Consulting and mentoring services based on high level geostatistics
Geovariances provide consulting services internationally, mainly to the Mining, upstream Oil and Gas, Nuclear Decommissioning and Contaminated Sites industries, but not only. We conduct geostatistics-based studies including sub-surface or oil reservoir modeling, mineral or life resource evaluation, contamination or any soil property mapping, sampling optimization or risk analysis. We are also able to combine our high expertise and experience to respond to unusual issues our clients bring to us.
Unlock the situation and save time
Our consultants have industry experience addressing your challenges and specific issues as well as, being experts in geostatistics, are able to find innovative solutions to address the problems you face. They perfectly know the advanced techniques which will benefit your project.
Be more confident with the results
Our consultants deliver relevant, practical and robust consulting solutions. Our software expertise helps us wisely choose the most suitable methods and tools to process your data properly, deliver controlled and safer models and optimize the results which help your decision making process. No technical, black box answers that you cannot own or implement – only practical, well documented, tailored solutions.
Get quickly the service you need
At your offices or in our premises, remotely or on-site, on an ad-hoc basis or through long-term commitment, our versatile team of consultants is ready to provide immediate flexibility and reactivity to develop efficient geostatistical solutions focused on answering your problems. Running site-specific, specially focused mentoring sessions is one of their specialties.
Feel reassured about your workflows
Our consulting team is committed to helping you whatever your level in geostatistics in order to identify the best solution to your issues. They help you implement appropriate geostatistical approaches, improve your workflows, gain productivity, integrate our software solutions seamlessly with your available software solutions or quantify the benefit of advanced quality control and modeling methodologies.
Hear from our clients
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.
Data Quality Control
Before starting an evaluation of the contamination leading to decision-making tools for site clean-up, it is essential to ensure data quality. Thus, based on solid and reliable foundations, uncertainties occurring during the different steps of the decision process will be better controlled and the final quality of the project improved.
The objective of this white paper is to assess at best the validity of the data through visual, statistical and geostatistical basic tools before going further into their analysis.
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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Contributions of the CETAMA working group n°10 on sampling and radiological characterisation (poster) | Anne Courtadon et Danièle Roudil, CEA Marcoule; Yvon Desnoyers, Geovariances; Didier Dubot, CEA; Guy Granier, IRSN; Catherine Ollivier-Dehaye, EDF
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Key Functionalities new module Studio RM 2016 | Presented by Olivier Bertoli at the UC2016 event organized in the UK by Datamine
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Geostatistical study to support sustainable remediation of a site with historical lead impact (poster) | Claire FAUCHEUX, Nicolas JEANNEE, Perrine MARTIN , Maarten CUYPERS, Annelies JACOBS
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Geostatistical study supporting cost-effective remediation of a site with historical lead impact (presentation) | Maarten Cuypers, Annelies Jacobs, Perrine Martin Nicolas Jeannée, Claire Faucheux
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Geological interpretation of lithofacies border effects curves for the Wallon Subgroup, northeastern Surat Basin, QLD, Australia | Presented at Geostats Rendezvous - Perth 2013 - Feb. 2013 by S. Hamilton (University of Queensland), N. Desassis (Mines ParisTech)
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Use case 3: post accidental site remediation − CEA | EPJ Nuclear Sci. Technol. 6, 13 (2020) | Yvon Desnoyers and Claire Faucheux, Geovariances; and Nadia Pérot, CEA Cadarache
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Development of a user-friendly guideline for data analysis and sampling design strategy | EPJ Nuclear Sci. Technol. 6, 16 (2020) | Yvon Desnoyers, Geovariances and Bart Rogiers, SCK.CEN Belgian Nuclear Research Centre
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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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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
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Cetama working group n°10 contributions on sampling and radio-logical characterisation (Technical article) | Anne Courtadon et Danièle Roudil, CEA Marcoule; Yvon Desnoyers, Geovariances; Didier Dubot, CEA; Guy Granier, IRSN; Catherine Ollivier-Dehaye, EDF