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Mark Jessell - Assessing and mitigating uncertainty in 3D geological models in varying scenarios

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Mark Jessell of UWA presents the latest in 3D modelling workflows, amnesia and uncertainty mitigation.

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Mark Jessell - Assessing and mitigating uncertainty in 3D geological models in varying scenarios

  1. 1. Mark Jessell CET/SES/UWA Dory Nemo Assessing and mitigating uncertainty in 3D geological models in varying scenarios 4/4/2018
  2. 2. Characterised Uncertainty = scientific and economic value The age of this granite is 107 Ma…………………………………………….Value $5000? The age of this granite is 107  290 Ma………………………………………..Value $50 The age of this granite is 107 Ma  ????? ………………………………..Value $???
  3. 3. Uncertainty frameworks & metrics • Mann, 1993 Uncertainty in geology: International Association for Mathematical Geosciences, IAMG Studies in Mathematical Geology, v. 20, p. 241–254. • Kennedy & O’Hagan 2001 Bayesian Calibration of Computer Models Marc C. Kennedy; Anthony O'Hagan Journal of the Royal Statistical Society. Series B (Statistical Methodology ), Vol. 63, No. 3. (2001), pp. 425-464. • Bardossy and Fodor, 2001 Traditional and new ways to handle uncertaintyin geology: Natural Resources Research, v. 10, p. 179−187. • Thore et al., 2002 Structural uncertainties: Determination, management and applications: Geophysics, v. 67, p. 840–852. • Tacher et al., 2006 Geological uncertainties associated with 3-D subsurface models: Computers and Geosciences, v. 32, p. 212–221. • Caers, 2011 Modelling uncertainty in the earth sciences: Chichester, Wiley, 239 p. • Lark et al., 2013 A statistical assessment of the uncertainty in a 3-D geological framework model: Proceedings of the Geologists’ Association, v. 124, p. 946–958. • Nearing et al 2016 A philosophical basis for hydrological uncertainty Grey S. Nearing, Yudong Tian, Hoshin V. Gupta, Martyn P. Clark, Kenneth W. Harrison & Steven V. Weijs (2016) A philosophical basis for hydrological uncertainty, Hydrological Sciences Journal, 61:9, 1666-1678
  4. 4. 1 km 3D geomodelling scenarios Sedimentary Basins Mines Regional Lithosphere 3D Constraints RICH (3D seismic, deep boreholes, gravity) RICH (dense boreholes, magnetics, seismic, electromagnetics) POOR (rare boreholes, surface outcrops, gravity, magnetics) RICH (Teleseismic, seismic, gravity, MT) Structural Complexity SIMPLE(R) COMPLEX COMPLEX SIMPLE(R) Dedicated Software Gocad 1989, Geomodeller 1999… MicroMine 1986, Leapfrog 2003... Noddy 1981, Gocad- Sparse 1995, Geomodeller 1999 … Gocad 1989 Starting point 3D seismic reflection Boreholes Maps Seismic tomography, RF Dimensionality 3D -> 3D 1D -> 3D 2D -> 3D 3D -> 3D 30 km5 km 200 km Geothermal energy Hydrogeology Urban Geology Natural Hazards …
  5. 5. Haldoresen & Damsleth, 1990 The Hybrid model is a two-stage model In Stage 1, a discrete model describes the large-scale heterogeneities in the reservoir- e.g., the various sedimentological building blocks or the flow units. In Stage 2, different continuous models describe the spatial variations of the petrophysical parameters within each class.
