multiscale Characterization and Quantification of Arsenic Mobilization and Attenuation During Injection of Treated Coal Seam Gas Coproduced Water into Deep Aquifers. Issue 12 (26th December 2017)

• Record Type: Journal Article
• Authors: Rathi, Bhasker; Siade, Adam J.; Donn, Michael J.; Helm, Lauren; Morris, Ryan; Davis, James A.; Berg, Michael; Prommer, Henning
• Is Part Of: Water resources research. Volume 53:Issue 12(2017)
• Page Start: 10779
• Page End: 10801
• Publication Date: 2017-12-26
• Subjects: reactive transport -- arsenic -- reinjection -- surface complexation modeling -- uncertainty; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2017WR021240
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Coal seam gas production involves generation and management of large amounts of co‐produced water. One of the most suitable methods of management is injection into deep aquifers. Field injection trials may be used to support the predictions of anticipated hydrological and geochemical impacts of injection. The present work employs reactive transport modeling (RTM) for a comprehensive analysis of data collected from a trial where arsenic mobilization was observed. Arsenic sorption behavior was studied through laboratory experiments, accompanied by the development of a surface complexation model (SCM). A field‐scale RTM that incorporated the laboratory‐derived SCM was used to simulate the data collected during the field injection trial and then to predict the long‐term fate of arsenic. We propose a new practical procedure which integrates laboratory and field‐scale models using a Monte Carlo type uncertainty analysis and alleviates a significant proportion of the computational effort required for predictive uncertainty quantification. The results illustrate that both arsenic desorption under alkaline conditions and pyrite oxidation have likely contributed to the arsenic mobilization that was observed during the field trial. The predictive simulations show that arsenic concentrations would likely remain very low if the potential for pyrite oxidation is minimized through complete deoxygenation of the injectant. The proposed modeling and predictive uncertainty quantification method can be implemented for a wide range of groundwater studies that investigate the risks of metal(loid) or radionuclide contamination. Key Points: The study provides a methodology to assess the potential geochemical impacts of large‐scale water injection Reactive transport modeling was used to incorporate field and laboratory experimental data to understand arsenic mobilization processes A new Monte Carlo framework was developed to efficiently quantify the predictive uncertainty of the long‐term fate of arsenic