Reactive transport modeling of redox processes to assess Fe(OH)^sub 3^ precipitation around aquifer thermal energy storage wells in phreatic aquifers

• Published in: Environmental earth sciences 2016-04, Vol.75 (8), p.1
• Main Authors: Possemiers, Mathias, Huysmans, Marijke, Anibas, Christian, Batelaan, Okke, Van Steenwinkel, Jos
• Format: Article
• Language: English
• Subjects: Aquifers; Energy storage; Feasibility studies; Hydroxides; Iron; Metal oxides; Precipitation; Temperature effects; Thermal energy; Water depth; Water quality assessments; Water wells
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-016-5398-7

Well clogging due to iron (hydr)oxide precipitation can negatively influence the performance, or even cause failure, of aquifer thermal energy storage (ATES) wells in aquifers with varying redox conditions. The interactions between physical and chemical processes during ATES operation are, however, not well understood. The reactive transport modeling code PHT3D is used to assess the effects of alternating pumping by ATES systems near the redox boundary on the precipitation of iron hydroxides for two cases, Leuven and Antwerp in the north of Belgium. Results show in both investigated cases that initial mixing plays an important role in the development of Fe(OH)^sub 3^ precipitation around the wells, with the highest concentration of Fe(OH)^sub 3^ around the cold well. The initial injection into the warm well causes both the initial mixing and temperature effects to counteract each other, so that the Fe(OH)^sub 3^ concentration at the cold well is lower and closer to those of the warm well. Since the temperature dependence of the reaction rate of Fe(OH)^sup +^, Fe(OH)^sub 2^ and other reactive species is not taken into account, the impact of the temperature effect on iron (hydr)oxide precipitation should not be viewed quantitatively. However, avoiding the mixing of oxygen/nitrate rich water with iron rich water remains the best strategy to prevent well clogging. Feasibility studies for ATES should therefore assess water quality variations with depth, and use this information to optimize filter screen settings. In this way, the well screen setting can be optimized and the risk of well clogging due to iron (hydr)oxide precipitation is reduced.