3D stochastic modelling of litho-facies in The Netherlands
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1 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
2 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 meter) Spatial planning Scenario calculations
3 Modeling with geological expertise Drilling Fault 50 km Depositional extent Supporting Data(surface) Supporting Data(extent) Supporting Data(faults) 70 km
4 Digital Geological Model (DGM): 33 Geological Formations are modeled > drillings 320 km 400 m 280 km
5 Uncertainty quantification for base of Geological Formations Base Kreftenheye Formation (m.a.s.l) Standard Deviation (m)
6 From Geology to Geohydrology: modeling aquifers and aquitards based on lithological differences 167 hydrological units
7 For every unit, relevant parameters are modeled and available through the Internet Thickness (m) Permeability (m/d) Hydraulic resistance (d)
8 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
9 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
10 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 Maart 2010
11 Rhine-Meuse Delta North Sea The Hague Zuid-Holland Rotterdam Maart 2010
12 Schematic West East cross-section West East Maart 2010
13 Schematic West East cross-section West East Maart 2010
14 Schematic West East cross-section West East Maart 2010
15 Basal peat West East Maart 2010
16 Holland peat West East Maart 2010
17 Dunes and beach sands West East Maart 2010
18 Rhine-Meuse Delta North Sea The Hague 25 km Zuid-Holland Rotterdam Maart 2010
19 Boreholes with stratigraphy 25 km 90 m November 2009
20 Lowermost Pleistocene unit (Waalre) November 2009
21 Pleistocene Rhine sediments (Urk and Sterksel) November 2009
22 Pleistocene Rhine sediments (Kreftenheye) November 2009
23 Pleistocene cover sands (Boxtel) November 2009
24 Holocene Basal Peat November 2009
25 Holocene Rhine sediments (Echteld) November 2009
26 Lower complex of tidal flats and channels (Wormer) November 2009
27 Hollandveen Peat November 2009
28 Beach and shoreface sands (Zandvoort) November 2009
29 Upper complex of tidal flats and channels (Walcheren) November 2009
30 Coastal dunes (Schoorl) November 2009
31 Schematic West East cross-section West East Maart 2010
32 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 Maart 2010
33 Holocene channel systems Channel belt generation A young B The Hague C D North Sea E old Rotterdam Maart 2010
34 Boreholes within channels Channel belt generation A young B C D E old Maart 2010
35 TNO Geological Survey of the Netherlands Channel belt generation A young B C D E old
36 Lithology and sand grain-sizes Percentage Organic deposits 4.99 Clay and silt Clayey sand and sandy clay Other than sand Fine sand 23.9 Medium sand Coarse sand and gravel Sand Sand, grain-size unknown a b c Maart 2010
37 Sand versus other sediments Realisation Maart 2010
38 Sand versus other sediments Realisation Maart 2010
39 Sand versus other sediments Realisation 3 (10 in total) Maart 2010
40 Sand grain-size classes 10 realisations for each realisation of sand versus other sediments 100 realisations November 2009
41 Other sediments 10 realisations for each realisation of sand versus other sediments 100 realisations November 2009
42 Combined lithology and sand grain-size model Combination of sand grain-size model and the model with other sediments (100 realisations) November 2009
43 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
44 Probabilities Probability that a cell contains medium sand Probability that a cell contains fine sand November 2009
45 Case study: RandstadRail tunnel Maart 2010
46 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 Maart 2010
47 RandstadRail (lithology) Z N 0 500m Antropogenic Clay & Loam Peat Fine sand Medium sand Coarse sand Maart 2010
48 RandstadRail (lithology) Z N 0 500m Antropogenic & subsurface Clay & Loam Peat Fine sand Medium sand Coarse sand Maart 2010
49 RandstadRail (peat probability estimate) Z N 0 500m 0% 100% Maart 2010
50 RandstadRail (coarse sand prob. estimate) Z N 0 500m 0% 100% Maart 2010
51 Combining Helicopter Electromagnetics (HEM) drilling +
52 Area Burval: Buried Valleys (Interreg-IIIb project)
53 HEM - data
54 HEM + drillings clay 15 70m fine sand 50 coarse sand 120
55 Correlating HEM and drillings 3D subsurface characterization of the Netherlands 1 januari 2008August 9, 2008
56 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
57 Results 1 70 m Clay Fine Sand Coarse Sand Extent of buried valley
58 Results 2 Horizontal slice at -4m Clay Fine Sand Coarse Sand Extent of buried valley
59 Results 3 North 70 m South Clay Fine Sand Coarse Sand Extent of buried valley
60
61 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
62 Thank you for your attention
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