Isatis.neo | Geostatistics made accessible
Go beyond the basics with the most comprehensive geostatistical software available.
Isatis.neo helps you explore, analyze, and visualize your spatial data with precision, creating accurate models, insightful maps, and in-depth uncertainty analyses to drive confident, data-backed decisions.
Why choose Isatis.neo
Simplify complex geostatistics — Isatis.neo transforms intricate geostatistical processes into streamlined workflows, enabling you to focus on insights rather than software complexities.
Tailored for your industry — Available in Standard, Mining, and Petroleum editions, Isatis.neo addresses specific industry needs with specialized tools. A preconfigured workflow enables efficient seismic time-to-depth conversion with comprehensive uncertainty analysis.
Leverage cutting-edge technology — With advanced algorithms and machine learning integration, Isatis.neo ensures high-performance analysis and modeling.
Key features
Intuitive interface — User-friendly design for efficient navigation and operation.
Comprehensive toolset — From exploratory data analysis to advanced simulations.
Automation & customization — Automate tasks and customize workflows with Python scripting.
High compatibility — Supports various data formats and integrates seamlessly with other software.
Robust reporting — Generate detailed reports with integrated word processing tools.
Industries We Serve
Mining — Enhance resource estimation and mine planning.
Oil & Gas — Improve reservoir modeling and uncertainty analysis.
Subsurface industries and geological surveys — Analyze subsurface conditions for construction or energy projects.
Bioresources (fish stocks or forest biomass) — Improve sustainable management.
Air Quality — Enhance model accuracy, human exposure models and risk analysis
"Without the Isatis sample clustering tool, small areas of extreme grades would not have been considered in the estimation, and planning would not have targeted them, reducing resources."
As part of a test carried out for Eiffage Génie Civil, Geovariances generated a subsurface model that was completely consistent with the one generated with their usual tools, with the added benefit of the geostatistical approach, speed and the arguments needed to justify it.
Dans le cadre d’un test réalisé pour Eiffage Génie Civil, Geovariances a généré un modèle de sous-sol complètement cohérent avec celui généré avec leurs outils habituels, avec en plus pour l'approche géostatistique, rapidité et arguments nécessaires à sa justification.
A top-ten iron producing company sought a solution to produce weekly resource models instead of monthly models to improve their decision process regarding the destination of the mined materials. Geovariances developed automated routines based on Isatis.neo batch and Python coding
Maxwell GeoSystems help their clients reduce risk and improve construction performance. They integrate geostatistics with Isatis.neo in their modeling process to quantify the uncertainty of their interpretation for a greater understanding of the ground conditions.
Hear from our customers
Discover Isatis.neo at our upcoming events
Training
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June 30, 2025Recoverable Resource Estimation by nonlinear geostatistics – Module 1: Uniform Conditioning
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July 1-2, 2025Recoverable Resource Estimation by nonlinear geostatistics – Module 2: Multiple Indicator Kriging and Conditional Expectation
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July 2-3, 2025Recoverable Resource Estimation by nonlinear geostatistics – Module 3: Simulations
Resources
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Isatis.neo – Reveal the true nature of the subsurface. Gain a reliable understanding of subsoil structure and heterogeneity | Integrate geostatistics into your modeling workflow to uncover deeper insights, reduce uncertainty, and make smarter, risk-informed decisions with confidence.
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Isatis.neo Standard Edition (version française)
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Isatis.neo Mining Edition
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Isatis.neo Standard Edition
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Conversions & Uncertainties Workflow – Isatis.neo Petroleum Edition
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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Enhancing environmental radionuclides tracking: High-resolution isotopic analysis using NanoSIMS | Presented at Goldschmidt Pragues 2025 - Louise Darricau (IRSN)
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Application de la géostatistique dans l’analyse de risque géotechnique lié à la liquéfaction du sol (vidéo) | Gestion des Données et Nouvel Environnement numérique en Géotechnique - Journée technique CFMS 15 nov 2022
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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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Utilisation d’un algorithme de classification par Machine Learning pour la caractérisation géomécanique des sols | CFMS 2020 - par Marie-Cecile Febvey
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Co-kriging of log ratios: a worked alternative method | Clint Ward, Cliffs, Ute Mueller ECU
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Coherent modeling of mineral grades and zones by coupling cokriging and Support Vector Machine | Authors: P. Masoudi, J. Langanay , R. Rolo - Presented at MMME’25 (www.international-aset.com)
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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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Mapping Groundwater Level by Geostatistical Methods: Ordinary Versus Universal Kriging; Alongside a Discussion on Neighbourhood | P. Masoudi (Geovariances), C. Faucheux (Geovariances), H. Binet (Geovariances) - 85th EAGE Annual Conference & Exhibition, Jun 2024, Volume 2024, p.1 - 5
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3D Modelling of Standard Penetration Test in the Framework of Assessing Liquefaction Risk | P. Masoudi (Geovariances), H. Binet (Geovariances), C. Simon (EDF-DI-TEGGM), B. Pelletier (EDF-DI-TEGGM), C. Faucheux (Geovariances), F. Rambert (Geovariances), Y. Assy (Geovariances) - EAGE GeoTech 2024 Fourth EAGE Workshop on Distributed Fibre Optic Sensing, Apr 2024, Volume 2024, p.1 - 5
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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