Creating increasingly realistic groundwater models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modeling procedure. Using multiple-point statistics (MPS) for stochastic hydrostratigraphic modeling provides a degree of flexibility that allows the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modeling. This paper focuses on comparing three MPS methods: snesim, DS and iqsim. The MPS methods are tested and compared on a real-world hydrogeophysical survey from Kasted in Denmark, which covers an area of 45ĝ€km2. A controlled test environment, similar to a synthetic test case, is constructed from the Kasted survey and is used to compare the modeling results of the three aforementioned MPS methods. The comparison of the stochastic hydrostratigraphic MPS models is carried out in an elaborate scheme of visual inspection, mathematical similarity and consistency with boreholes. Using the Kasted survey data, an example for modeling new survey areas is presented. A cognitive hydrostratigraphic model of one area is used as a training image (TI) to create a suite of stochastic hydrostratigraphic models in a new survey area. The advantage of stochastic modeling is that detailed multiple point information from one area can be easily transferred to another area considering uncertainty.
Barfod, A.A.S., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark, Hydrogeophysics Group, Department of Geoscience, Aarhus University, C.F. Møllers Allé 4, Aarhus C, 8000, Denmark; Møller, I., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark; Christiansen, A.V., Hydrogeophysics Group, Department of Geoscience, Aarhus University, C.F. Møllers Allé 4, Aarhus C, 8000, Denmark; Hoyer, A.-S., Department of Groundwater and Quaternary Geology Mapping, Geological Survey of Denmark and Greenland (GEUS), C.F. Møllers Allé 8, Aarhus C, 8000, Denmark; Hoffimann, J., Stanford Center for Reservoir Forecasting, School of Earth, Energy and Environmental Sciences, Stanford University, Green Earth Sciences, 367 Panama St, Stanford, CA 94305, United States; Straubhaar, J., Centre d'Hydrogéologie et de Géothermie (CHYN), Université de Neuchâtel, Switzerland; Caers, J., Stanford Center for Reservoir Forecasting, School of Earth, Energy and Environmental Sciences, Stanford University, Green Earth Sciences, 367 Panama St, Stanford, CA 94305, United States
Recommended Citation:
Barfod A.A.S.,Møller I.,Christiansen A.V.,et al. Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods[J]. Hydrology and Earth System Sciences,2018-01-01,22(6)