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Combining geostatistics and flow simulators to identify transmissivity
Institution:1. BRGM, Avenue Claude Guillemin, BP 6009, 45060 Orléans Cedex 2, France;2. Centre de Géostatistique, 35 rue St. Honoré, 77305 Fontainebleau, France;1. College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, PR China;2. Forestry and Fruit Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, PR China;1. Jan Dlugosz University in Czestochowa, Faculty of Science and Technology, Armii Krajowej 13/15, 42-200, Czestochowa, Poland;2. Czestochowa University of Technology, Department of Advanced Energy Technologies, Dabrowskiego 73, 42-201, Czestochowa, Poland;3. AGH University of Science and Technology, Faculty of Energy and Fuels, A. Mickiewicza 30, 30-059, Cracow, Poland;4. Niigata University, 2-8050 Ikarashi, Niigata, 950-2181, Japan;1. Vrije Universiteit Brussel (VUB), Department ELEC, Pleinlaan 2, 1050 Brussels, Belgium;2. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
Abstract:The reconstruction of the transmissivity field from the more numerous experimental hydraulic head data, an inverse problem, remains the focus of continuing stochastic-based research. The difficulty of this problem arises not only from the complexity of the diffusion equation that links the two variables, but also from taking into account the physical aspects of the site under study; e.g. the boundary conditions, the effective recharge, and the geology. In practical applications, the validity of purely analytical techniques proposed to date is limited by certain simplifying assumptions, like the linearization of the flow equation, made in order to obtain a solution. For this reason, a hybrid methodology combining geostatistical techniques with deterministic numerical flow simulators is proposed. This combination allows the numerical calculation of the direct and cross covariances needed to cokrige the transmissivity from both the transmissivity and hydraulic head data. The flexibility of numerical flow simulators takes away the need for the simplifying assumptions of analytical techniques to apply the proposed methodology.
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