Courses offered by the Centre for Computational Geostatistics

Teaching is a primary focus of the CCG, from the fundamentals of geostatistics to advanced courses on specialized topics. Learn more about the industry courses offered to mining, petroleum, and geoscience professionals, as well as the university courses that are offered to graduate students.

Fundamentals of Geostatistics Short Course
Principles and Hands-on Practice

Short Course offered by Prof. Clayton V. Deutsch
TBC – Edmonton, AB Canada
University of Alberta, Campus

Short Course Registration Form

Course Registration Fees:
CCG Member – $1,650.00 (CAD)
Non-Member – $1,800.00 (CAD)

The fundamental principles of geostatistics will be covered in lectures and hands-on exercises. Each participant will have an appreciation for the variety of geostatistical techniques and tools available to address problems of heterogeneity modeling and decision making. The participants will be able to apply key techniques and software with their own data. The limitations and assumptions of different methods will be revealed.

This course is primarily intended for geoscientists, engineers and computer scientists with an interest in learning how to build geostatistical models. Students who have taken a similar course will benefit from the new course material and discussion on best practices.

Advanced Multivariate Geostatistics Short Course

This course covers essential theory and practice of advanced multivariate geostatistical modeling. This course builds on existing courses to cover advanced and emerging techniques for multivariate geostatistics. Selected CCG software is used for some hands-on exercises. The mornings are reserved for lecturing. The afternoons are a combination of lectures, hands-on exercises and demonstrations. The main topics include:

1. Multivariate geostatistical model construction
   • Conventional cokriging and cosimulation, including the related Markov, Intrinsic and LMC coregionalization models
   • Linear decorrelation transforms, including PCA and MAF
   • Multivariate Gaussian distribution and the normal score transform
   • Multivariate Gaussian transformations, including the PPMT
   • Variable aggregation and re-expression, including logratios
2. Multivariate classification, including discriminant and cluster analysis
3. Multivariate prediction, including parametric and non-parametric regression
4. Multivariate density estimation, including KDE and Gaussian mixture models
5. Multivariate analysis with unequally sampled data, including cokriging and imputation
6. Post-processing and model checking

Characterization and Management of SAGD Reservoirs with Geostatistical and Optimization Techniques (Short Course)

The tools and techniques presented are not restricted to heavy oil reservoirs to be developed with variants of Steam Assisted Gravity Drainage (SAGD), but the emphasis and examples are all drawn from this area of application. This four day course is appropriate for geoscientists at an introductory level that want an overview of specialized techniques or for geomodelers at an intermediate or advanced level who want details of the latest techniques applied to this important class of resource projects. Each participant will gain an appreciation for the application of modern geostatistical tools and techniques to characterize and manage SAGD reservoirs.

Current best practice geomodeling and geostatistics will be reviewed. SAGD-specific topics that will be covered include (1) 1-D processing of well data for autopicking of surfaces and calculation of effective resource/reserve summaries, (2) resource mapping with realistic uncertainty, (3) geomodeling of facies and petrophysical properties, (4) regridding of models for flow simulation and proxy modeling, (5) optimization (preplanning) of drainage areas and (6) proxy modeling for fast approximate prediction of dynamic flow response. Detailed documentation and all software will be provided with the class notes.

An Introduction to Advanced Geostatistics (Short Course)

Advanced geostatistical techniques are reviewed and taught at an introductory level. This is appropriate for geoscientists that have been introduced to basic or intermediate geomodeling and experienced geomodelers who want a refresher/update on the latest advanced techniques. Each participant will gain an appreciation for the application of modern geostatistical tools and techniques to model heterogeneity and quantify uncertainty. The place, limitations and assumptions of the different methods will be discussed.

Current best practice geomodeling will be reviewed. Techniques that are no longer recommended will be explained. Novel facies modeling including multi-training image multiple point statistics and event based modeling will be presented. This is particularly relevant for high resolution modeling of heavy oil reservoirs and for conventional reservoir modeling. Multivariate techniques for decorrelating data and aggregating multiple variables will be presented. These are particularly important for characterizing unconventional reservoirs and for integrating seismic data. The latest scaling and characterization techniques will be presented. This is useful for using image logs together with conventional well logs in heavy oil and conventional reservoir modeling.

University Undergraduate and Graduate Geostatistics Courses

The following courses are available to registered University of Alberta students studying in the Faculty of Engineering, Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering. The following course descriptions are for information only; for more information and to register for the courses, please visit the University of Alberta website.

MIN E 310 – Ore Reserve Estimation

Conventional and geostatistical methods for construction of orebody models. Contouring techniques for mapping bounding surfaces of stratigraphic layers. Coordinate transforms and geometric techniques. Estimation and simulation methods for characterizing ore grade variability. Ore reserve classification, uncertainty assessment, mine selectivity, and grade control.

MIN E 612 – Principles of Geostatistics

Geostatistical methods are presented for characterizing the spatial distribution of regionalized variables. The theory of random variables and multivariate spatial distributions is developed. This class focuses on the quantification of spatial variability with variograms, estimation with kriging, and simulation with Gaussian techniques.

MIN E 613 – Non-Parametric and Multivariate Geostatistics

Cell based methods for geology modeling, including indicator formalism for categorical data and truncated Gaussian simulation. Object based and process-based approaches for fluvial reservoirs. Indicators for continuous variable estimation and simulation. Multivariate geostatistics including models of coregionalization, cokriging, Gaussian cosimulation, Markov-Bayes simulation and multivariate data transformation approaches. Introduction to advanced simulation approaches including direct simulation, simulated annealing and multiple point simulation. Prerequisite: Consent of instructor.

MIN E 615 – Application of Geostatistics

Public domain and commercial software are reviewed for geostatistical modeling. Special projects in petroleum, mining, environmental and other areas will be undertaken.