Upscaling and Inverse Modeling of Groundwater Flow and Mass ...

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104 BIBLIOGRAPHY Tran, T., 1996. The ‘missing scale’ and direct simulation of block effective properties. Journal of Hydrology 183 (1-2), 37–56. Tran, T., Wen, X., Behrens, R., 1999. Efficient conditioning of 3D finescale reservoir model to multiphase production data using streamline-based coarse-scale inversion and geostatistical downscaling. In: SPE Annual Technical Conference and 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 of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique. Journal of Hydrology 255 (1-4), 151–168. Wen, X. H., Gómez-Hernández, J. J., 1996. Upscaling hydraulic conductivities: An overview. J. of Hydrology 183 (1-2), ix–xxxii. White, C. D., Horne, R. N., 1987. Computing absolute transmissibility in the presence of Fine-Scale heterogeneity. SPE 16011. Yeh, W., 1986. Review of 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 handling Non-Gaussianity of parameters and 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 of 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 of Hydrology. 5 Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter Abstract Real-time data from on-line sensors offer 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 and control of 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 and transport parameters (hydraulic conductivity and porosity) and state variables (piezometric head and concentration) of a groundwater flow and contaminant transport problem. A synthetic experiment is used to demonstrate the capability of the EnKF to estimate hydraulic conductivity and porosity by assimilating dynamic head and multiple concentration data in a transient flow and transport model. In this work the worth of hydraulic conductivity, porosity, piezometric head, and 105
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Upscaling and Inverse Modeling of G
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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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CHAPTER 1. INTRODUCTION 3 Chapter 2
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6 CHAPTER 2. A COMPARATIVE STUDY OF
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8 CHAPTER 2. A COMPARATIVE STUDY OF
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10 CHAPTER 2. A COMPARATIVE STUDY O
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38 BIBLIOGRAPHY Berkowitz, B., Sche
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40 BIBLIOGRAPHY Gómez-Hernández,
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42 BIBLIOGRAPHY Salamon, P., Fernà
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44 CHAPTER 3. TRANSPORT UPSCALING U
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154 CHAPTER 6. GROUNDWATER FLOW INV
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170 BIBLIOGRAPHY Deutsch, C. V., Jo
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172 BIBLIOGRAPHY Lee, S., Carle, S.
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174 BIBLIOGRAPHY Yeh, W., 1986. Rev
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176 CHAPTER 7. CONCLUSIONS travels
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178 CHAPTER 7. CONCLUSIONS predicti
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180 APPENDIX A. FLOWXYZ3D - A THREE
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182 APPENDIX A. FLOWXYZ3D - A THREE
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