Geostatistics for Mineral Resource Estimation

Geostatistics is the most efficient and powerful framework to characterise, estimate and manage your mineral resource.

Geologists or mining engineers can apply geostatistics at all stages of the mine life cycle: from exploration to development, production and even for site remediation. Geostatistics offers a wide range of methodologies adapted to all commodities and styles of deposits.

Geovariances’ scientific rigour, continuous innovation and geostatistical expertise guarantee the quality of your evaluations at different stages of the development of your projects (feasibility studies, bankable studies, desktop reviews, etc.).

HEAR FROM OUR CUSTOMERS

"J'ai beaucoup apprécié l'expertise en formation de Geovariances lors d'une première expérience d'un cours organisé à Institut des Sciences de la Terre à Dakar sur l'estimation des ressources minérales."


Abdoul Aziz Ndiaye, Directeur - Institut des Sciences de la Terre (UCAD), Sénégal
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"Minestis reconciles in a dynamic way the geological and the geostatistical block models. The software brings important innovations in data analysis, modeling, resource / reserve estimation and taking into account geological uncertainty."


Abdoul Aziz Ndiaye, Directeur - Institut des Sciences de la Terre (UCAD), Sénégal
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"Minestis structured workflow combined with Geovariances tailored support has made conditional simulations accessible to a broad range of users and increased their use."


Owen Herod, Group Geology Manager - Imerys, France
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"Minestis, excelentes ferramentas de geoestatística e eção de análise grupal, com uma incrível ferramenta que permite identificar e usar os agrupamentos amostrais homogêneos"


Marco Aurelio Perez do Nascimento, Managing Director - SRK Consultores do Brasil Ltda
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"Isatis maintenance contract gives us easy access to innovation which improve our day-to-day work and the possibility to guide the software developements according to our needs. Ore valorization, selective exploitation,..."


Monique Le Guen, Geology Department Manager Resources & Reserves - Eramet Nickel Division, France
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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

December 4, 2017

The search for productivity improvements is pervasive in the mining industry and Mineral Resource Estimation software are no strangers to ...

November 27, 2017

Geovariances is pleased to provide Isatis licenses to participants of Geostat2018 Workshop organized by the Wroclaw University of Science ...

August 24, 2017

Limited time! To celebrate the release 2017 of Minestis, we offer a free Minestis one-year subscription. Get yours now!

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Events

February 19-23, 2018

Join us for this unique opportunity to learn about the theoretical principles behind multivariate grade estimation, KNA and UC, and how th...

March 4-7, 2018

Stop by our booth #1330 to know more about our software solutions Minestis and Isatis.

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Resources

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Which block size for mineral resource estimation

A key aspect of mineral resource estimation (MRE) is the definition of the block dimensions used to estimate the deposit attributes.

A satisfactory compromise is to be found to get an estimate that allows making decisions upon volumes that are representative of the physical reality of the operation while being aware that the density of information available at the time of estimation probably does not warrant the direct estimation of such volumes.

Through this white paper, learn how to choose a relevant support size for mineral resource estimation.

Localized Multivariate Uniform Conditioning

Estimating tonnage and grade, from sparse data, at a mining scale resolution is a challenge. Uniform Conditioning (UC), provides a powerful approach to estimating recoverable resources at a local scale, i.e. predicting the local distributions of SMUs (selective mining units) within larger panels conditional to neighbouring information.

Through Geovariances long-lasting experience in applying UC (and now LMUC), learn how LMUC helps you optimise the accuracy of your predicted recoverable resource estimates and access the information you have available regarding recoveries predicted at the mining (SMU) scale.

Use of Simulations for Mining Applications

Linear Interpolation techniques like kriging are inappropriate to deal with issues that require a full characterisation of the spatial distribution. The simulations provide a huge flexibility to deal with the complexity of the mining process and an access to the uncertainty assessment.

Through Geovariances multiple experiences in developing successfully various simulation strategies in different environments (kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, alteration and hydrothermal type deposits), learn how geostatistical simulations can help in resource estimation and classification.

Geological Facies Simulations

Whatever the resource involved – oil & gas, coal or metallic resources – capturing the variability of the geological parameters is essential at the modelling stage as the characteristics of the distributions of key parameters conditioning the resource recovery (e.g. rock properties, grades, etc.) are informed by the geological context. A large variety of simulation techniques is available to model geological facies.

Through Geovariances strong experience in developing successfully simulation strategies for different geological environments (e.g. kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, hydrothermal type deposits, etc.), learn how to choose the best facies modelling technique according to the specific geological depositional environment. Analyse each method advantages and drawbacks.

Uncertainty of Mineral Resource Estimates From Confidence Intervals to Resource Classification

Resource classification methodologies are still under research and debate. Most of the time, ad hoc techniques, based on simple and easy to get criteria, are applied.

Hints and pitfalls of these methodologies are worth deeper thinking about. The probabilistic framework of geostatistics seems adapted to provide quantitative inputs to that process as it is particularly appropriate to assess uncertainty in resource models and thus appraise the risk.

Through this white paper, find out more about the geostatistics-based classification methodologies, their pros and cons.

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