104 BIBLIOGRAPHY Tran, T., 1996. The ‘missing scale’ <strong>and</strong> direct simulation <strong>of</strong> block effective properties. Journal <strong>of</strong> Hydrology 183 (1-2), 37–56. Tran, T., Wen, X., Behrens, R., 1999. Efficient conditioning <strong>of</strong> 3D finescale reservoir model to multiphase production data using streamline-based coarse-scale inversion <strong>and</strong> geostatistical downscaling. In: SPE Annual Technical Conference <strong>and</strong> Exhibition. Tureyen, O., Caers, J., 2005. A parallel, multiscale approach to reservoir modeling. Computational Geosciences 9 (2), 75–98. Wen, X. H., Deutsch, C., Cullick, A., 2002. Construction <strong>of</strong> geostatistical aquifer models integrating dynamic flow <strong>and</strong> tracer data using inverse technique. Journal <strong>of</strong> Hydrology 255 (1-4), 151–168. Wen, X. H., Gómez-Hernández, J. J., 1996. <strong>Upscaling</strong> hydraulic conductivities: An overview. J. <strong>of</strong> Hydrology 183 (1-2), ix–xxxii. White, C. D., Horne, R. N., 1987. Computing absolute transmissibility in the presence <strong>of</strong> Fine-Scale heterogeneity. SPE 16011. Yeh, W., 1986. Review <strong>of</strong> parameter identification procedures in groundwater hydrology: The inverse problem. Water Resources Research 22 (2), 95–108. Zhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H.-J., Li, L., 2011. An approach to h<strong>and</strong>ling Non-Gaussianity <strong>of</strong> parameters <strong>and</strong> state variables in ensemble Kalman filter. Advances in Water Resources, in press, doi:10.1016/j.advwatres.2011.04.014. Zhou, H., Li, L., Gómez-Hernández, J. J., 2010. Three-dimensional hydraulic conductivity upscaling in groundwater modelling. Computers & Geosciences 36 (10), 1224–1235. Zimmerman, D., De Marsily, G., Gotway, C., Marietta, M., Axness, C., Beauheim, R., Bras, R., Carrera, J., Dagan, G., Davies, P., et al., 1998. A comparison <strong>of</strong> seven geostatistically based inverse approaches to estimate transmissivities for modeling advective transport by groundwater flow. Water Resources Research 34 (6), 1373–1413.
Submitted to Journal <strong>of</strong> Hydrology. 5 Jointly Mapping Hydraulic Conductivity <strong>and</strong> Porosity by Assimilating Concentration Data via Ensemble Kalman Filter Abstract Real-time data from on-line sensors <strong>of</strong>fer the possibility to update environmental simulation models in real-time. Information from on-line sensors concerning contaminant concentrations in groundwater allow for the real-time characterization <strong>and</strong> control <strong>of</strong> a contaminant plume. In this paper it is proposed to use the CPU-efficient Ensemble Kalman Filter (EnKF) method, a data assimilation algorithm, for jointly updating the flow <strong>and</strong> transport parameters (hydraulic conductivity <strong>and</strong> porosity) <strong>and</strong> state variables (piezometric head <strong>and</strong> concentration) <strong>of</strong> a groundwater flow <strong>and</strong> contaminant transport problem. A synthetic experiment is used to demonstrate the capability <strong>of</strong> the EnKF to estimate hydraulic conductivity <strong>and</strong> porosity by assimilating dynamic head <strong>and</strong> multiple concentration data in a transient flow <strong>and</strong> transport model. In this work the worth <strong>of</strong> hydraulic conductivity, porosity, piezometric head, <strong>and</strong> 105
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c⃝ Copyright by Liangping Li 2011
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Abstract The need to reduce the com
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improved as more data is assimilate
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Resumen La necesidad de reducir el
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Por último, en el tercer bloque, e
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Resum La necessitat de reduir el co
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flux i el transport (conductivitat
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Acknowledgements I want to thank my
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xx CONTENTS 3 Transport Upscaling U
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xxii CONTENTS
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xxiv LIST OF FIGURES 2.7 Flow compa
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xxvi LIST OF FIGURES 4.4 Reference
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xxviii LIST OF FIGURES
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1.1 Motivation and Objectives 1 Int
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172 BIBLIOGRAPHY Lee, S., Carle, S.
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174 BIBLIOGRAPHY Yeh, W., 1986. Rev
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