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.
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.
"Conversions & Uncertainties Workflow is an operational tool that can eventually add value to our portfolio of prospects thanks to robust, reliable and easy-to-compare methodology. That is really a huge improvement."
Hear from our customers
Discover Isatis.neo at our upcoming events
Events
Resources
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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Factorial kriging for estimating and mapping the geochemical background from in situ gamma dose rate measurements downstream of a former uranium mine | Journal of Environmental Radioactivity Volume 237, October 2021, 106681
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Utilisation d’un algorithme de classification par Machine Learning pour la caractérisation géomécanique des sols | CFMS 2020 Marie-Cecile Febvey Utilisation d'un algorithme de classification par machine learning pour la caractérisation géomecanique.pdf
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Spatial variability of soil nitrogen forms and the activity of N-cycle enzymes | J. Długosz, A. Piotrowska-Długosz
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Space-Temporal Variation in Underground Water Some Quality Parameters in Klodzko Water Intake Area Using Statistical and Geostatistical Methods (SW Part of Poland)
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Mapping with Auxiliary Data of Varying Accuracy