Geostatistics Courses

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.

Citation Program in Applied Geostatistics

Instructor: Prof. Clayton V. Deutsch
Location: University of Alberta, Main Campus
Registration Fee: $8,000 (CAD) GST Exempt

Edmonton Citation Schedule for 2018:

  • Week One: May 22-25, 2018 – Attendance is mandatory
  • Week Two: June 18-21, 2018
  • Week Three: July 16-19, 2018
  • Week Four: August 13-16, 2018

The Citation Program in Applied Geostatistics program (CPAG) is designed to assist mining and petroleum engineers and geoscientists better pinpoint the resource bodies for which they search. This program fills an important niche between the conventional one-week short course and the two-year Masters degree program in the Faculty of Engineering. It is ideally suited to those from industry who seek a more in depth understanding of modern geostatistical tools.

  • Communicate the place of geostatistical models of heterogeneity and uncertainty in modern resource/reserve evaluation
  • Explain geostatistical concepts and tools
  • Apply simulation / uncertainty management concepts

These objectives are met through classroom instruction, tutoring, and self-study. In addition to prepared assignments, each participant will undertake a realistic mini-project using their own data (if available) or provided data.

The main topics include:

  • Geologic rock types and stochastic modeling
  • Trends and non-stationarity
  • Variograms for spatial continuity modeling
  • Kriging for optimal estimation of resources
  • Simulation for uncertainty quantification
  • Multivariate modeling
  • Decision making in presence of uncertainty

Prerequisite: Bachelors Degree in Engineering and/or Geoscience related field.

Check out the Applied Geostatistics Website https://www.ualberta.ca/extension/continuing-education/programs/environmental-studies/geostatistics to apply for admission. Contact the Faculty of Extension at (780) 492-5532 or email cpag@ualberta.ca for more information.

The Citation Program in Applied Geostatistics is also available in Chile (March) and Mexico City (late April and lat February).

Vine del Mar – Chile
Contact: marcelo@maptek.cl

Apply to the 2018 Citation Program in Applied Geostatistics

Citation Discussion Forum

The citation discussion forum was created to serve as an interactive platform for citation participants. Users can post their questions, reply to other participants, learn more about GSLIB and interact directly with other professionals. The forum is only available for citation attendants.

Access the Citation Discussion Forum

Masters in Geostatistical Modelling of Mineral Deposits

A new specialized training program, Masters in Geostatistical Modelling of Mineral Deposits, will be taught in partnership with Maptek South America and the Adolfo Ibáñez University. Beginning in 2018, the program is oriented towards working geologists and mining engineers with experience in geostatistics, and consists of 15 modules taught over two years

Logistics

The course is comprised of 388 hours that are structured as follows:

  • Courses and workshops – 328 hours
  • Projects and application – 60 hours

 

Location and dates:

  • Maptek, 2 North 465, Viña del Mar, Campus UAI Viña del Mar. Avda. Padre Hurtado 750, Viña del Mar
  • 13 weekends on Thursday, Friday and Saturday, from March 2018 to January 2020, 9:00 a.m. to 7:30 p.m

 

Teachers

Dr. Clayton V. Deutsch, P.Eng. (Principal Professor)

  • Professor, School of Mining and Petroleum, University of Alberta
  • PhD in Applied Earth Sciences, Stanford University
  • Engineering Canada Research Chair in Natural Resources Uncertainty Management
  • Alberta Chamber of Resources Industry Chair in Mining Engineering
  • School of Mining and Petroleum Engineering Department of Civil & Environmental Engineering

Dr. John Manchuk, P.Eng.

  • Research Associate, School of Mining and Petroleum Engineering, University of Alberta, Canada
  • PhD in Mining Engineering, University of Alberta.

Dr. Ryan M. Barnett, P.Geo.

  • Research Associate, School of Mining and Petroleum Engineering, University of Alberta, Canada
  • PhD in Mining Engineering, University of Alberta.

Dr. Cristián Cáceres

  • Professor of Engineering and Sciences, Adolfo Ibáñez University, Chile
  • PhD. in Mining Engineering, Specialty in Geomechanics, University of British Columbia.

Dr. Gonzalo Ruz

  • Professor of Engineering and Sciences, Adolfo Ibáñez University, Chile
  • PhD in Machine Learning, Cardiff University, UK.

Juan Daniel Silva

  • Professor of Engineering and Sciences, Adolfo Ibáñez University, Chile
  • Docteur in Geology of L’Ingenieur- Ecoles des Mines de Paris

Rafael Sotil

  • Coach, teacher and director of companies
  • Civil Industrial Engineer, MBA University of Chile.

 

Subjects and Schedule

SubjectTeacherDates
Introduction to GeostatisticsC.DeutschMarch 22-24, 2018
Future development of MiningJ. Daniel Silva - J. MoralesMay 23, 2018
Seasonality and SoftwareJ. ManchukMay 24 to 26, 2018
Space continuityR.Barnett26 to 28 July 2018
Future development of MiningM. Herrera - J. MoralesAugust 17, 2018
Modeling application in MiningC. Cáceres - G. RuzAugust 18, 2018
Kriging.C.DeutschSeptember 27 to 29, 2018
Parameter InferenceC.DeutschNovember 22 to 24 2018
Modeling application in Mining. Machine LearningC. Cáceres - G. RuzJanuary 18-20, 2019
Modeling of Categorical VariablesJ. ManchukMarch 28-30, 2019
Modeling of Continuous VariablesJ. ManchukMay 23 to 25, 2019
Multivariate AnalysisR.BarnettJuly 25-27, 2019
Geometallurgy and GeomechanicsR.BarnettSeptember 26 to 28, 2019
Workflows for problem solvingC.DeutschNovember 28-30, 2019
Modeling application in MiningC. Cáceres - G. RuzJanuary 16, 2020

The defense of projects and graduation will take place on January 17, 2020.

