Addresses the burning questions in 3D modelling: What is a good model? What is its usability (beyond pretty pictures)? How reproducible and extensible is it? How can we separate data and interpretation? How do we consider model uncertainty? Features a Bayesian model space exploration of a synthetic case study

1. Uncertainties in 3-D Structural Models
A Probabilistic Perspective and some Considerations to include Additional
Geological Knowledge
Centre for Exploration Targeting (CET)
Geomodelling seminar presentation
March 1, 2014
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 1 / 55

2. Overview of Presentation
3-D Geological
Modelling
Uncertainties
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55

3. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Model validation and
geological “rules”
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55

4. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55

5. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55

6. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55

7. Part 1: Geological Modelling and Uncertainties
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 3 / 55

8. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

9. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

10. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

11. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

12. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
Prospectivity analysis
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

13. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
Prospectivity analysis
Multiple methods and approaches
SKUA%
Earthvision% Geomodeller%
Noddy%
Explicit(
Implicit(
Kinema/c/(
Mechanical(
Geophysical(
Inversion(
VPmg%
Kine3D%
Vulcan%(old)%
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55

14. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

15. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

16. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

17. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

18. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
Consideration of uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

19. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
Consideration of uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55

20. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Bardossy and Fodor (2001):
Sampling and
observation error
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55

21. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55

22. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Incomplete knowledge
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
Conceptual and model
uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55

23. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Incomplete knowledge
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
Conceptual and model
uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55

24. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 7 / 55

25. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 8 / 55

26. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 9 / 55

27. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 10 / 55

28. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 11 / 55

29. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 12 / 55

30. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 13 / 55

31. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 14 / 55

32. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 15 / 55

33. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 16 / 55

34. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 17 / 55

35. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 18 / 55

36. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 19 / 55

37. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Unfortunately, quite infeasible in real applications...
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 19 / 55

38. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55

39. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55

40. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
Randomly generate new
observation sets
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55

41. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
Randomly generate new
observation sets
Create models for all sets
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55

42. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55

43. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55

44. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
How to include additional geological constraints and knowledge?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55

45. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
How to include additional geological constraints and knowledge?
How to combine stochastic geological modelling with geophysical
inversions?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55

46. Part 2: Model Validation and Geological “Rules”
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 22 / 55

47. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55

48. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55

49. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55

50. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55

51. Simple model: Graben
Model of a simple graben (essentially 2-D)
1 km
1 km
Interpolation with Geomodeller,
automation with Python; 3-D view
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

52. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

53. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

54. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

55. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
Surfaces: σ = 75 m in z
direction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

56. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
Surfaces: σ = 75 m in z
direction
Geological knowledge: graben,
normal faulting, three layers
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55

57. Model realisations - all models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 25 / 55

58. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55

59. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55

60. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
Combined effect of syntectonic
sedimentation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55

61. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
Combined effect of syntectonic
sedimentation
Implementation of rules in Python package wrapping stochastic
geological uncertainty simulation and rejection sampling
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55

62. Additional constraints
Additional constraints for Graben model
max
min
Additional constraints:
Min/max values for objects
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55

63. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55

64. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55

65. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
Thickness variation across
fault compartments
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55

66. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
Thickness variation across
fault compartments
In total: 27 constraints based on
these geometric relationships defined.
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55

67. Model realisations - validated models only
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 28 / 55

68. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55

69. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55

70. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
However:
Constraints are fixed values, whereas they might actually be highly
uncertain themselves!
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55

71. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
However:
Constraints are fixed values, whereas they might actually be highly
uncertain themselves!
Inefficient sampling, high rejection rate (> 99% in this case!)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55

72. Part 3: Probabilistic Framework for Multiple Constraints
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 30 / 55

73. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

74. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

75. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

76. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

77. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

78. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
Possibility to explore wide range of parameter space (non-linearities)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

79. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
Possibility to explore wide range of parameter space (non-linearities)
Hypothesis: probabilistic Bayesian framework and combination with
Markov Chain Monte Carlo (MCMC) sampling suitable to address these
questions.
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55

80. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55

81. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55

82. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55

83. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
We want to know the posterior p(θ|y): probability (uncertainty) of a
geological parameter set, given geological rules
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55

84. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
We want to know the posterior p(θ|y): probability (uncertainty) of a
geological parameter set, given geological rules
We need to define the likelihood functions p(y|θ): probability of a
rule, given geological data set
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55

85. Simple example
From simple graben to even simpler example
Reduce the simple graben model to its bare minimum:
From 3-D...
(which is essentially 2-
D)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 33 / 55

86. Simple example
From simple graben to even simpler example
Reduce the simple graben model to its bare minimum:
From 3-D...
(which is essentially 2-
D)
Depth
Some random x-range
Thickness (t1)
Depth of surface 1 (d1)
Depth of surface 2 (d2)
From 3-D (which is essentially 2-D) to 2-D (which is actually even 1-D...)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 33 / 55

87. Prior distribtuions
Prior distributions for depths and thickness: all parameters
independent
0 50 100 150 200 250 300 350
Depth [m]
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
pdf(d1),pdf(d2)
prior d1
prior d2
0 50 100 150 200
Depth [m]
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
pdf(t)
prior thickness
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 34 / 55

88. Sampling from the posterior
Rejection sampling from posterior, determination of “probable”
geological models
0 50 100 150 200 250 300
Some random x-range
300
250
200
150
100
50
0
Depth
Selection of prior samples (N=30)
0 50 100 150 200 250 300
Some random x-range
300
250
200
150
100
50
0
Selection of accepted samples (N=30)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 35 / 55

