Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods

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  • Adrian Barfod
  • Ingelise Møller, GEUS, Danmark
  • Anders Vest Christiansen
  • Anne-Sophie Høyer, GEUS
  • ,
  • Júlio Hoffimann, Stanford Center for Reservoir Forecasting (SCRF), Stanford University
  • ,
  • Julien Straubhaar, Centre d'hydrogéologie et de géothermie, Université de Neuchâtel, Schweiz
  • Jef Caers, Stanford Center for Reservoir Forecasting (SCRF), Stanford University
Creating increasingly realistic hydrological models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modelling procedure. Using Multiple Point Statistics (MPS) for stochastic hydrostratigraphic modelling provides a degree of flexibility that allows the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modelling. 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 45 km2. 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, a practical example for modelling new survey areas is presented. A cognitive hydrostratigraphic model of one area is used as Training Image to create a suite of stochastic hydrostratigraphic models in a new survey area. The advantage of stochastic modelling is that detailed multiple point information from one area can be easily transferred to another area considering uncertainty.

The presented MPS methods each have their own set of advantages and disadvantages. The DS method had average computation times of 6-7 h, which is large, compared to iqsim with average computation times of 10-12 min. However, iqsim generally did not properly constrain the near-surface part of the spatially dense soft data variable. The computation time of 2-3 h for snesim was in between DS and iqsim. The snesim implementation used here is part of the Stanford Geostatistical Modeling Software, or SGeMS. The snesim setup was not trivial, with numerous parameter settings, usage of multiple grids and a search tree database. However, once the parameters had been set it yielded comparable results to the other methods. Both, iqsim and DS, are easy to script and run in parallel on a server, which is not the case for the snesim implementation in SGeMS.
TidsskriftHydrology and Earth System Sciences
Sider (fra-til)3351-3373
StatusUdgivet - 18 jun. 2018

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