1. Combined Geological Modelling and Flow Simulation J. Florian Wellmann, Lynn Reid, Klaus Regenauer-Lieb and the Western Australian Geothermal Centre of Excellence TIG-10 Workshop, Adelaide 11/2010

2. Outline Addressing the problem that geological modelling and geothermal simulation are usually separated (and geological uncertainty not considered – even if significant) Workflow to integrate both steps into one framework Two example models: 1.Hypothesis testing for different geological scenarios 2.Combination with geological uncertainty simulation

3. Geological Modelling Construction of a structural representation of the subsurface Interpolation based on discretized geological observations (e.g. from drillholes, seismics, field work) Applied modelling tool: GeoModeller (Intrepid Geophysics, BRGM) (Calcgano et al., 2008)

4. Geothermal Flow Simulation Coupled simulation of fluid and heat transport equations in the subsurface Based on property distribution (e.g. permeability, porosity, thermal conductivity, heat capacity) in subsurface and boundary conditions (e.g. basal heat flux) Applied simulators: TOUGH2, SHEMAT Permeability Porosity

5. Boundary conditions Geological Model Mesh Property assignment Simulation Discretized geological model Geological Data Manual steps

6. Critical steps Mainly related to –model construction, –mesh generation and –processing to flow simulation Once constructed, the geological model is rarely changed or extended, even if significant source of uncertainty! Steps before flow simulation

7. Automation steps Geological modeling Discretization Model simulation setup Simulation and analysis Implicit potential-field method (GeoModeller TM ); enables direct model update Automated rectilinear mesh discretization (python scripts) Direct update of input files for simulation with SHEMAT and TOUGH2 (python scripts) Simulation with available codes, post-processing and analysis (python scripts) Change one data point Evaluate effect on flow field

8. Geological Hypothesis Testing near-surface heat flux (z-dir)

9. Combination with Uncertainty Simulation Consider uncertainties in structural geological models (one of main sources of uncertainty) Approach: random change of input data (discretized surface position, orientation data)

11. Wells don’t penetrate basement! Assume: structure more or less well defined (seismics) but exact position at depth unknown Example model North Perth Basin

12. Change bottom of formations randomly Formation NameStandard deviation Cadda20m Woodada-Kockatea100m Permian200m Standard deviation for data points defined at bottom of formation Position of formation bottoms changed about random value Create 20 different input data sets and 20 different models

13. Results of simulation For 20 geological models, we obtain 20 simulated flow and heat flow fields (drawing from the uncertainty distributions) Example of one temperature model

14. Local mean and standard deviation of Temperature mean stdev

15. Conclusion Uncertainties in structural model influence simulated geothermal flow field but they are usually not considered Developed integrated workflow –enables hypothesis testing and consideration of geological uncertainty –compliments and extends other approaches (e.g. stochastic simulation, as presented by Tony Meixner), e.g.: physics (multi-phase, thermo-hydro) mesh (rectilinear) consideration of uncertainties in geological data (not the model) Specifically suited for early exploration stages and resource evaluation where uncertainties in the structural model are dominant.

16. Outlook Complete implementation on supercomputer Optimal mesh construction for geological models (e.g. automatic rectilinear refinement, extruded triangular for TOUGH2) Coupling to advanced resource estimation methods (talk at AGEC) Combination with GIS methods Thank you for your attention!

17. Appendix

18. Uncertainty in Geology models: different types Incomplete knowledge Are all relevant structures known? How to analyse uncertainties in structural models?

19. Uncertainty in Geology models: different types Uncertainty of interpolation How good is the interpolation between data points? How to analyse uncertainties in structural models?

20. Uncertainty in Geology models: different types How exact is the data? Uncertainty in raw data How to analyse uncertainties in structural models? Applies specifically to interpreted data and assumptions We consider this to be a significant part of model uncertainties (Wellmann et al, 2010)

21. “Complex” and “simple” geological settings

22. Mesh geometries Regular mesh Rectilinear mesh Extruded triangular mesh (only TOUGH2) (work in progress: optimal mesh generation from geological models) (Include example extruded triangular?)

23. Processing simulated models to simulation Coupled fluid and heat flow simulation in a 2-D subset of the model Discretization in a regular grid Two highly permeable formations

24. Convective vs. conductive heat transfer Local Peclet number Pe = l v /  In our case: - l : characteristic length - v: fluid velocity (model result) -  : thermal diffusivity (10 -6 m 2 /s) Conduction dominated: low Pe-Number Convection dominated: high Pe-Number As characteristic length scale, we use engineering lifetime of 30 years and get l approx 60 m

25. Example of local Pe-Numbers for one model Pe x Pe z left right downup

26. Local mean and standard deviation: Pe z-direction

27. Problem description In hot sedimentary aquifer systems, fluid and heat flow fields are sensitive to changes in geometry In realistic settings, a geometrical description of the geology (geological model) is usually the basis for fluid and heat flow simulations Geological models contain uncertainties We hypothesise that uncertainties in the geometrical description of geology have a significant influence on simulated flow fields ?? Geological model Temperature field