89. Sampling from the posterior
Rejection sampling from posterior, determination of “probable”
geological models
0 50 100 150 200 250 300
Some random x-range
300
250
200
150
100
50
0
Depth
Selection of prior samples (N=30)
0 50 100 150 200 250 300
Some random x-range
300
250
200
150
100
50
0
Selection of accepted samples (N=30)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 35 / 55

90. Posterior distribtuions
Posterior distributions: how did combining the information change
uncertainty?
0 50 100 150 200 250 300 350
Depth [m]
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
pdf(d1),pdf(d2)
prior d1
prior d2
posterior d1
posterior d2
0 50 100 150 200
Depth [m]
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
pdf(t)
prior thickness
posterior thickness
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 36 / 55

91. Parameter correlation
Parameter correlations: prior and posterior
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 37 / 55

92. Parameter correlation
Parameter correlations: prior and posterior
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 37 / 55

93. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

94. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

95. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

96. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

97. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

98. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

99. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
Detailed analysis of posterior distribution using information theory
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

100. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
Detailed analysis of posterior distribution using information theory
Possibly analyse as Bayesian network
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55

101. Part 3: Application: North Perth Basin
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 39 / 55

102. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55

103. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55

104. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
Significant uncertainties at depth
“...owing to the poor quality of
seismic data [...] [the top] Permian
is commonly only a phantom
horizon.” (Mory and Iasky, 1996)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55

105. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
Significant uncertainties at depth
“...owing to the poor quality of
seismic data [...] [the top] Permian
is commonly only a phantom
horizon.” (Mory and Iasky, 1996)
How uncertain is the model and how can additional information and
geological knowledge reduce these uncertainties?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55

106. Model setup
Initial 3-D geological model
(Mory and Iasky, 1996)
Depth(km)
0
2
4
6
Extent: 34 km EW, 38 km NS, Depth to 7.5 km
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 41 / 55

107. Model setup
Initial 3-D geological model
(Mory and Iasky, 1996)
Depth(km)
0
2
4
6
Extent: 34 km EW, 38 km NS, Depth to 7.5 km
Interpolation with Geomodeller,
input data discretised as:
Surface contact points
Orientation measurements
Plus: definition of stratigraphy
and fault interaction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 41 / 55

108. Uncertainties and constraints in cross-sections
Contact points in cross-sections and definition of fault compartments
Cross Section C
Cross Section B
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 42 / 55
(Jonathan Poh et al. in prep.)

109. Uncertainties and constraints in cross-sections
Contact points in cross-sections and definition of fault compartments
Cross Section C
Cross Section B
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Fault compartments
1
2
3
4
5
6
34 km
38 km
7.5 km
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 42 / 55
(Jonathan Poh et al. in prep.)

110. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)

111. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)

112. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)

113. North Perth Basin - first results, unvalidated models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 44 / 55
Next step: parameterise and add constraints

114. Combining probabilistic modelling with resource
estimations
Probabilistic geothermal resource assessment
Geothermal resource estimation for
North Perth Basin model with
estimation of uncertainty:
Simulate temperature field for
all valid models
calculate geothermal resource
(heat in place)
Preliminary results, presented at
Australian Geothermal Energy Conference
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 45 / 55

115. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

116. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

117. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

118. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

119. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

120. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
Perform rejection sampling for automatic model validation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

121. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
Perform rejection sampling for automatic model validation
Compare differences in geothermal resource estimation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55

122. Outlook and Future Work
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 47 / 55

123. Consideration of additional constraints
Additional geologically motivated constraints
Geometric constraints
Min/max extent
On-surface
Off-surface
Correlation
Thickness
Volume
Curvature
Depth
Lateral Extent
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 48 / 55

124. Consideration of additional constraints
Additional geologically motivated constraints
Geometric constraints
Min/max extent
On-surface
Off-surface
Correlation
Thickness
Volume
Curvature
Depth
Lateral Extent
Stratigraphic relationships
Depth
Lateral Extent
A
B
C
D
EFG
I
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 48 / 55

125. Fault network constraints
Fault shape and interaction
Fault shape and effect
Throw
Direction Angle
Listric
Thickness variation
Depth
Lateral Extent
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 49 / 55

126. Fault network constraints
Fault shape and interaction
Fault shape and effect
Throw
Direction Angle
Listric
Thickness variation
Depth
Lateral Extent
Fault interaction
LateralExtent
Lateral Extent Lateral Extent
LateralExtent
2
1 1
2
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 49 / 55

127. Curvature analysis
Curvature analysis of surfaces
0
20
40
60
80
100 0
20
40
60
80
100
0
10
20
30
40
50
0 50 100 150 200 2500
50
100
150
200
250 Shape index
1.0
0.5
0.0
0.5
1.0
1.0 0.5 0.0 0.5 1.00
1000
2000
3000
4000
5000
6000 Synclastic
synform
Anticlastic
synform
Anticlastic
antiform
Synclastic
antiform
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 50 / 55

128. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55

129. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55

130. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
...
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55

131. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
...
?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55

132. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55

133. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55

134. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55

135. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55

136. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
Idea: use as stochastic model to generate typical probability
distributions expected for specific events (simplest case: fault offset, as
used before!)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55

137. Combining geological modelling and multiphase flow
simulations
Combined inversion of structural interpolation and fluid flow
simulation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 53 / 55

138. Combination with Seismics: Madagascar
Combining implicit geological modelling with seismic simulations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 54 / 55

139. Thank you
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 55 / 55