Introduction Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO2 emissions into the atmosphere. The assessment of the risks related to CO2 storage is an important task. Events such as CO2 leakage and brine displacement and infiltration could result in hazards for human health and the environment and therefore have to be investigated in detail. In this work numerical simulations are performed to estimate the risk related to the displacement of brine. The injected CO2 will displace the brine that is initially present in the saline aquifer. The brine can be displaced over large areas and can reach shallower groundwater resources. High salt concentrations could lead to a degradation of groundwater quality. For water suppliers the most important information is whether and how much salt is produced at a water production well. In this approach the salt concentrations at water production wells depending on different parameters are determined for the assumption of a 2D model domain accounting for groundwater flow. Recognized ignorance resulting from grid resolution is qualitatively studied and statistical uncertainty is investigated for three parameters: the well distance, the water production rate, and the permeability of the aquifer. One possible way of estimating statistical uncertainties and providing probabilities is performing numerical Monte Carlo (MC) simulations. The MC approach is computationally very demanding because many simulations runs are needed to get an appropriate statistical accuracy. A possible way to handle the complexity and uncertainties with acceptable computational costs is by running MC simulations with a reduced model using a model reduction technique called arbitrary polynomial chaos expansion (aPC) . The aPC is applied in this work to provide probabilities and risk values for salt concentrations at the water production well. Mixing in the aquifer has a key influence on the salt concentration at the well. Dispersion and diffusion are the relevant processes for mixing. Depending on the applied grid the numerical dispersion strongly influence the results as well. The distance of the well is a key parameter that influences the salt concentration at the well, thus the time that the salt has for mixing until reaching the well is relevant. References  Oladyshkin, S. und W. Nowak: Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering & System Safety 106 (2012) 179–190.
Xx. International Conference on Computational Methods in Water Resources: Book of Abstracts and List of Participants, 2014
Salt infiltration; CO2 storage; Uncertainty
Main Research Area:
20th International Conference on Computational Methods in Water Resources, 2014