Image public
ELK stack with a database / indexation technique for quick searches / dashboards and views.
An essential step of risk assessment methodologies in subsurface activities is to simulate multiple possible realizations for the spatial distribution of the lithological facies.
Automatic extraction of images of interest in document database can improve the efficiency of operational workflows and help professionals save time for higher-value activities
Quantifying the uncertainties associated to well-log data can benefit any decision making based on machine learning workflows where these data are used
Predictive mapping is essential to evaluate underground prospectivity in various fields, such as Oil and Gas industry, Mineral prospection or Geothermal Energy
Predicting rock properties while drilling a well, especially if at several tens of meters ahead of the drill bit, can be key to reduce the drilling risks and their associated costs
The representation of geological objects in micromodels is often insufficiently realistic
Lithological interpretation of core samples is a decisive early stage of many geoscience workflows
Accurately detecting and locating a large number of objects of interest in thin section images is an arduous task
Quantitative analyzes of thin sections often imply tedious searches and counts of specific elements such as micro-fossils
Identification of lithological types from rock samples is cornerstone in many subsurface activities
Digital transformation
towards geosciences 4.0
