Pattern Analysis in Drilling Reports using Optimum-Path Forest

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dc.contributor.authorSousa, G. J. [UNESP]
dc.contributor.authorPedronette, D. C.G. [UNESP]
dc.contributor.authorBaldassin, A. [UNESP]
dc.contributor.authorPrivatto, P. I.M. [UNESP]
dc.contributor.authorGaseta, M. [UNESP]
dc.contributor.authorGuilherme, I. R. [UNESP]
dc.contributor.authorColombo, D.
dc.contributor.authorAfonso, L. C.S. [UNESP]
dc.contributor.authorPapa, J. P. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionPetróleo Brasileiro S.A. - Petrobras
dc.date.accessioned2019-10-06T15:24:16Z
dc.date.available2019-10-06T15:24:16Z
dc.date.issued2018-10-10
dc.description.abstractWell drilling monitoring is an essential task to prevent faults, save resources, and take care of environmental and eco-planning businesses. During drilling, it is required that staff fill out a log to keep track of the activities that are currently occurring. With such data analyzed and processed, it is possible to learn how to prevent faults and take corrective actions in realtime. However, the most important information is usually stored in a free-text format, thus complicating the task of automated text mining. In this work, we introduce the Optimum-Path Forest (OPF) for sentence classification in drilling reports and compare its results against some state-of-art results. We show that OPF combined with text-based features are a compelling source to learn patterns in drilling reports.en
dc.description.affiliationInstitute of Geosc. And Exact Sciences UNESP - São Paulo State University
dc.description.affiliationCenpes Petróleo Brasileiro S.A. - Petrobras
dc.description.affiliationSchool of Sciences UNESP - São Paulo State University
dc.description.affiliationUnespInstitute of Geosc. And Exact Sciences UNESP - São Paulo State University
dc.description.affiliationUnespSchool of Sciences UNESP - São Paulo State University
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2018.8489232
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks, v. 2018-July.
dc.identifier.doi10.1109/IJCNN.2018.8489232
dc.identifier.scopus2-s2.0-85056558107
dc.identifier.urihttp://hdl.handle.net/11449/187061
dc.language.isoeng
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectDrilling report
dc.subjectOptimum-Path Forest
dc.subjectPetroleum Engineering
dc.titlePattern Analysis in Drilling Reports using Optimum-Path Foresten
dc.typeTrabalho apresentado em evento
unesp.author.lattes4738829911864396[3]
unesp.author.orcid0000-0001-8824-3055[3]

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