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Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
methods
Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
methods
Full dynamic sim...
Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
methods
Kinematic modell...
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event 2: Fault E(b) Event...
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event 2: Fault E(b) Event...
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event 2: Fault E(b) Event...
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event 2: Fault E(b) Event...
Code example for fault model
Kinematic modelling
Advantage
Parameterisation of geological history
High level of complexity possible with multiple event...
Kinematic modelling
Advantage
Parameterisation of geological history
High level of complexity possible with multiple event...
Scenario Testing and Sensitivity Analysis for
3-D Kinematic Models and Geophysical Fields
J. Florian Wellmann1, Mark Linds...
Overview of Presentation
“PICO madness”
Fault exampleBasic concept Geophysics
Automation and
set-up of repro-
ducible expe...
Back to overview .
Kinematic modelling concept
Idea behind kinematic modelling
Evaluate interaction between tectonic event...
Back to overview .
Kinematic modelling concept
Idea behind kinematic modelling
Evaluate interaction between tectonic event...
Back to overview .
Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
metho...
Back to overview .
Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
metho...
Back to overview .
Kinematic models
3-D Modeling methods - “endmembers” in modelling methods
Geometric interpolation
metho...
Back to overview .
Additional considerations
Advantage
Parameterisation of geological history
High level of complexity pos...
Back to overview .
Additional considerations
Advantage
Parameterisation of geological history
High level of complexity pos...
Back to overview .
Additional considerations
Advantage
Parameterisation of geological history
High level of complexity pos...
Back to overview .
Setting up a simple model with pynoddy
Model set-up
A simple pynoddy model can be defined with a few lin...
Back to overview .
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event ...
Back to overview .
Setting up a simple model with pynoddy
Adding one fault
Additional code to add both faults:
Back to overview .
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event ...
Back to overview .
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event ...
Back to overview .
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event ...
Back to overview .
Simple fault model
(d) Event 1 + Event 2: combined effect of faults
(a) Initial Stratigraphy
(c) Event ...
Back to overview .
Changing aspects of existing models
Concept
Basic concept: possible to load and modify
existing history...
Back to overview .
Selected model from Virtual Geophysics Atlas
Figure: Sections through the fold and thurst belt model in...
Back to overview .
Calculation of geophysical fields
Gravity and Magnetic field calculation
pynoddy enables the calculation ...
Back to overview .
Comparison of gravity fields
Figure: Gravity of original and changed model
Back to overview .
Stratigraphic difference between generated block models
Figure: Stratigraphic difference between the two ...
Back to overview .
Automation and reproducible experiments
Concept
Main idea: enable definition of reproducible experiments...
Back to overview .
Experiment setup
Creating an experiment object
Experiment objects can be created directly from an exist...
Back to overview .
Outlook
IPython Notebooks
Many more examples about model manipulation and experiment extension
are avai...
Back to overview .
Gippsland Basin study
Experiment for uncertainty analysis in the Gippsland Basin
Back to overview .
More information
Thank you for viewing the presentation!
More information
If you are interested, please...
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Scenario Testing and Sensitivity Analysis for 3-D Kinematic Models and Geophysical Fields - Florian Wellman, RWTH Aachen

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This presentation was presented by Florian Wellmann, Mark Lindsay and Mark Jessell and the recent EGU 2015 conference.
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Geological models are widely used to represent the structural setting of the subsurface. Commonly, a single model is generated for a region, representing the best interpretation of the structural setting in the light of all available information. It is, however, widely accepted that a such created model still contains uncertainties. We hypothesise here that it is possible to transform a single kinematic model into a powerful predictive tool for scenario analysis and uncertainty quantification.
We extend the functionality of a kinematic structural and geophysical modelling approach, implemented in the software Noddy, with a set newly developed Python modules to expose, generalise and automate essential
parts of the modelling workflow. We show how these methods enable us to quickly generate and analyse different geological scenarios.
In addition to the geological model, Noddy also enables the direct calculation of geophysical fields of gravity and magnetics. We can use this functionality to compare the model to measured potential fields. With an example for a fold and thrust belt model, we show how to quickly estimate how changes in the model (due to parameter uncertainties, for example) affect the calculated gravity field in the model range.
Finally, we present the possibility to efficiently generate an ensemble of model realisations for predictive geomodel analysis with an application to a case study in the Gippsland Basin, Victoria. The results show that our
approach can successfully extend the functionality of traditional modelling methods with an additional layer of
predictive power towards an efficient evaluation of uncertainties in structural geological models.

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Scenario Testing and Sensitivity Analysis for 3-D Kinematic Models and Geophysical Fields - Florian Wellman, RWTH Aachen

