Universität Stuttgart
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Item Open Access Geostatistical methods for the identification of flow and transport parameters in the subsurface(2005) Nowak, Wolfgang; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)Per definition, log-conductivity fields estimated by geostatistical inversing do not resolve the full variability of heterogeneous aquifers. Therefore, in transport simulations, the dispersion of solute clouds is under-predicted. Macrotransport theory defines dispersion coefficients that parameterize the total magnitude of variability. Using these dispersion coefficients together with estimated conductivity fields would over-predict dispersion, since estimated conductivity fields already resolve some of the variability. Up to presence, only a few methods exist that allow to use estimated conductivity fields for transport simulations. A review of these methods reveals that they are either associated with excessive computational costs, only cover special cases, or are merely approximate. Their predictions hold only in a stochastic sense and cannot take into account measurements of transport-related quantities in an explicit manner. In this dissertation, I successfully develop, implement and apply a new method for geostatistical identification of flow and transport parameters in the subsurface. The parameters featured here are the log-conductivity and a scalar log-dispersion coefficient. The extension to other parameters like retardation coefficients or reaction rates is straightforward. Geostatistical identification of flow parameters is well-known. However, simultaneous identification together with transport parameters is new. In order to implement the new method, I develop a modified Levenberg-Marquardt algorithm for the Quasi-Linear Geostatistical Approach and extend the latter to the generalized case of uncertain prior knowledge. I derive the sensitivities of the state variables of interest with respect to the newly introduced scalar log-dispersion coefficient. Further, I summarize and extend the list of spectral methods that help to drastically speed up the expensive matrix operations involved in geostatistical inverse modeling. If the quality and quantity of input data is sufficient, the new method accurately simulates the dispersive mechanisms of spreading, dilution and the irregular movement of the center of mass of a plume. Therefore, it adequately predicts mixing of solute clouds and effective reaction rates in heterogeneous media. I perform extensive series of test cases in order to discuss and prove certain properties of the new method and the new dispersion coefficient. The character and magnitude of the identified dispersion coefficient depends strongly on the quality and quantity of input data and their potential to resolve variability in the conductivity field. Because inverse models of transport are coupled to inverse models of flow, the information in the input data has to sufficiently characterize the flow field. Otherwise, transport-related input data cannot be interpreted. Application to an experimental data set from a large-scale sandbox experiment and comparison to results from existing approaches in macrotransport theory show good agreement.