Parameterizability of processes in subsurface energy and mass storage: Supported by a review of processes, codes, parameters, and a regional example: Schleswig-Holstein, Germany

• Published in: Environmental earth sciences 2016-05, Vol.75 (10), p.1, Article 885
• Main Authors: Dethlefsen, Frank, Beyer, Christof, Feeser, Volker, Köber, Ralf
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Biogeosciences; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geology; Hydrology/Water Resources; Parameter estimation; Sensitivity analysis; Subsurface Energy Storage; Terrestrial Pollution; Thematic Issue; Thermal energy; Uncertainty; Underground storage
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-016-5626-1

The numerical simulation of scenarios is a promising approach when quantifying the potential hydraulic, thermal, geomechanical, and chemical effects of subsurface energy and mass storage. Particularly, the coupling of processes is a strong point in numerical simulations. This study defines the geoscientific parameter demand as well as the demand for process understanding for simulating subsurface energy and mass storage, describes the existing numerical codes to conduct the simulations, provides generally valid parameter values, and emphasizes on discussing parameters where only few values exist. In this context, it is exemplified that the parameterizability of the regarded processes is determined by an uncertainty in parameter values (variability or aleatory uncertainty) as well as in the understanding of processes (epistemic uncertainty) as it was suggested by Walker et al. (Integr Assess 4:5–17, 2003 ). The study categorizes the knowledge about parameter values and processes into these uncertainty groups and exemplarily evaluates the impacts of the uncertainties. Using this approach illustrates the concepts needed for calculating prediction errors of numerical scenario simulations, such as sensitivity analyses in the case of statistical data uncertainty and laboratory or field studies in the case of scenario uncertainties.