  1. 1. Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods
  2. 2. Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods Full dynamic simulations
  3. 3. Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods Kinematic modelling approach Full dynamic simulations
  4. 4. Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Example of a fault model: initial stratigraphic pile,
  5. 5. Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Example of a fault model: initial stratigraphic pile, effect of the first fault only,
  6. 6. Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Example of a fault model: initial stratigraphic pile, effect of the first fault only, effect of the second fault only,
  7. 7. Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Example of a fault model: initial stratigraphic pile, effect of the first fault only, effect of the second fault only, combined effect of both faults.
  8. 8. Code example for fault model
  9. 9. Kinematic modelling Advantage Parameterisation of geological history High level of complexity possible with multiple events Automation and implementation in Python scripts straight-forward Very fast computation, even for complex models
  10. 10. Kinematic modelling Advantage Parameterisation of geological history High level of complexity possible with multiple events Automation and implementation in Python scripts straight-forward Very fast computation, even for complex models More examples
  11. 11. Scenario Testing and Sensitivity Analysis for 3-D Kinematic Models and Geophysical Fields J. Florian Wellmann1, Mark Lindsay2 and Mark Jessell2 (1) Graduate School AICES, RWTH Aachen University (2) Centre for Exploration Targeting (CET), The University of Western Australia PICO presentation — EGU 2015 April 15, 2015
  12. 12. Overview of Presentation “PICO madness” Fault exampleBasic concept Geophysics Automation and set-up of repro- ducible experiments
  13. 13. Back to overview . Kinematic modelling concept Idea behind kinematic modelling Evaluate interaction between tectonic events in geological history
  14. 14. Back to overview . Kinematic modelling concept Idea behind kinematic modelling Evaluate interaction between tectonic events in geological history Define influence of events on pre-existing geology with purely kinematic methods
  15. 15. Back to overview . Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods
  16. 16. Back to overview . Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods Full dynamic simulations
  17. 17. Back to overview . Kinematic models 3-D Modeling methods - “endmembers” in modelling methods Geometric interpolation methods Kinematic modelling approach Full dynamic simulations
  18. 18. Back to overview . Additional considerations Advantage Parameterisation of geological history High level of complexity possible with multiple events Very fast computation, even for complex models Direct extension to geophysical forward modelling
  19. 19. Back to overview . Additional considerations Advantage Parameterisation of geological history High level of complexity possible with multiple events Very fast computation, even for complex models Direct extension to geophysical forward modelling Disadvantage Simplification of processes (no dynamics!)
  20. 20. Back to overview . Additional considerations Advantage Parameterisation of geological history High level of complexity possible with multiple events Very fast computation, even for complex models Direct extension to geophysical forward modelling Disadvantage Simplification of processes (no dynamics!) Implementation Original code in C (first published in 70’s!) pynoddy: new implementation in Python, linking to C-code (Now) a high level of flexibility for automation All open source: see pynoddy project page.
  21. 21. Back to overview . Setting up a simple model with pynoddy Model set-up A simple pynoddy model can be defined with a few lines of code. The first step is (usually) to define an initial stratigraphy, for example as a sedimentary layer-cake:
  22. 22. Back to overview . Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Development of a fault network model with pynoddy: initial stratigraphic pile,
  23. 23. Back to overview . Setting up a simple model with pynoddy Adding one fault Additional code to add both faults:
  24. 24. Back to overview . Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Development of a fault network model with pynoddy: initial stratigraphic pile,
  25. 25. Back to overview . Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Development of a fault network model with pynoddy: initial stratigraphic pile, effect of the first fault only,
  26. 26. Back to overview . Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Development of a fault network model with pynoddy: initial stratigraphic pile, effect of the first fault only, effect of the second fault only,
  27. 27. Back to overview . Simple fault model (d) Event 1 + Event 2: combined effect of faults (a) Initial Stratigraphy (c) Event 2: Fault E(b) Event 1: Fault W Development of a fault network model with pynoddy: initial stratigraphic pile, effect of the first fault only, effect of the second fault only, combined effect of both faults.
  28. 28. Back to overview . Changing aspects of existing models Concept Basic concept: possible to load and modify existing history files, e.g.: Created with (original) Noddy GUI (limited to Windows); From online repository, Atlas of Structural Geophysics Loading models from the Atlas of Virtual Geophysics It is possible to directly load models into the Python modules:
  29. 29. Back to overview . Selected model from Virtual Geophysics Atlas Figure: Sections through the fold and thurst belt model in (a) NS-direction, and (b) EW-direction (vertical exaggeration of 1.5) through the centre of the model. (c) Three-dimensional representation for the central three layers of the fold and thrust belt model. The gray surfaces correspond to the location of the sections in the figure above.
  30. 30. Back to overview . Calculation of geophysical fields Gravity and Magnetic field calculation pynoddy enables the calculation of geophysical fields directly from the generated block models. In the combination with the Python scripts, it is easily possible to change aspects of model and evaluate the effect on the simulated potential field. Example of gravity calculation Change event parameters: Update modelled gravity field:
  31. 31. Back to overview . Comparison of gravity fields Figure: Gravity of original and changed model
  32. 32. Back to overview . Stratigraphic difference between generated block models Figure: Stratigraphic difference between the two generated block models
  33. 33. Back to overview . Automation and reproducible experiments Concept Main idea: enable definition of reproducible experiments Implementation Definition of an Experiment class to combine pre- and postprocessing methods Additional basic settings to store experiment settings (number of realsiations, random seeds, etc.) Specific experiment types can easily be defined by inheriting from the base experiment class.
  34. 34. Back to overview . Experiment setup Creating an experiment object Experiment objects can be created directly from an existing history file: Experiment classes combine pre- and postprocessing of kinematic models and the model is recomputed whenever required: Which directly creates this section plot:
  35. 35. Back to overview . Outlook IPython Notebooks Many more examples about model manipulation and experiment extension are available online as interactive IPython notebooks!
  36. 36. Back to overview . Gippsland Basin study Experiment for uncertainty analysis in the Gippsland Basin
  37. 37. Back to overview . More information Thank you for viewing the presentation! More information If you are interested, please have a look at available online resources: pynoddy repository on github (feel free to download, modify, and contribute!) Online documentation about pynoddy There is also a set of online tutorials available. See Abstract for this presentation

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