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Enriched Clustering Methodology for the Automated Interpretation of Electrofacies from Wireline Data
Clustering well-log data into electrofacies using machine learning with a purely unsupervised approach, cluster-number optimization, and outlier removal based on spatial autocorrelation.
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
Well logs interpretation is often rather subjective and highly time-consuming
It is often burdensome to handle large amounts of wells files
Digital transformation
towards geosciences 4.0
