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
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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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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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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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Kartotrak, software solution for contaminated site and soil characterization | Kartotrak, software solution for contaminated site and soil characterization
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Geostatistical deconvolution to find and locate punctual sources | Geostatistical deconvolution to find and locate punctual sources
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Non-Destructive identification of silt-clay layers on borehole core logs in PVC liners | Clay Conference 2022 - Non-Destructive identification of silt-clay layers on borehole core logs in PVC liners
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Full paper: On measuring the spatial sampling density of a deposit for mineral resource classification | Full paper - On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard
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Poster: On measuring the spatial sampling density of a deposit for mineral resource classification | On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard