Clogging development

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Assessment of clogging processes with regard to site-specific and operational parameters.

One of the major challenges for the sustainable management of MAR facilities is clogging. Clogging leads to a reduction of soil hydraulic conductivity and hence a loss in performance. Every MAR scheme will unavoidably experience clogging to some extent during its operational life. Therefore the recognition of the clogging potential and appropriate application of mitigation and remediation measures through engineering design or operational management is required (Martin, 2013).

Comparison of normalized soil hydraulic conductivities (Kt/Kstart) of the upper soil layer determined using tracer breakthrough curves (K experiment) and calibrated with the help of the water level measurements in the infiltration basin (K simulation, hydraulic conductivity reduction [λ] = 1.9 × 10−5 min−1) (Glass et al., 2020)
In infiltration basins, the water quality of the recharge water and the soil hydraulic conductivity influence the extent of the resulting clogging rate which occurs mainly at the infiltration surface. In recharge boreholes, clogging occurs mainly at the well-aquifer-interface. Four main clogging types can be distinguished: physical, chemical, biological and mechanical clogging (Martin, 2013). The following table gives an overview of the clogging types and the involved processes (Martin, 2013):

Clogging type Clogging process
Chemical –          Geochemical reactions causing precipitation of minerals
–          Aquifer matrix dissolution (can also increase hydraulic conductivity)
–          Ion exchange
–          Ion adsorption
–          Oxygen reduction
–          Formation of insoluble scales
–          Formation dissolution
Physical –          Accumulation of suspended solids
–          Flow velocity induced damage
–          Clay swelling
–          Clay deflocculation
–          Invasion of drilling fluids (emulsifiers( deep into formation
–          temperature
Mechanical –          entrained air/gas binding
–          hydraulic loading causing formation, aquitard or casing failures
Biological –          algae growth and accumulation of biological flocs
–          microbiological production of polysaccharides
–          bacterial entrainment and growth

A few studies tried to estimate the occurring clogging rate in injection wells. Besides the dissolution of minerals also their precipitation can affect the performance of an artificial recharge system as it may cause chemical clogging (Anderson et al., 2006; Page et al., 2014). Gutiérrez-Ojeda et al., 2007 identified precipitating calcite as the major cause for the permeability reduction around an injection site in Mexico. The precipitation of Fe-oxides or hydroxides can also contribute to chemical clogging as calculated by applying PHREEQC in Australia (Vanderzalm et al., 2013). The numerical code CLOG can assess different aspects of clogging taking into account the accumulation of suspended sediments, bacterial growth, chemical reactions and the generation of gas as well as compaction (Pérez-Patricio, 2001). CLOG was applied to a field and various laboratory experiments demonstrating that the main spatial and temporal trends in porosity reduction caused by clogging can be reproduced (Pérez-Patricio, 2001). Masciopinto, 2013 applied a simple physical clogging model to predict the time span for clogging of fractures in Lebanon. Furthermore, FEFLOW was used to evaluate transmissivity changes caused by clogging (Dillon et al., 2010; Rinck-Pfeiffer et al., 2013).

Although clogging of surface water spreading facilities is a major concern, modeling of issues related to clogging has been limited so far (Hutchinson et al., 2013). With the help of EASY-LEACHER, an analytical two-dimensional reactive transport spreadsheet model, accumulation rates and chemical composition of the sludge layer can be predicted, thus helping to minimize clogging in relation to basin recharge (Stuyfzand, 2002). A simple mathematical model was developed by Phipps et al., 2007 to predict the reduction of percolation rates in infiltration basins over time. The analytical transport code CXTFIT in combination with MODFLOW-MT3DMS also helped to determine clogging of infiltration basins by simulating tracer experiments of a technical scale experimental field (Grützmacher et al., 2006). Wett, 2006 examined the clogging of a riverbank filtration system in Austria by simulating tracer tests using MODFLOW and MT3D. The hydraulic conductivity decreased by one order of magnitude through clogging resulting in a halving of infiltration rate during one year of operation (Wett, 2006). The variably saturated water flow model HYDRUS-2D was modified by Glass et al, 2020, to include time-variable hydraulic conductivities using a scaling factor to more realistically represent clogging. Although the processes that lead to clogging were not integrated in the approach, it can help to evaluate resulting infiltration capacities numerically and optimize the operation and design of MAR facilities.

The following INOWAS tools can be used to predict and evaluate the development of clogging during MAR:

  • T12. Clogging estimation by MFI-Index (tool unavailable)

REFERENCES

  • Anderson, M., Dewhurst, R., Jones, M., Baxter, K., 2006. Characterisation of turbidity and well clogging processes in a double porosity Chalk aquifer during the South London Artificial Recharge Scheme trials, in: UNESCO (Ed.), Recharge Systems for Protecting and Enhancing Groundwater Resources – Proceedings of the 5th International Symposium on Management of Aquifer Recharge ISMAR 5, Berlin, Germany, 11–16 June 2005. pp. 593–598.
  • Dillon, P.J., Pavelic, P., Page, D., Miotlinski, K., Levett, K., Barry, K., Taylor, R., Wakelin, S., Vanderzalm, J.L., Molloy, R., Parsons, S., Dudding, M., Goode, A., 2010. Developing Aquifer Storage and Recovery (ASR) Opportunities in Melbourne – Rossdale ASR demonstration project final report.
  • Glass, J., Šimůnek, J., Stefan, C., 2020. Scaling factors in HYDRUS to simulate a reduction in hydraulic conductivity during infiltration from recharge wells and infiltration basins. Vadose zone j. 19. https://doi.org/10.1002/vzj2.20027
  • Grützmacher, G., Bartel, H., Wiese, B., 2006. Simulating bank filtration and artificial recharge on a technical scale, in: UNESCO (Ed.), Recharge Systems for Protecting and Enhancing Groundwater Resources – Proceedings of the 5th International Symposium on Management of Aquifer Recharge ISMAR5, Berlin, Germany, 11–16 June 2005. pp. 498–503.
  • Gutiérrez-Ojeda, C., Martínez-Morales, M., Ortiz-Flores, G., 2007. Artificial recharge of groundwater in a coal mining area of northeast Mexico, in: Fox, P. (Ed.), Management of Aquifer Recharge for Sustainability: Proceedings of the 6th International Symposium on Managed Artificial Recharge of Groundwater, ISMAR6, Phoenix, Arizona USA October 28 – November 2, 2007. Acacia Publishing Incorporated, pp. 74–83.
  • Hutchinson, A.S., Milczarek, M., Banerjee, 2013. Clogging Phenomena Related to Surface Water Recharge Facilities, in: Martin, R. (Ed.), Clogging Issues Associated with Managed Aquifer Recharge Methods. IAH Commission on Managing Aquifer Recharge, Australia, pp. 95–106.
  • Martin, R. (Ed.), 2013. Clogging issues associated with managed aquifer recharge methods. IAH Commission on Managing Aquifer Recharge, Australia.
  • Masciopinto, C., 2013. Management of aquifer recharge in Lebanon by removing seawater intrusion from coastal aquifers. Journal of Environmental Management 130, 306–312. https://doi.org/10.1016/j.jenvman.2013.08.021
  • Page, D., Vanderzalm, J., Miotliński, K., Barry, K., Dillon, P., Lawrie, K., Brodie, R.S., 2014. Determining treatment requirements for turbid river water to avoid clogging of aquifer storage and recovery wells in siliceous alluvium. Water Research 66, 99–110. https://doi.org/10.1016/j.watres.2014.08.018
  • Pérez-Patricio, A., 2001. Integrated Modeling of Clogging Processes in Artificial Groundwater Recharge (Dissertation). Technical University of Catalonia, Barcelona.
  • Phipps, D.W., Lyon, S., Hutchinson, A.S., 2007. Development of a percolation decay model to guide future optimization of surface water recharge basins, in: Fox, P. (Ed.), Management of Aquifer Recharge for Sustainability: Proceedings of the 6th International Symposium on Managed Artificial Recharge of Groundwater, ISMAR6, Phoenix, Arizona USA October 28 – November 2, 2007. Acacia Publishing Incorporated, pp. 433–446.
  • Rinck-Pfeiffer, S., Dillon, P., Ragusa, S., Hutson, J., Fallowfield, H., de Marsily, G., Pavelic, P., 2013. Reclaimed Water for Aquifer Storage and Recovery: A Column Study of Well Clogging, in: Martin, R. (Ed.), Clogging Issues Associated with Managed Aquifer Recharge Methods. IAH Commission on Managing Aquifer Recharge, Australia, pp. 26–33.
  • Stuyfzand, P.J., 2002. Modelling the accumulation rate and chemical composition of clogging sludge layers in recharge basins with Easy-Leacher 4.6, in: Dillon, P. (Ed.), Management of Aquifer Recharge for Sustainability: Proceedings of the 4th International Symposium on Artificial Recharge of Groundwater. ISAR-4, Adelaide, South Australia, 22-26 September 2002. A.A. Balkema, Lisse, pp. 221–224.
  • Vanderzalm, J., Smitt, C., Barry, K., Dillon, P., Davidge, S., Gornall, D., Seear, H., Ife, D., 2013. Potential for Injection Well Clogging in an Anoxic Sandstone Aquifer Receiving Gresh, Deoxygenated but Chlorinated Injectant, in: Martin, R. (Ed.), Clogging Issues Associated with Managed Aquifer Recharge Methods. IAH Commission on Managing Aquifer Recharge, Australia, pp. 34–49.
  • Wett, B., 2006. Monitoring Clogging of a RBF-System at the river Enns, Austria, in: Hubbs, S. (Ed.), Riverbank Filtration Hydrology, Nato Science Series: IV: Earth and Environmental Sciences. Springer Netherlands, pp. 155–177.