Isatis.neo | Geostatistics made accessible

Isatis.neo is a smart and powerful software solution in geostatistics. Featuring an intuitive user interface, it results from Geovariances’ dual commitment to developing breakthrough technology and making first-class 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, each version offers a preconfigured workflow for an optimized way to tackle classical although challenging issues:
Mineral Resource Estimation including Ore Control and Reconciliation for the Mining Edition,
Time-to-Depth Conversion with comprehensive uncertainty analysis for the Petroleum Edition. 

 

 

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 wide choice of proven and state-of-the-art statistical and geostatistical tools in a fully flexible package letting you design your own process to best address your specific issues. And if you need further analyses, Isatis.neo gives you access to the power of Python functionalities and coding through its Calculator to generate your own variables and functions.

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.

Optimize your process

Our software users have all different skill levels in geostatistics. This is why we wanted Isatis.neo to be partly workflow-driven to give you the best and optimized way to your objectives. We have developed business-oriented pre-configured workflows for that purpose:
Resources in Isatis.neo Mining Edition,
Conversions & Uncertainties in Isatis.neo Petroleum Edition.

Hear from our customers

"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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"I found Isatis.neo Resources Workflow extremely useful and helpful. I managed to produce high-level geostatistical models very quickly."


Ashley Brown, Manager, Resource Delineation - Beckman, Brown, & Associates
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"Isatis.neo was amazing; it did everything I could think of and prompted me to do things I hadn't thought of. I haven't come across any other geostatistics software which comes close to its functionality."


Matthew Graham-Ellison, Student -
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Geovariances is pleased to bring you a conversation with Daniel Guibal and Michael Cunningham, both long-time users of Isatis, now happy users of Isatis.neo. They recently used the software to provide a new JORC resource estimate of the Ausgold Katanning Gold Project.


Daniel Guibal | Mike Cunningham, Independent consultants - Condor Geostats Services | Sonny Consulting Services
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"O curso Prática em Geoestatística com uso do software Isatis.neo, promovido pela Universidade Federal do Rio Grande do Sul em parceia com a Geovariances, foi muito intenso e produtivo. Agradeço toda a equipe pelo suporte e pela qualidade do conteúdo."


Saulo da Silva Nunes, Geólogo de Exploração Pleno - Alcoa
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Discover Isatis.neo at our upcoming events

News

March 30, 2022

Geovariances se complace en organizar un curso dedicado exclusivamente a las estudiantes universitarias.

February 1, 2022

A Geovariances orgulha-se de ser parceira do Departamento de Engenharia de Minas da Universidade Federal do Rio Grande do Sul...

October 19, 2021

The Geostatistics Team from MINES ParisTech and Geovariances partner together to offer a new CFSG online, their high-level training progra...



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Events

Del 14 al 28 de septiembre
A Geovariances irá percorrer toda a América para mostrar o que há de melhor em geoestatística. Convidamos você a juntar-se a nós e conh...
From Sept. 26 to Oct. 3, 2022
Geovariances is to tour the American continent to showcase the best in geostatistics. We invite you to join us and discover what you'll be ab...
Del 1 al 27 de octubre de 2022
Geovariances va a recorrer el continente americano para mostrar lo mejor de la geoestadística. Le invitamos a unirse a nosotros y a descubri...
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Resources

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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.

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.

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