Isatis.neo | Geostatistics made accessible

Isatis.neo is the leading and most comprehensive software solution for geostatistics. Featuring an intuitive user interface, it results from Geovariances’ dual commitment to developing breakthrough technology and making premium geostatistics accessible to more users.

Isatis.neo

Designed for every business dealing with spatialized data, Isatis.neo exceeds industry standards in geostatistics. The software enables thorough data analysis and visualization, produces high-quality maps and models, and allows you to carry out extensive uncertainty and risk analyses that optimize your decision-making process.

Available in a Standard Edition, Isatis.neo is also offered in two special versions, Petroleum Edition and Mining Edition, to better meet the specific requirements of these two industries. In addition to business-oriented tools, the Petroleum Edition offers a preconfigured workflow for Time-to-Depth Conversion with comprehensive uncertainty analysis. 

 

 

Improve your performance

Geostatistics can appear daunting if you are not familiar with the approach. This is why we have been working to make Isatis.neo straightforward to use so that you only focus on your geostatistical analysis, not on how to use the tool. Your performance is boosted thanks to its intuitive interface, but also by cutting-edge parallelized algorithms and powerful scripting procedures that allow fast and easy model updating.

Tailor your project to your needs

Isatis.neo provides a complete set of powerful and intuitive statistical and geostatistical tools in a fully flexible package letting you design your process to best address your specific issues. And if you need further analyses, Isatis.neo enables you to write python coding into your batch processes, allowing for a high degree of customization required for optimized workflows.

Make better decisions

Isatis.neo makes you benefit from Geovariances’ technical excellence in geostatistics. The software derives from robust, tried and tested Isatis software and 35 years of know-how in developing geostatistics-based software solutions in partnership with the French Mining School of Paris. With Isatis.neo, you are certain to hold the keys for data and risk-informed decision making.

Go beyond with Machine Learning

With Isatis.neo, you may combine geostatistics with Machine Learning techniques to solve real and complex problems.

Hear from our customers

"I use Isatis.neo to validate block models generated with other software. I also like its specific tools, such as Flattening, which I used to rotate a vein and allowed me to increase kriging efficiency from 40% to 70%."


Antonio Umpire, Unit Manager Group Resource Estimation & Reporting - SIBANYE-STILLWATER
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"He vuelto a encantarme con Isatis.neo, conociendo sus herramientas cada vez más poderosas en simulaciones y con una grafica mejorada /// I am happy with Isatis.neo's continued performance. I have found its simulation tools to be increasingly powerful, with improved graphics."


Ricardo Líbano Granada, Geólogo Senior de Recursos - Antofagasta Minerals
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“O recurso mineral de 2023, estimado pelo Isatis.neo, aumentou Life of Mine e o valor dos ativos minerais da EuroChem /// The 2023 mineral resource, estimated through Isatis.neo, increased Life of Mine and Eurochem's mineral asset value.”


Rodrigo De Andrade Miotto, Specialist Geologist - Mining resources - EuroChem
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"Con Isatis.neo, usted puede implementar rápidamente flujos de trabajo en proyectos con múltiples dominios y variables a modelar /// With Isatis.neo, you can quickly implement workflows in projects involving multiple domains and variables."


Sergio Igancio Salinas Rozas, Geologist – Geostatistician - GeoEstima
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"I have used Isatis.neo full on for a big multi-domain multi-element model from compositing to reporting, using gaussian and raw, multiple block sizes, etc. and I’m very impressed. Fast migrations, fast estimation, good reporting, validation, and visualization functionality."


Danny Kentwell, Principal Consultant (Resource Evaluation) - SRK Consulting
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Discover Isatis.neo at our upcoming events

News

May 7, 2024

Join Roberto Rolo's talks and workshops to learn about Isatis.neo's features like facies and grade simulation and Python-based workflow sc...

February 20, 2024

Explorez le potentiel de la géostatistique pour l'estimation et la classification des ressources minérales. Découvrez les meilleures pr...

January 26, 2024

Enroll in CFSG, the online specialized training cycle in mining geostatistics, learn from the mining geostatistics experts and acquire the...



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Events

28 mai 2024, 11h
Découvrez si la géostatistique est faite pour vous lors de ce webinaire illustrant son utilité dans divers domaines tels que sites pollué...
June 10-13, 2024
Join us in a talk comparing ordinary and universal kriging in mapping water elevation surfaces. Stop by booth #DTA116 and explore our geostat...
June 18-21, 2024
Join us for a talk on combining geological, geotechnical, and geophysical data and other site investigation data to cost-effectively predict ...
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Resources

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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.

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.

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