  6. 6. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  7. 7. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  8. 8. Measurement Error Allmendinger et al., 2017 6º 6º 14º 30 Measurements for each instrument for each orientation of sandstone surface
  9. 9. Natural Variability 56º
  10. 10. Pakyuz-Charrier et al in prep., 2018 Down-hole Positional Error Gjerde et al., 2011 Measurement While Drilling (MWD) technology error at 2200 m depth 60m N-S 100m E-W 15m Z
  11. 11. Williams, 2009 Natural Petrophysical Variability Litho-control (McGaughey 2018) vs alteration-control (Dentith 2018)
  12. 12. +/- 5% error in positional measurements +/- 5° error in structural measurements • Mitigated by stochastic modelling? Large errors resulting from natural variability • Mitigated by denser data collection and stochastic modelling? Measurement Error & Natural Variability
  13. 13. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  14. 14. Same geology, different mappers, different datasets (mag survey was 1976) BRGM 1992 BGR 2004 south-east Côte d’Ivoire Geology “House styles”
  15. 15. Texture Grainsize Lithology Colour Shade Oxidation Texture Grainsize VA FG VA FG SH FG VA FG VA FG BX FG SP FG SP FG MA FG MA FG FR VC FR VC FR VC FR VC FR VC FL VC FR VC FL FG CL VC CL VC CL VC CL VC FL VC CL VC CL VC CL CG CL CG BD FG CL VC MA FV MA FV MA FC MA FC BX FC MA FC BX CG MA FC MA FC MA FV MA FV BX CG MA FC FL FG MA FG MA FG BD VF MA CG MA FC MA CG MA FC BD FG BD VC MA CG MA CG MA FV MA VC BD FV MA VC MA VC MA FG MA VC FL VF BD FV BX FV BD VF BD VF BX FG BD VF BD VF MA VF BD FC PW FG BX VC MA FG BD FV MA FG BD FV MA VF BD FV MA FG BD FV MA FG MA FG MA FG BX FM MA MG BX FM MA MG MA MG BD FV BD VF BD FG BD VF FL VF BD FG BX CG BD FM MA FG SH VF MA VF BD FV MA VF BD FV BD FC SH VF MA VC MA FG BD FV FL FG FL FG BD VF MA FG BD FV MA VC MA VC MA VC MA VC MA FG MA MG BX FV MA MG BX MG GP CG BX FV GP CG GP CG SH FG GP MG GP MC GP MC MA FC SH FG MA FM MA FG MA MG MA FM SH FG MA FM MA MG MA MG MA FG BD VF PW FG PW FG FL VF FL VF FL FG MA MG BD FM PW FG BX CG PW FG PW FG FL FG PW VF SH FG SH FG BD VF BX FV PW VF PW FG FL VF PW VF PW VF PW VF BX FM MA VF MA VF FL VF FL VF FL FG FL FG FL FG FL FG FL FG Lithology Colour Shade Oxidation Texture Grainsize Lithology Colour Shade Oxidation Texture Grainsize MB GN P FR VA FG MB GN P FR VA FG SZ GY D FR SH FG MB GN D FR VA FG MB GN D FR VA FG UM GN D FR BX FG UM GN D FR SP FG UM GN D FR SP FG RTS RD D OX MA FG RTSC WH L OX MA FG FDC GNRD M PO FR VC FDC GNRD M FR FR VC FDC GN M FR FR VC FDC GNRD M FR FR VC FDC GN M FR FR VC F KH M FR FL VC FDC KH M FR FR VC VN WH M FR FL FG SCF KH M FR CL VC SCF KH M FR CL VC SCF KH M FR CL VC AN GNRD D FR CL VC F KH M FR FL VC AN GNRD D FR CL VC NSR SCF GN D FR CL VC NSR SCX BE L FR CL CG NSR SCX GN D FR CL CG IZS GY D FR BD FG SCX GNRD D FR CL VC AN M L FR MA FV AN M L FR MA FV SZ TN L FR MA FC AN TN L FR MA FC F TN L FR BX FC AN TN L FR MA FC F GY D FR BX CG AN TN L FR MA FC AN TN L FR MA FC AN KH D FR MA FV AN KH D FR MA FV F GY D FR BX CG AN TN D FR MA FC F GY D FR FL FG AN BK D FR MA FG AN BK D FR MA FG SL BK D FR BD VF NSR SGW GY D FR MA CG AN BK D FR MA FC NSR SGW BK D FR MA CG AN BK P FR MA FC SL GY D FR BD FG SCX GY D FR BD VC AN GY D FR MA CG NSR SCX GY D FR MA CG AN GY D FR MA FV SCX GY D FR MA VC SSP GY