You are here



We look forward to meeting you.

AR2Tech offers training opportunities, we participate in conferences, and we publish our work. We create customized training and evaluation programs that focus on your unique analytical needs.


Available Courses

Dr. Boucher has taught geostatistics world-wide, in companies, in universities and at conferences. His experience at Stanford University translates into high-quality teaching targeted to a wide mining, petroleum and environmental agencies audience. While some short courses may be given remotely, AR2Tech is always ready to travel and work with your infrastructure and find the methods that best suit your training needs and future usage.

Machine Learning and Data Analysis for Geomodeling [new for 2016]

This 4 day short course aims at providing the essential skills to understand and apply modern tools in data analysis to problems commonly found in geomodelling applications. The course is based on the python scikit-learn package. It covers dimensionality reduction, clustering, regression, classification and data visualization. The attendees should be familiar with the basic of programming (variables, conditional statement, loop), a basic primer for the python language will be provided. The last day is reserved for a workshop where the attendees will apply their new skills on their own data set under the supervision of the instructor. The emphasis of the course is empowering the attendees to use theses tools on their own spatial applications with the help of the ar2gems software. Instructor: Herve Gross

Geomodeling for Reservoir Simulation [new for 2016]

This course covers fundamentals of reservoir simulation as a practical tool for reservoir performance forecasting and optimization. The goal of the class is to give geomodelers a clear understanding for how geological reservoir models are exploited with a simulator. The class is hands-on, and theory is kept to the essential (no prerequisites needed). Guided exercises with a simulator will be used to demonstrate concepts. The connection between geomodeling and simulation will be facilitated by the AR2GEMS platform using appropriate plug-ins. Instructor: Herve Gross.

Applied Geostatistics

This course covers the fundamentals of geostatistics from variogram modeling, kriging, cokriging as well as stochastic simulations with variograms and training images. The focus is on an intuitive understanding of the underlying concepts with the help of computer-based and classroom exercises using SGeMS. The strengths and limitations of the techniques are highlighted to provide a critical perspective and insight on when a method is appropriate for a given study. The difference between estimation map (kriging) and simulated realizations is given particular attention. The course can be anywhere from 2 to 5 days depending on the need of the host organization.

Multiple-point geostatistics

Training image-based geostatistics is profoundly changing spatial modeling. The traditional analytical nature of models, e.g. through variograms, is replaced with a more visual approach in the form of training images. The training image explicitly contains the patterns, or essentially, the multiple-point statistics (MPS), that are deemed relevant for the spatial phenomena under study. This course introduces the attendees to the theory, concept and practice of multiple-point geostatistics. Attendees learn MPS-algorithms through realistic 2D and 3D modeling problems using SGeMS. A strong emphasis is on understanding and using the algorithms in a real-world setting such that their capabilities and limitations are well understood. The course can be tailored from 1-4 days depending on the needs of your organization.

SGeMS – Users

This course is targeted for the geostatisticians who want to get more out of SGeMS. There is a strong emphasis on scripting and practical exercises. (1-5 days)

SGeMS – Developers

The aim of this course is to enable the participants to access the full power of SGeMS by understanding the design and the API. Exercises include programming of geostatistical algorithms, I/O filters, actions as well as widgets for the user interfaces. (3-5 days)

Recent training programs

  • December 6th-9th 2016: Machine Learning and Data Analysis for Geomodeling, UFRGS, Porto Alegre, Brazil.
  • April 18th-22nd 2016: Reservoir Simulation for Geomodelers, UFRGS, Porto Alegre, Brazil. Link to Syllabus
  • December 9th-13th 2013: Multiple-Point Geostatistics: Modeling with Training-Image Based Algorithms, UFRGS, Porto Alegre, Brazil.
  • December 2nd-6th 2013: Plugin development with SGeMS, UFRGS, Porto Alegre, Brazil.
  • August 14nd-16th 2013: Multivariate Geostatistics: Integrating Secondary Information, UFRGS, Porto Alegre, Brazil.
  • August 13nd-15th 2012: Mineral Resources Uncertainty Evaluation with Geostatistical Simulations, Brisbane, Australia.
  • April 30th-May 4th 2012: Quantitative Estimation of Reserves and Resources of Discovered Deposits, Daejeon, South Korea.
  • December 5th-9th 2011: Applied Geostatistics with SGeMS, UFRGS, Porto Alegre, Brazil.
  • December 12th-16th 2011: Multiple-Point Geostatistics: Modeling with Training-Image Based Algorithms, UFRGS, Porto Alegre, Brazil.
  • December 2010: Petroleum Geostatistics, BHP Petroleum, Houston, TX, USA.
  • November 2010: Applied Geostatistics with SGeMS, UFRGS, Porto Alegre Brazil.
  • November 2010: Multiple-Point Geostatistics: Modeling with Training Image-Based Algorithms, UFRGS, Porto Alegre, Brazil.
  • September 2010: Introduction to Geostatistics, Geoenv2010, Gent, Belgium.
  • September 2009: Multiple-point statistics, International Association of Mathematical Geosciences (IAMG), Stanford, CA, USA.
  • November 2008: Multiple-point statistics, Geostat 2008, Santiago, Chile.
  • April 2007: Introduction to SGeMS, University of Texas, Austin, TX, USA.
  • December 2006: Best-of-SCRF Petroleum Geostatistics, Waseda University, Tokyo, Japan.
  • April 2006: Introduction to SGeMS, Half-Moon Bay, CA, USA.

Stanford University Courses

  • EESS 213 Spatial Statistics and Analysis of Environmental data
  • GES/ENERGY 161 2007-2009 Statistical Methods for the Earth and Environmental Sciences - Geostatistics.
  • 2007-2010 GES/ENERGY 246, Reservoir Characterization and Flow Modeling with Outcrop Data.
  • 2008-2010 EESS 342, Seminar - Geostatistics.