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Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

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HÉBERT, Vanessa, Thierry PORCHER, Valentin PLANES, Marie LÉGER, Anna ALPEROVICH, Bastian GOLDLUECKE, Olivier RODRIGUEZ, Souhail YOUSSEF, 2020. Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties. The 2019 International Symposium of the Society of Core Analysts (SCA 2019). Pau, Aug 26, 2019 - Aug 30, 2019. In: SCHEMBRE-MCCABE, Josephina, ed., Holger OTT, ed.. E3S Web of Conferences : The 2019 International Symposium of the Society of Core Analysts (SCA 2019). EDP Sciences, 01003. eISSN 2267-1242. Available under: doi: 10.1051/e3sconf/202014601003

@inproceedings{Hebert2020-02-05Digit-48873, title={Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties}, year={2020}, doi={10.1051/e3sconf/202014601003}, number={146}, publisher={EDP Sciences}, series={E3S Web of Conferences}, booktitle={E3S Web of Conferences : The 2019 International Symposium of the Society of Core Analysts (SCA 2019)}, editor={Schembre-McCabe, Josephina and Ott, Holger}, author={Hébert, Vanessa and Porcher, Thierry and Planes, Valentin and Léger, Marie and Alperovich, Anna and Goldluecke, Bastian and Rodriguez, Olivier and Youssef, Souhail}, note={Article Number: 01003} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/48873"> <dc:creator>Alperovich, Anna</dc:creator> <dc:contributor>Porcher, Thierry</dc:contributor> <dc:contributor>Planes, Valentin</dc:contributor> <dc:creator>Youssef, Souhail</dc:creator> <dc:contributor>Rodriguez, Olivier</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48873/3/Alperovich_2-u8ijrueg73ur2.pdf"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-03-02T09:14:07Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-03-02T09:14:07Z</dcterms:available> <dc:creator>Porcher, Thierry</dc:creator> <dcterms:abstract xml:lang="eng">To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.</dcterms:abstract> <dc:contributor>Youssef, Souhail</dc:contributor> <dc:creator>Goldluecke, Bastian</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Léger, Marie</dc:contributor> <dc:contributor>Hébert, Vanessa</dc:contributor> <dc:language>eng</dc:language> <dc:contributor>Goldluecke, Bastian</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:issued>2020-02-05</dcterms:issued> <dc:creator>Léger, Marie</dc:creator> <dc:creator>Planes, Valentin</dc:creator> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48873/3/Alperovich_2-u8ijrueg73ur2.pdf"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:rights>Attribution 4.0 International</dc:rights> <dc:creator>Hébert, Vanessa</dc:creator> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:contributor>Alperovich, Anna</dc:contributor> <dc:creator>Rodriguez, Olivier</dc:creator> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/48873"/> <dcterms:title>Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties</dcterms:title> </rdf:Description> </rdf:RDF>

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