 

Additional Information

For additional information and to register, please visit the following website:

Register to the Masters of Geostatistical Modeling in Mineral Deposits

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Probabilistic Resource Modeling with Geostatistics and Python

What. A four day intensive course going through the steps of surface, boundary, rock type/facies and
multivariate property modeling for probabilistic resources. The worked example is based on real data
and is suitable for Mining and Petroleum (and other) applications. All software, data, notebooks and a
full worked solution will be provided to participants.

Dates and Location. May 14-17, 2018 at the University of Alberta in Edmonton, Alberta. An appropriate
centrally located accessible room has been reserved for the class.

Background. The generation of geostatistical resource models has become commonplace. The CCG has
been developing methodology and software for probabilistic resources for 20 years. This course
presents the steps to construct realizations of surfaces, boundaries, categorical variables and multiple
continuous variables for probabilistic resources. The focus is on essential theory, implementation
details and practice. Each participant will have the opportunity to perform the entire workflow in a
series of python notebooks. Participants will benefit from completing an integrated workflow with
software they take with them. The latest proven techniques will be used. See Outline below.

Who Should Attend. This course is primarily intended for geoscientists, engineers and others with an
interest in learning how to quantify resource uncertainty and build geostatistical models with python
and the latest tools. Relative newcomers to resource modeling will benefit from the overview and going
through all of the steps in a probabilistic resource model. Experienced professionals will benefit from
exposure to the latest modeling techniques and software.

Instructor. Clayton Deutsch will coordinate the course, be present for the duration and deliver most
lectures. Dr. Deutsch leads the CCG and is a Professor in the School of Mining and Petroleum
Engineering at the University of Alberta. Dr. Deutsch holds the Alberta Chamber of Resources Industry
Chair in Mining Engineering and the Canada Research Chair in Uncertainty Management.

Cost and Registration. The course will be administered by Clayton V. Deutsch Consultants Ltd.
Registration fees will offset course preparation, professional time and an honorarium to the extensive
support that will be provided by senior students and associates:

Student RegistrationCost
GeneralRegular Registration2000 CAD
Early Registration (before March 31)1750 CAD
CCG MemberRegular Registration1500 CAD
Early Registration (before March 31)1250 CAD

Contact cdeutsch@ualberta.ca for registration details and any questions regarding the course.

Outline. The following series of lectures and practice sessions will be presented. A common set of data
will be used for all practice sessions. Participants will have an opportunity to practice and will also be
provided with the full solution set. Participants can bring their own data to play with and for questions.

DayTimeSubject
Monday 8:30 – 9:15 Introduction to Probabilistic Resource Assessment
9:15 – 10:00 Stationarity: Surfaces, Boundaries, Rock Types
10:30 – 11:15 Data Transformation, Declustering and Trends
11:15 – 12:00 Variogram Calculation, Interpretation and Modeling
1:00 – 1:45 Introduction to Python, CCG Programs and other Tools
1:45 – 2:30 Practice: Computer Setup
3:00 – 3:45 Practice: Python, Data, File and Program Management
3:45 – 4:30 Practice: Visualization with Pygeostat and Paraview
Tuesday 8:30 – 9:15 Surface Modeling with Uncertainty
9:15 – 10:00 Practice: Surface Modeling
10:30 – 11:15 Thickness and Tabular Modeling with Uncertainty
11:15 – 12:00 Practice: Thickness Modeling
1:00 – 1:45 Boundary Modeling with Uncertainty
1:45 – 2:30 Practice: Boundary Modeling
3:00 – 3:45 Complex Multiple Overlapping Geometry Modeling
3:45 – 4:30 Practice: Geometry Modeling
Wednesday 8:30 – 9:15 Categorical Variable Definition and Trend Modeling
9:15 – 10:00 Practice: Categorical Trend Modeling
10:30 – 11:15 Hierarchical Truncated PluriGaussian (HTPG) Modeling
11:15 – 12:00 Practice: HTPG Modeling I
1:00 – 1:45 HTPG Modeling Setup and Implementation
1:45 – 2:30 Practice: HTPG Modeling II
3:00 – 3:45 PPMT Transform for Multivariate Property Modeling
3:45 – 4:30 Practice: PPMT Modeling I
Thursday 8:30 – 9:15 PPMT Modeling Setup and Implementation
9:15 – 10:00 Practice: PPMT Modeling II
10:30 – 11:15 Model Assembly and Post Processing
11:15 – 12:00 Practice: Model Post Processing
1:00 – 1:45 Model Coordinates, Regridding and Rescaling
1:45 – 2:30 Practice: Model Manipulation and Reporting
3:00 – 3:45 Discussion and Help Session for Participant Problems
3:45 – 4:30 Application of Probabilistic Resources

Some Details on the Software. The modeling project will be executed in a series of Python Jupyter
notebooks on a Windows machine (ideally the participant’s laptop, but laptops are available). The open
source Anaconda Python distribution will be used with ParaView as the open source visualization
application. CCG programs and the Pygeostat package will be used. Installation and setup is
straightforward. Detailed instructions on software installation, the data, exercise notebooks and the
working solution will be sent before the class. Prereading and computer preparation will be suggested

Information & Dates for the Geostatistics Short Course

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.

Information & Dates for the Geostatistics Short Course

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.

Information & Dates

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.

Information & Dates for the Advanced Geostatistics Short Course

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.

University of Alberta School of Mining and Petroleum Engineering

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.