Isatis | the Reference software in geostatistics
Isatis is the most comprehensive and advanced general purpose geostatistics toolkit. First released 25 years ago, it is widely recognized as the reference geostatistical software solution.
Isatis addresses very different issues from various industries, enabling thorough data analysis and visualization, quality mapping, accurate resource estimation, advanced geomodeling, risk analysis. It offers a robust and sound geostatistical technology which is continuously improved, year after year.
Isatis connectivity with premier software packages makes it easy to integrate in your day-to-day workflow.
Get trusted results
Adopting Isatis means you take advantage of a tried and tested software solution by more than 5000 users from various industries, including mining, oil & gas and environment, but also a bunch of fields like hydrogeology, oceanography, forestry, agriculture or archeology. You enjoy efficient and reliable algorithms originating from the Center of Geostatistics (Mines ParisTech) and that have been benchmarked and constantly improved since Isatis 1st release.
Boost your productivity
You need to gain time with repetitive tasks. With Isatis, you benefit from an integrated environment in which all operations can be stored and reproduced in an automatic way. You are able to set up production workflows through scripting procedures that can be run in a click for a fast and easy updating of your models. Besides, Isatis optimized and parallelized algorithms make you save time on heavy computations.
Benefit from a leading technology
Your needs change over time. We are listening to your concerns and ideas and make Isatis continuously evolve. Each year, you enjoy innovative methodologies derived from our research consortia for enhanced geostatistical models. This is one reason why 95% of our customers renew their software maintenance contract year after year. Other ones are the top-level technical support provided and the free access to comprehensive technical resources.
Solve any problem, even the unusual ones
Your data is unique and your modeling objectives too. Isatis gives you access to the most exhaustive set of proven and state of the art geostatistical methodologies, with an impressive number of available kriging and simulations algorithms, giving you the means to address all your issues. And Isatis being a workflow-free flexible toolbox, it is easy for you to design your own process, standard or original.
Geovariances recently achieved a study for a top-ten Aluminum producing company which demonstrated that they could perform reliable resource classification with only 75% of the holes they are used to drill, resulting in a money saving of 25% for drilling campaigns.
The French Marine Environmental Science Laboratory LEMAR has used Isatis in the framework of a spatial epidemiology study aiming at analyzing the origin and spread of marine viral diseases in oysters, so that to identify the main risk factors at the regional scale.
Isatis advanced capabilities improve the global quality of reservoir models
Isatis was proved to be a key tool in the process of ore reserve estimation and auditing at CSN.
"LIAM, the INRS research laboratory led by professor Erwan Gloaquen, uses Isatis for nearly 10 years to generate quality 3D geological models."
Hear from our customers
Discover Isatis at our upcoming events
Resources
-
Isatis 2018 new features | Discover what's new in Isatis 2018
-
Flumy, Reservoir geological model for meandering channelized systems
-
3D Facies Proportions in Isatis
-
Isatis, Software de Geoestadística para una Mejor Estimación de Recursos Mineros
-
Isatis, La Geoestadística para Ingenieros Mineros y Geólogos
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.
-
Co-kriging of log ratios: a worked alternative method | Clint Ward, Cliffs, Ute Mueller ECU
-
Local Uncertainty Benchmarking – A coal case study | Written by C. Mawdesley, D. Barry, O. Bertoli and R. Saha
-
Sensitivity study of the estimation variance approximation of a quotient | Comparison with Conditional Simulations in the Mn Deposit of Bangombé (Gabon)
-
Key Functionalities new module Studio RM 2016 | Presented by Olivier Bertoli at the UC2016 event organized in the UK by Datamine
-
Fitting cross-variogram models and Pesky negative Eigen values | David Barry, Geovariances
-
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
-
Geostatiscal Analysis of Meandering Processes and Channel Migration: Case Study of Modern Analogues for the Long Nab Member, Scalby Formation, Yorkshire, UK | Example of Flumy use
-
Interpolation of GPS and Geological Data Using InSAR Deformation Maps: Method and Application to Land Subsidence in the Alto Guadalentín Aquifer (SE Spain) | Remote Sens. 2016, 8, 965; doi:10.3390/rs8110965 | www.mdpi.com/journal/remotesensing | Keywords: InSAR; aquifer; subsidence; interpolation; kriging with external drift
-
Updated Radiological Inventory of G1 Reactor Thanks to a Strengthened Data Processing | Y. Desnoyers (Geovariances), M. Da Costa (CEA), M. Magnin (CEA), V. Lerat (CEA) | Presented at WM2017 Conference
-
Spatial variability of soil nitrogen forms and the activity of N-cycle enzymes | J. Długosz, A. Piotrowska-Długosz