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Challenges in conditioning a stochastic geological model to a comprehensive soft dataset.

Challenges in conditioning a stochastic geological model to a comprehensive soft dataset. Julian Koch, Xin He, Karsten Høgh Jensen, Jens Christian Refsgaard. Outline. Motivation of the study Study site and data Methodologies Results – Geological Modelling Results – Hydrological Modeling.

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Challenges in conditioning a stochastic geological model to a comprehensive soft dataset.

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  1. Challenges in conditioning a stochastic geological model to a comprehensive soft dataset. Julian Koch, Xin He, Karsten Høgh Jensen, Jens Christian Refsgaard

  2. Outline • Motivation of the study • Study site and data • Methodologies • Results – Geological Modelling • Results – Hydrological Modeling

  3. NiCA: Nitrate reduction in geologically heterogeneous catchments. • Identifying vulnerable or robust nitrate leaching areas • Addressing the geological structure uncertainty

  4. Study site and data A delineated glacial structure in the Norsminde catchment Study site and data A delineated glacial structure in the Norsminde catchment • 112 boreholes • Airborne based geophysical survey (SkyTEM)

  5. SkyTEM (1) Clay: high conductance (elec.)  low resistivity (Ω) Sand: low conductance (elec.)  high resistivity (Ω) Higher sand probability Higher clay probability Cyril Schamper, 2012

  6. SkyTEM (2) • Histogram probability matching method • Pair: Categorical data from the boreholes with the resistivity values. • Group: Resistivity data in bins of 10 Ωm. • Fitt: Curve fitting for reading sand probability. • 46Ωm cut off value (50% sand probability) • SkyTEM data as soft conditioning data

  7. T-ProGS A transition probability/Markov model approach that simulates realizations by performing a sequential indicator simulation (SIS) and simulated quenching Spatial statistics of sand: Proportion: 23% & Mean length: 5m / 500m

  8. 3D Simulation with T-ProGS

  9. 3D Simulation with T-ProGS T-ProGS SIM P(Sand) SkyTEM MEAS P(Sand)

  10. Overconditioning SkyTEM MEAS P(Sand) T-ProGS 20m data SIM P(Sand) T-ProGS 200m data SIM P(Sand)

  11. Validation Facies Probability Distribution

  12. Simulated river discharge location St. 270003 St. 270002 Xin He, 2013

  13. Simulated river discharge calibration Uncalibrated Calibrated Xin He, 2013

  14. Conclusion (1) Hydrological Modeling • Discharge flow simulations: 1. small sample size; 2. geological uncertainty not the dominating factor; 3. model is insensitive. • Model calibration: 1. can account for the systematic bias; 2. increases the variation between realizations; 3. poor recession flow.

  15. Conclusion (2) Geological Modeling • Geophysical data (SkyTEM) can significantly improve the understanding of the subsurface heterogeneity • Interpreting the SkyTEM data with a probabilistic approach can account for uncertainties • Stochastic modeling can incorporate and honor soft data in the modeling process • A set of stochastically generated realizations represents the geological structure uncertainty • Validation is necessary

  16. Thank you for your attention!

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