Centre for Computational Geostatistics February 2013

1. Centre for Computational GeostatisticsFebruary 2013 Dr. Clayton V. Deutsch, P.Eng. Professor, School of Mining and Petroleum Engineering Canada Research Chair in Natural Resources Uncertainty Management Alberta Chamber of Resources Industry Chair in Mining Engineering

2. Centre for Computational Geostatistics (CCG) • Formed in 1998 as a joint industry project at the University of Alberta • Organizations provide funding in return for access and results (documents, data, programs,...) • Multiple levels of membership: Partner, Member, Associate Member and Participant. Based on mutual agreement. • Funding is used for student support (tuition, stipends,...). Faculty are employees of the University of Alberta

3. Vision/Mission/Strategy • Our Vision(what we want to become…): To be the unquestioned leader in the education of geostatistical practitioners and the development of geostatistical tools. • Our Mission(what we want to do…): To teach and equip people to build high quality geostatistical models that realistically reflect natural heterogeneity and accurately measure our uncertainty. • Our Strategy(how we organize what we do…): Our efforts are organized around (1) the delivery of classes to undergraduate students, graduate students, and professionals in industry, (2) the exploration of interesting research ideas in a variety of subject areas and aspects of heterogeneity and uncertainty modeling, and (3) the completion of relevant projects for industry.

4. Members • The number of members is high and stable (some changes to list, five under preparation…) • Membership agreement modern and flexible • Membership levels: • By mutual agreement • Provides flexibility and access • Balance from different industries is good • Petroleum companies • Mining companies • Software vendors • Government agencies • CCG provides many research results • Collaboration is strongly encouraged

5. People of CCG • Clayton and Jeff as professors • Research associates • Alice provides administrative support • Student numbers are stable and climbing • New students • Visiting scholars • Visiting researchers from industry

6. Deliverables • Substantial report each year • Many ideas and programs • 650 papers in CCG reports • Many subjects covered • Dissemination and searching • Guidebooks (new ones): • Guide to Scripting • Guide to Intrinsic Random Functions • Guide to Best Practices (distributed with software) • CCG Resources (go to a demo?)

7. Dissemination of CCG Results • The basic idea of CCG: sponsoring organizations provide unrestricted research funding for preferential access to research results • Research results include papers, guidebooks and monographs, source code, compiled programs, generated data, and training images • Reasonable effort will be made to ensure that the results adhere to the highest standards of scholarship • Comments on distribution of results: • All CCG researchers share their results openly within the CCG community • In principle, we oppose secrecy in research, but distribution is limited to the CCG community pending possible future publication • Each student has full rights to distribute their research as they see fit • Contact Clayton with any questions or concerns • Let’s see some research results/directions…

8. Highlights from 2012: 1/3 • Multivariate modeling • Projection pursuit and direct mapping to MVN • Data replacement in Gaussian and non parametric context • Multiple point statistics • Continuous variables • Mixing training images or linked to tidal range • Important directions • Fracture and fault modeling • Processing for LVA modeling • Uncertainty and data spacing/placement

9. Highlights from 2012: 2/3 • Mining • Simulated learning models • Model checking • Model localization • Petroleum • Modeling at different scales and data integration • Proxy modeling II • Decision making in presence of uncertainty • Ranking and model applications • Model checking • Multiscale ranking and clustering realizations • Model downscaling • Gradual deformation of realizations • CHV and proxy ranking • Scaleup and gridding

10. Highlights from 2012: 3/3 • Useful variogram tools • Automatic fitting from variogram volumes • Nugget effect and standardized pairwise • Extended model of coregionalization • Miscellaneous • Data spacing, uncertainty and optimal placement • Geomechanical properties and scaling • Conditioning object based models and distance function models • Choice of parameters in modeling – checking • Providing useful programs

11. Projection Pursuit Iterations Video

12. PPMT Gaussianity • 25 iterations generally achieves high multiGaussianity Normal Score Transformed PPMT Transformed

13. Gibbs Sampler for Data Replacement • The following video demonstrates Gibbs sampling for replacing data in a non-Gaussian context (the Gaussian case is very easy)

14. Fracture Modeling

15. Discrete Fracture Modeling • Work is progressing well • Optimizing/simulating with a non-standard measure of fracture spacing • Different, but valid • Paper shows comparison

16. Locally Varying Anisotropy • Track curvilinear distances through anisotropy field

17. Faulting and Locally Varying Anisotropy • Jeff and his students actively developing LVA • An incremental, but important development related to faulting

18. Geologic Process Modeling

19. Process Mimicking • Process mimicking or event based modeling will become much more common – especially for training images…

20. Multiple TI Multiple Point Statistics

21. MPS for Facies • Facies are critical heterogeneity for flow • Cell based • Object based • Process mimicking • Multiple point statistics have emergedas one solution to complex dataconditioning • CCG has developed Gibbs Sampler • Training image library

22. Uncertainty in Geological Limits

23. Sampling Realizations

24. Parameter Uncertainty

25. Directly Simulating P10/P50/P90 • Generalized Linear Distributions (GLD) • Specify quantile on global

26. Permeability Modeling • Micro and mini modeling • FMI and core photos • Match to core data • Apply to geomodel

27. White – Primary Variables Gray – Associations Red – Mineralogy Black – SG

28. Model Checking • Checking estimates • Bad estimates • Conditional bias • Checking uncertainty • Narrow • Fair • Model validation and verification

29. Proxy Modeling • Semianalytical solution for rising and spreading period calibrated with some flow simulations • Permits fast calculation of performance • Transfer geological uncertainty through flow predictions • Optimize development decisions and sequencing Heterogeneous Homogeneous

30. Other Material • Multiscale modeling • Randomized methods for large matrices • EnKF data assimilation • Micromodeling for K:

31. Yet More Material • Cross variograms for non colocated data • More useful programs • Correlation matrices andEigenvalues…

32. Flow based Gridding • Workflow • Preliminary mapping • Coarse flow solve • Compute streamline/particle density • Convert to volume constraint • Generate grid

33. WP/DA/SP Optimization • Incorporated porosity, oil saturation, and recovery factor

34. Research Directions • Continue to develop and promote classical geostatistical techniques: • Best practice and training • Incremental/novel developments • New computer/numerical methods including: • Machine learning • Optimization techniques • Inverse theory • Model construction and data integration methodologies: • Multiscale-multivariate data • Different types of measurements • Pseudo-genetic geologic modeling • Areas of application: • Balance Mining and Petroleum • Heavy oil because of local importance • Tools for preprocessing, model construction and postprocessing

35. Future Plans • Continue delivering: • Annual Report • Guidebooks • Monographs • Software • Theses, papers, course notes,… • Develop CCG Guide to Best Practices • Develop CCG Resources • Streamline administration