D FR BD FV SCX GY L FR MA VC SCX GY L FR MA VC MB BE L FR MA FG SCX GY L FR MA VC F BK D FR FL VF SBS GY D FR BD FV BXR BN D FR BX FV SBS BK D FR BD VF SBS BK D FR BD VF BXR BK D FR BX FG SL GY D FR BD VF SL GY D FR BD VF MB GY D FR MA VF ISV GY D FR BD FC MB GY D FR PW FG BXR GY D FR BX VC MB GY D FR MA FG ISV GY D FR BD FV MB GY D FR MA FG ISV GY D FR BD FV MB GY L FR MA VF ISV GY D FR BD FV MB GY D FR MA FG ISV GY D FR BD FV MB GY D FR MA FG MB GY D FR MA FG MB GY D FR MA FG VN GN L FR BX FM MD GY L FR MA MG BX GY D FR BX FM MD GY L FR MA MG MD GY L FR MA MG SGW BK D FR BD FV ISV BK D FR BD VF SS GY D FR BD FG ISV BK D FR BD VF F BK D FR FL VF ISV GY D FR BD FG F GY D FR BX CG ISV GY D FR BD FM MB GY D FR MA FG SBS BK D FR SH VF MB GY L FR MA VF ISV GY D FR BD FV MB BN D FR MA VF ISV BK D FR BD FV ISV GY D FR BD FC F GY D FR SH VF BX GY D FR MA VC MB BN D FR MA FG ISV BK D FR BD FV F BK D FR FL FG F BK D FR FL FG SBS BK D FR BD VF MB GY D FR MA FG ISV GY D FR BD FV SCM GY D FR MA VC SCM GY D FR MA VC SCM GY D FR MA VC SCM GY D FR MA VC MD GN D FR MA FG MD GN D FR MA MG F GN D FR BX FV MD GN D FR MA MG F GN D FR BX MG MDG GN D FR GP CG F BN D FR BX FV MD GN L FR GP CG MD GN L FR GP CG SZ GN L FR SH FG MD GY L FR GP MG MD GN L FR GP MC MD GN L FR GP MC MD GN D FR MA FC SZ GN L FR SH FG MD GY L FR MA FM MD GN D FR MA FG MD GN D FR MA MG MD GN D FR MA FM SZ GN D FR SH FG MD GN D FR MA FM MD GN D FR MA MG MD GN D FR MA MG MB GN D FR MA FG ISV GY L BD VF MB GN D FR PW FG MB GN D FR PW FG MB GN L FR FL VF ISV GY L FR FL VF MD GN L FR FL FG MD GN D FR MA MG ISV GY L FR BD FM MB GN D FR PW FG VN WH L FR BX CG MB GN D FR PW FG MB GN D FR PW FG MB GN P FR FL FG MB GY D FR PW VF LO GY D FR SH FG LO GY D FR SH FG ISV BK D FR BD VF MB GY L FR BX FV MB GY L FR PW VF MB GN D FR PW FG F GY L FR FL VF MB GN D FR PW VF MB GN D FR PW VF MB GN D FR PW VF F GN D FR BX FM MB GN D FR MA VF MB GN D FR MA VF MB GY D FR FL VF ISV GY D FR FL VF MB KH L FR FL FG MB KH L FR FL FG MB KH L FR FL FG MB GY D FR FL FG MB GY D FR FL FG e Grainsize Lithology Colour Shade Oxidation Texture Grainsize FG FG FG FG FG FG FG FG FG FG VC VC VC VC VC VC VC FG VC VC VC Lithology Colour Shade Oxidation Texture Grainsize Lithology Colour Shade Oxidation MB GN P FR VA FG MB GN P FR VA FG SZ GY D FR SH FG MB GN D FR VA FG MB GN D FR VA FG UM GN D FR BX FG UM GN D FR SP FG UM GN D FR SP FG RTS RD D OX MA FG RTSC WH L OX MA FG FDC GNRD M PO FR VC FDC GNRD M FR FR VC FDC GN M FR FR VC FDC GNRD M FR FR VC FDC GN M FR FR VC F KH M FR FL VC FDC KH M FR FR VC VN WH M FR FL FG SCF KH M FR CL VC SCF KH M FR CL VC SCF KH M FR CL VC Archival logging uncertainty
  16. 16. Nathan et al., 2017 Learning characteristic natural gamma shale marker signatures
  17. 17. Large errors during observations • Mitigated by additional data types & machine learning? Observational Uncertainty
  18. 18. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  19. 19. Lark et al, 2014 Base of London Clay One geologist’s interpretation of the base of the London Clay (red) with 95% confidence intervals (blue) based on 28 geologists interpretations
  20. 20. 3D Seismic Case History of the Darlot – Centenary Gold Mine Foley et al., AEGC Extended Abstract 2018
  21. 21. Interpretation of one (synthetic) dataset Bond et al., I.J. Sci. Ed., 2011, v33.
