3D stochastic modelling of litho-facies in The Netherlands Jan L. Gunnink, Jan Stafleu, Freek S. Busschers, Denise Maljers TNO Geological Survey of the Netherlands Contributions of: Armin Menkovic, Tamara van de Ven, Jan Peeters, Marc Hijma, Kim Cohen, Wim Dubelaar, Ronald Harting, Laura Vonhögen & Jeroen Schokker
From small-scale to large-scale models Country-wide model: Geological Formations Serves as Framework model Regional model: more detail for applied use: e.g. Geohydrological modeling (REGIS II) Detailed model (uppermost 30-50 meter) Spatial planning Scenario calculations
Modeling with geological expertise Drilling Fault 50 km Depositional extent Supporting Data(surface) Supporting Data(extent) Supporting Data(faults) 70 km
Digital Geological Model (DGM): 33 Geological Formations are modeled > 16000 drillings 320 km 400 m 280 km
Uncertainty quantification for base of Geological Formations Base Kreftenheye Formation (m.a.s.l) Standard Deviation (m)
From Geology to Geohydrology: modeling aquifers and aquitards based on lithological differences 167 hydrological units
For every unit, relevant parameters are modeled and available through the Internet Thickness (m) Permeability (m/d) Hydraulic resistance (d)
Spatial detail necessary South DGM / REGIS II North Holocene layer Pleistocene layer GEOTOP- NL Holocene channel belts Pleistocene layer Marine A Marine B Fluvial clay 12 km. Holocene deposits in the Netherlands sandy clay clayey sand sand Peat
GeoTOP 3D subsurface model of the Netherlands: Nation-wide model (~ 41,000 km 2 ) Upper 30 meters Cellsize 100 x 100 x 0.5 meters Each cell contains: Stratigraphical unit + uncertainty Lithology and sand grain-size classes + uncertainty Hydrological, physical and chemical properties Model application: Groundwater and pollution management Land subsidence studies Infrastructural issues Aggregate resources and clay resources 9 16 Maart 2010
Workflow W-03 Lithostratigraphical interpretation of boreholes 2D Interpolation of stratigraphical surfaces 3D Interpolation of lithoclass within each stratigraphical unit Stochastic simulation techniques allow quantification of uncertainties 10 16 Maart 2010
Rhine-Meuse Delta North Sea The Hague Zuid-Holland Rotterdam 11 16 Maart 2010
Schematic West East cross-section West East 12 16 Maart 2010
Schematic West East cross-section West East 13 16 Maart 2010
Schematic West East cross-section West East 14 16 Maart 2010
Basal peat West East 15 16 Maart 2010
Holland peat West East 16 16 Maart 2010
Dunes and beach sands West East 17 16 Maart 2010
Rhine-Meuse Delta North Sea The Hague 25 km Zuid-Holland Rotterdam 18 16 Maart 2010
Boreholes with stratigraphy 25 km 90 m 19 30 November 2009
Lowermost Pleistocene unit (Waalre) 20 30 November 2009
Pleistocene Rhine sediments (Urk and Sterksel) 21 30 November 2009
Pleistocene Rhine sediments (Kreftenheye) 22 30 November 2009
Pleistocene cover sands (Boxtel) 23 30 November 2009
Holocene Basal Peat 24 30 November 2009
Holocene Rhine sediments (Echteld) 25 30 November 2009
Lower complex of tidal flats and channels (Wormer) 26 30 November 2009
Hollandveen Peat 27 30 November 2009
Beach and shoreface sands (Zandvoort) 28 30 November 2009
Upper complex of tidal flats and channels (Walcheren) 29 30 November 2009
Coastal dunes (Schoorl) 30 30 November 2009
Schematic West East cross-section West East 31 16 Maart 2010
Holocene channelbelts Zuid-Holland Extent Mapped Generations A t/m E Drillings Top and base sand in drillings (pointdata) Litho-classes in drillings (peat, clay, sandy clay etc.) Modelling 2D interpolation top and base 3D interpolation litho-classes 32 16 Maart 2010
Holocene channel systems Channel belt generation A young B The Hague C D North Sea E old Rotterdam 33 16 Maart 2010
Boreholes within channels Channel belt generation A young B C D E old 34 16 Maart 2010
TNO Geological Survey of the Netherlands Channel belt generation A young B C D E old
Lithology and sand grain-sizes Percentage 0 5 10 15 20 25 30 Organic deposits 4.99 Clay and silt Clayey sand and sandy clay 10.48 19.23 Other than sand Fine sand 23.9 Medium sand Coarse sand and gravel 5.32 14.14 Sand Sand, grain-size unknown 21.95 a b c 0 0 0 36 16 Maart 2010
Sand versus other sediments Realisation 1 37 16 Maart 2010
Sand versus other sediments Realisation 2 38 16 Maart 2010
Sand versus other sediments Realisation 3 (10 in total) 39 16 Maart 2010
Sand grain-size classes 10 realisations for each realisation of sand versus other sediments 100 realisations 40 30 November 2009
Other sediments 10 realisations for each realisation of sand versus other sediments 100 realisations 41 30 November 2009
Combined lithology and sand grain-size model Combination of sand grain-size model and the model with other sediments (100 realisations) 42 30 November 2009
Combined lithology and sand grain-size model Lithology Organic deposits 43 Clay and silt Clayey sand and sandy clay Fine sand Medium sand Coarse sand 30 and November gravel 2009
Probabilities Probability that a cell contains medium sand Probability that a cell contains fine sand 44 30 November 2009
Case study: RandstadRail tunnel 45 16 Maart 2010
Rotterdam tunnel projects Early planning assessment of risks and costs critical What can we expect? Depth Pleistocene sands? Position channel belt systems? Concrete lining Steel lining Holocene clays Holocene clays Soil structure adaptations Source: Pleistocene sand Pleistocene sand 46 16 Maart 2010
RandstadRail (lithology) Z N 0 500m Antropogenic Clay & Loam Peat Fine sand Medium sand Coarse sand 47 16 Maart 2010
RandstadRail (lithology) Z N 0 500m Antropogenic & subsurface Clay & Loam Peat Fine sand Medium sand Coarse sand 48 16 Maart 2010
RandstadRail (peat probability estimate) Z N 0 500m 0% 100% 49 16 Maart 2010
RandstadRail (coarse sand prob. estimate) Z N 0 500m 0% 100% 50 16 Maart 2010
Combining Helicopter Electromagnetics (HEM) drilling +
Area Burval: Buried Valleys (Interreg-IIIb project)
HEM - data
HEM + drillings clay 15 70m fine sand 50 coarse sand 120
Correlating HEM and drillings 3D subsurface characterization of the Netherlands 1 januari 2008August 9, 2008
3D modelling HEM data converted into probability of occurrence of clay, fine sand and coarse sand + Drillings, classified into lithology (clay, fine sand, coarse sand) 3D modelling: Simulations (using Simple Kriging) with a-priori local probabilities
Results 1 70 m Clay Fine Sand Coarse Sand Extent of buried valley
Results 2 Horizontal slice at -4m Clay Fine Sand Coarse Sand Extent of buried valley
Results 3 North 70 m South Clay Fine Sand Coarse Sand Extent of buried valley
Conclusion HEM data provide a quick and affordable proxy for geology Drillings and geological expertise are essential for understanding Combining data leads to a lithological model, incl. uncertainties
Thank you for your attention