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An essential step of risk assessment methodologies in subsurface activities is to simulate multiple possible realizations for the spatial distribution of the lithological facies.
An innovative method to automatically generate maps of prospectivity, based on Machine Learning and multicriteria decision analysis.
A web-based solution, which transforms cement bond logs images into sets of values and interprets them in terms of cement quality in a semi-automatic fashion.
Well correlations can be partially automated to efficiently consider multiple scenarios and better assess the corresponding uncertainties.
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
Document classification is one of the major parts of the manual effort, especially when the documents to classify are scattered within a huge database
Lithological interpretation of core samples is a decisive early stage of many geoscience workflows
Field trips are increasingly limited by budget, safety, footprint or inclusion issues
Field data management remains mainly paper-based and is time-consuming
Efficiently searching for relevant information within mass of unstructured data is often a time-consuming prerequisite of scientific tasks
Companies often accumulate very large amounts of documents stored in multiple folders
Well logs interpretation is often rather subjective and highly time-consuming
It is often burdensome to handle large amounts of wells files
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