  22. 22. Ridge strength map (+ve Phase Symmetry) Magnetic data Feature Evidence Tools in the Integrated Exploration Platform Valley strength map (-ve Phase Symmetry) Edge strength map (Phase Congruency) David Nathan, Jason Wong CET/UWA
  23. 23. Joining up the dots After Caumon, 2007
  24. 24. Polson & Curtis 2010 in Curtis 2012 The science of subjectivity “Scientists should therefore not be ashamed of subjectivity, but we should strive to develop methods to quantify and sometimes to reduce its effects”
  25. 25. Major personal biases during interpretation • Mitigated by collective analysis? Interpretation Ambiguity
  26. 26. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  27. 27. “…our non-geophysical colleagues might be tempted to think that geophysicists have eliminated uncertainty from our subsurface images. Nothing could be further from the truth. Most (if not all) of the time, geophysical characterization of the subsurface involves estimating solutions to ill-posed inverse problems.” Amaru et al. (2017) Introduction to special section on velocity-model uncertainty
  28. 28. North West Shelf 3D Velocity Modeling Laureline Monteignies* Cédric Magneron Natalia Gritsajuk ASEG Abstract 2016
  29. 29. Tchikaya et al., 2016 Multiple realisations of inversion of gravity data
  30. 30. Inherent ambiguity in geophysical data • Mitigated by stochastic simulation? Inversion Suites
  31. 31. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  32. 32. Scaling issues Watterson et al, 1996 All faults Faults with throw > 20m
  33. 33. Cherpeau et al, 2012
  34. 34. Grose et al., 2017 Structural data constraints for implicit modelling of folds
  35. 35. Chilés et al. 2004 Uncertainties in surface generation
  36. 36. de Kemp et al 2016 2010 2015 Model evolution with time Data availability + =
  37. 37. De Kemp et al 2016 Uncertainty derived from model evolution Uncertainty derived from input data density
  38. 38. Major personal biases during domain construction • Mitigated by stochastic simulation? • Mitigated by incorporation of additional data types? Domain Construction
  39. 39. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  40. 40. Kriging-based property infill Leuangthong & Srivastava, 2012
  41. 41. Feyen & Caers 2006 Physics based property infill
  42. 42. Parquer et al., 2017 Physics based property infill
  43. 43. Complexity of physics and chemistry of natural systems • Mitigated by stochastic simulation? • Mitigated by incorporation of prior knowledge? Property Infill
  44. 44. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream Prediction
  45. 45. Downsteam prediction Typically requires additional prior knowledge => More uncertainty Past • Transport properties (history matching) Actual • Grade • Rock physics (geomet, porosity) • Petrophysics Future • Transport properties • Mine planning • Economic viability
  46. 46. Multistage Uncertainty Propagation
  47. 47. Haldoresen & Damsleth, 1990 Rock Physics Geology Geology Rock Physics Rock Physics Stats Stats
  48. 48. Haldoresen & Damsleth, 1990
  49. 49. Charles et al., 2001
  50. 50. Thore et al., 2002
  51. 51. National Drilling Initiative Mt Isa Geophysical Province 0 100 200 kilometres What is the best drilling strategy to optimize cover AND bedrock sampling? Not a regular grid if we have any prior knowledge?
  52. 52. Unperturbed geological model Same petrophysical properties (6) (5) (4) (3) (2) (1)
  53. 53. Monte Carlo Probabilistic models for each lithology Giraud et al. 2017 Pakyuz-Charrier et al., 2018
  54. 54. NC P P GP GP Giraud et al. Geophysics, 2017 Geologically: bestGeologically: best T R U E 2D Geophysical Inversion results Single domain: unconstrained inversion Single domain: petrophysics only Joint inversion: Petrophysics only Single domain: Geology and Petrophysics Joint inversion: Geology and Petrophysics True model Geoph y Petro Geoph y Petro Geoph y GeolPetro Geoph y GeolPetro Geoph y Density Magnetic Susceptibility Geol
  55. 55. Topology as a 3D model classifier Post-processing 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 Archetype candidates Normal Faulted Both None NA Topology Pakyuz-Charrier, in prep 2018 Lindsay et al, 2012
  56. 56. 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 𝟎 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 𝟏 1 1 1 Cluster 1: 930 models Cluster 2: 10 models Cluster 3: 60 models Archetypical uncertainty models
  57. 57. How to carry uncertainty forward? 1. Brute force propagation (e.g. Monte Carlo) 2. Homogenisation via PDF representation of variability 3. Identification of important classes density Mag sus Step 1 Step 2 Step 3
  58. 58. Use of uncertainty in decision making is very compartmentalized • Mitigated by propagation strategies? Current systems typically do not allow uncertainty as an input • Mitigated by propagation strategies? Multistage Uncertainty Propagation
  59. 59. Conclusions
  60. 60. Scenario Stage Mine Basin Regional Direct acquisition Drill hole logging Borehole position Surface Geology Interpretation Indirect acquisition Geophysical inversions Velocity Model Geophysical inversions Model Construction Topology of surfaces & property infill Interpretational Ambiguity Physics-based construction & property infill Downstream Uncertainty Petrophysics to “useful” properties Rock physics uncertainty (leaky or tight faults…) Rock physics uncertainty Biggest sources of uncertainty?
  61. 61. What is stopping us? • Current software is poorly adapted to using probabilistic information as inputs and/or storing probabilistic information (particularly multiple domain models) as outputs • Current software does not use physics-based prior knowledge to populate domains or define the domains themselves • Spatial and temporal topology only partially accounted for during modelling • Geophysical inversions are too-often reduced to fixed support for domain boundary interpretations
  62. 62. l∞p = New Open Source 3D geological modelling platform = GemPy + foldinv + map2model + pyNoddy + CURE + TOMOFASTx… + + + + + Implicit modeller Structural inversion algorithms Geological Event Manager Topological Analysis of source data 3D Uncertainty Analysis Integrated Geophysical Inversion l∞p consortium+ +
  63. 63. Measurement Error & Variability Observational Uncertainty Interpretation Ambiguity Inversion Suites Domain Construction Property Infill Downstream PredictionCurrent situation at Mine and regional scale
  64. 64. Conclusion Although there is much to learn from O&G and other geoscience fields in terms of improving uncertainty analysis in individual tasks in the 3D modelling workflow, probably the most important lesson is the need to move towards a coherent workflow (though not necessarily a single piece of software) where uncertainties are retained and propagate from data acquisition to model predictions.
  65. 65. Weinersmith 2017 Weinersmith & Weiner 2017

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