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Automated facies prediction in drillholes using machine learningNormal access

Authors: M. Blouin, A. Caté, L. Perozzi and E. Gloaguen
Event name: 79th EAGE Conference and Exhibition 2017 - Workshops
Session: WS01 Data Science for Geosciences
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701657
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 498.15Kb )
Price: € 20

Machine learning is a popular topic in geosciences at the moment. It allows the management and interpretation of data in quantities and varieties (number of variables) that a human being would not be able to achieve. Rock physical properties acquired along drillholes can be used to generate predictions about the nature and characteristics of the rock when wireline logging is taking place. In this paper, we investigate the accuracy of facies prediction using machine learning algorithms by automatically interpreting geological rock types along drillholes from rock physical properties. A data-processing workflow is proposed to enhance the prediction power of the geophysical measurements, a model calibration approach is outlined and predictions on test data are presented. Results show more than 80% of correspondence between the automated prediction and the geologist interpretation.

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