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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.
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.
Well correlations can be partially automated to efficiently consider multiple scenarios and better assess the corresponding uncertainties.
Well correlations can be partially automated to efficiently consider multiple scenarios and better assess the corresponding uncertainties.
Quantifying the uncertainties associated to well-log data can benefit any decision making based on machine learning workflows where these data are used
Quantifying the uncertainties associated to well-log data can benefit any decision making based on machine learning workflows where these data are used
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
For mature fields, a long history of production data is often available, but the impact of geological factors remains hard to assess
For mature fields, a long history of production data is often available, but the impact of geological factors remains hard to assess
Well logs interpretation is often rather subjective and highly time-consuming
Well logs interpretation is often rather subjective and highly time-consuming
It is often burdensome to handle large amounts of wells files
It is often burdensome to handle large amounts of wells files
Digital transformation
towards geosciences 4.0
