Training courses in mining, oil & gas and environmental geostatistics
Geovariances offers general or business-oriented geostatistics training available worldwide in various languages. Courses cover all aspects of geostatistics for resource estimation, contamination characterization, mapping, or risk analysis. We provide various training options, including live public, in-house, or online sessions, with standard content or customized to your team’s unique needs.
Short course training, like the one provided by Geovariances, is an excellent option to acquire relevant knowledge and practical skills quickly and efficiently. Geovariances also supports the online CFSG, the 10-week high-level training program in geostatistics or Cycle de Formation Spécialisée en Géostatistique (Specialized Training Cycle in Geostatistics) provided by the Center for Geosciences from Mine Paris – PSL. More info →
Our training courses are Qualiopi certified (a French national quality reference system). This recognition guarantees the quality of the training we deliver.
Choose the training best suited to your profile
Whatever your technical level or requirements, you will find a training adapted to your needs in our comprehensive course portfolio. Our courses range from short webinars and half-day introductory seminars to 3-5 day fundamental level training or advanced level workshops. Our programs are specifically tailored to your industry and your specific operational challenges and are conducted either in English, French, Spanish or Portuguese languages.
Develop the geostatistical skills you need
Our programs cover the whole geostatistics workflow, from data analysis and variography to advanced multivariate non-linear methods. With our courses, you can get a “hands-on experience” of the importance of key geostatistical concepts, rapidly develop valuable skills with our niche topics, improve your software practice or just get some insights on the added value of geostatistics. Training can be completed with mentoring for goal-oriented workshops.
Get your geostatistical training at the time you need it
You may pick up a public session from one of our catalogues. Or you can ask us to organize your specific training for you and your colleagues, privately and tailor-made to answer your unique requirements. You will then choose between instructor-led classroom covering 8 hours of content per day or live online solutions limited to 2 hours per session and 2 sessions per day. The choice is yours!
Learn from our experienced trainers
The skills you acquire with our experts help you build more reliable quantitative models and gain confidence in your results. Our trainers are senior geostatisticians, statisticians, geologists, mining or petroleum engineers adding up decades of experience in teaching geostatistics and carrying out consulting projects. They know your business and understand your specific challenges. They are also the ones who train the industry geostatistics experts.
Hear from our clients
Resources
Use of Simulations for Mining Applications
Linear interpolation techniques – such as kriging – are inappropriate for dealing with issues that require a full characterization of spatial distribution (for example, probability of exceeding a threshold, variability of a product per mining period, recoverable resources at various cut-offs, etc.).
Only conditional simulations reproduce the true variability of your orebody. They are flexible in their application to complex mining processes and uncertainty assessment.
Through Geovariances’ multiple experiences in developing a variety of simulation strategies in different environments: kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, alteration and hyd,rothermal type deposits, learn how geostatistical simulations can help in resource estimation and classification.
Hydrogeological Facies Modeling
Stochastic Methods for geological modeling and links with fluid flow simulations
Whatever the application domain – oil & gas production, aquifer pollution characterization, uranium production by lixiviation – characterizing the geological parameters and capturing their variability is essential to ensure realistic flow modeling…
Time to Depth Conversion
Time to depth conversion of geological surfaces is critical for structural model building. Quantifying the uncertainty attached to the conversion is also of primordial importance for assessing GRV uncertainties. Traditional velocity models used in time to depth conversion could benefit from geostatistical techniques used in data integration. The advantage of using geostatistical methods is that they fit the data in one step and allow quantifying the uncertainty attached to the prediction by mean of the generation of equiprobable realizations.
Through Geovariances long-lasting experience in geostatistical depth conversion studies, learn how geostatistics helps you improve the accuracy of your reservoir structural model and assess the uncertainties on surfaces.
Mapping with auxiliary data
Through this white paper, discover how you canimprove significantly map reliability and quality by incorporating various sources of information in the interpolation process.
This document details the different methods for assimilating various sources of information, taking into account the reliability of each source and how the uncertainty associated with any mapping result can be estimated and reduced.
Data Quality Control
Before starting an evaluation of the contamination leading to decision-making tools for site clean-up, it is essential to ensure data quality. Thus, based on solid and reliable foundations, uncertainties occurring during the different steps of the decision process will be better controlled and the final quality of the project improved.
The objective of this white paper is to assess at best the validity of the data through visual, statistical and geostatistical basic tools before going further into their analysis.
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Geostatistical mapping of a contaminated riverbank in Norway – ICRER 2022 | ICRER 2022 - T. K. Eriksen (Institute for Energy Technology, NUK Kjeller, Norway) and Y. Desnoyers (Geovariances)
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Geostatistical mapping of two nuclear sites in Norway – ICRER 2022 | ICRER 2022 - M. Bourgeaux-Goget (IFE) and Y. Desnoyers (Geovariances)
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Characterization of Radioactively Contaminated Land – IAEA Nuclear Back End Webinar Series 3.5
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Methodology for the Estimation of Contaminated soil by a Geostatistics-based method | WM2023 - E. Moreno (CEA Marcoule), Y. Desnoyers (Geovariances), I. Bisel (CEA Marcoule) and P. Marty (CEA Marcoule)
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Amélioration de l’estimation des teneurs en alumine d’un gisement de kaolin à l’aide des simulations par bandes tournantes | XVe Journées de géostatistique 2021 - V. Bouchet (Imerys), M.C. Febvey (Geovariances), Hélène Binet (Geovariances), Armand Dubus (Geovariances)
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Using multiple-point geostatistics for geomodeling of a vein-type gold deposit | A. Zhexenbayeva, N. Madani, (School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan), P. Renard, J. Straubhaar (Stochastic Hydrogeology Group, University of Neuchatel, Neuchatel, Switzerland) - Applied Computing and Geosciences 23 (2024)
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Plurigaussian conditional simulation (PGS) of the Budenovskoe uranium roll-front deposit, central Kazakhstan: 3D model of the host sedimentary sequence | M. Abzalov, D. Renard - Applied Earth Science. 2023;132(3-4):227-235
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Assessment of ambient dose equivalent rate distribution patterns in a forested-rugged terrain using field-measured and modeled dose equivalent rates | M. Yasumiishi, P. Masoudi, T. Nishimura, K. Ochi, X. Ye, J. Aldstadt, M. Komissarov - Radiation Measurements, Volume 168, 2023, 106978, ISSN 1350-4487, https://doi.org/10.1016/j.radmeas.2023.106978.
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Uncertainty analyses for short-term planning and quality control applied to Capitão do Mato e Galinheiro mines – MG | Giulia Marina Cerqueira Dias, Dissertação de Mestrado, Instituto de Geociências, Recursos Minerais e Meio Ambiente, São Paulo, 2023
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Fuzzy membership function for weighting pairs in variographical analysis | P. Masoudi, Spatial Statistics, Volume 52, December 2022, 100717, ISSN 2211-6753, https://doi.org/10.1016/j.spasta.2022.100717