@inProceedings{
 title = {Automated facies prediction in drillholes using machine learning},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 id = {7375ddb5-4311-310d-a464-eaf9586be41c},
 created = {2018-04-26T13:37:12.329Z},
 file_attached = {false},
 profile_id = {922b15e4-5966-3e4c-9726-03ebdaa136b2},
 last_modified = {2018-04-26T13:37:12.329Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {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.},
 bibtype = {inProceedings},
 author = {Blouin, M. and Caté, A. and Perozzi, L. and Gloaguen, E.},
 booktitle = {79th EAGE Conference and Exhibition 2017 - Workshops}
}

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