In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements. The algorithm has been designed to work even with a relatively small training set and amount of features. The method is based on a gradient boosting classifier which demonstrated to be effective in such a circumstance. A key aspect of the algorithm is feature augmentation, which resulted in a significant boost in accuracy. The algorithm has been tested also through participation to the SEG machine learning contest.

A machine learning approach to facies classification using well logs

Bestagini, Paolo;Lipari, Vincenzo;Tubaro, Stefano
2017-01-01

Abstract

In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements. The algorithm has been designed to work even with a relatively small training set and amount of features. The method is based on a gradient boosting classifier which demonstrated to be effective in such a circumstance. A key aspect of the algorithm is feature augmentation, which resulted in a significant boost in accuracy. The algorithm has been tested also through participation to the SEG machine learning contest.
SEG Technical Program Expanded Abstracts 2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1063267
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