Evolving Neural Conditional Random Fields for drilling report classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorCenpes - Petróleo Brasileiro S.A.-
Autor(es): dc.creatorRibeiro, Luiz C.F. [UNESP]-
Autor(es): dc.creatorAfonso, Luis C.S.-
Autor(es): dc.creatorColombo, Danilo-
Autor(es): dc.creatorGuilherme, Ivan R. [UNESP]-
Autor(es): dc.creatorPapa, João P. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:33:31Z-
Data de disponibilização: dc.date.available2022-02-22T00:33:31Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.petrol.2019.106846-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201430-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201430-
Descrição: dc.descriptionOil and gas prospecting is an important economic activity, besides being expensive and quite complex, thus requiring close monitoring to avoid work accidents and mainly environmental damages. An essential source of information concerns the daily drilling reports that contain operations technical interpretations and additional information from rig sensors. However, only a few works have focused on mining textual information from such reports for providing intelligent-based decision-making mechanisms to aid safety and efficiency concerns in drilling operations. This work proposes a contextual-driven approach based on Recurrent Neural Networks to recognize events in drilling reports that can outperform other related techniques. We also introduce a novel approach based on evolutionary computing to combine partially trained models using cyclical learning rates. Experiments conducted on two unbalanced datasets provided by Petrobras (Petróleo Brasileiro S.A.) show that our model improved Macro-F1 scores over the baseline by more than 47%. Besides, the proposed ensembling technique further enhanced these values by another 3% in the best scenario. Such promising results can shed light over new research directions in the field.1-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionUFSCar - Federal University of São Carlos Department of Computing-
Descrição: dc.descriptionCenpes - Petróleo Brasileiro S.A.-
Descrição: dc.descriptionUNESP - São Paulo State University Inst. of Geosciences and Exact Sciences-
Descrição: dc.descriptionUNESP - São Paulo State University School of Sciences-
Descrição: dc.descriptionUNESP - São Paulo State University Inst. of Geosciences and Exact Sciences-
Descrição: dc.descriptionFAPESP: #2013/07375-0-
Descrição: dc.descriptionPetrobras: #2014/00545-0-
Descrição: dc.descriptionFAPESP: #2014/12236-1-
Descrição: dc.descriptionFAPESP: #2016/19403-6-
Descrição: dc.descriptionCNPq: #307066/2017-7-
Descrição: dc.descriptionCNPq: #427968/2018-6-
Idioma: dc.languageen-
Relação: dc.relationJournal of Petroleum Science and Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConditional Random Fields-
Palavras-chave: dc.subjectDrilling reports classification-
Palavras-chave: dc.subjectNatural Language Processing-
Título: dc.titleEvolving Neural Conditional Random Fields for drilling report classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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