Pattern Analysis in Drilling Reports using Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/208923 |
Resumo: | Well 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 real-time. 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. |
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Pattern Analysis in Drilling Reports using Optimum-Path ForestOptimum-Path ForestDrilling reportPetroleum EngineeringWell 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 real-time. 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.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)PetrobrasFundação para o Desenvolvimento da UNESP (FUNDUNESP)UNESP Sao Paulo State Univ, Inst Geosc & Exact Sci, Rio Claro, SP, BrazilPetroleo Brasileiro SA Petrobras, Cenpes, Rio De Janeiro, RJ, BrazilUNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, BrazilUNESP Sao Paulo State Univ, Inst Geosc & Exact Sci, Rio Claro, SP, BrazilUNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, BrazilCNPq: 307066/2017-7CNPq: 308194/2017-9CNPq: 306166/2014-3FAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2016/19403-6Petrobras: 2014/00545-0FUNDUNESP: 2597.2017IeeeUniversidade Estadual Paulista (Unesp)Petroleo Brasileiro SA PetrobrasSousa, G. J. [UNESP]Pedronette, D. C. G. [UNESP]Baldassin, A. [UNESP]Privatto, P. I. M. [UNESP]Gaseta, M. [UNESP]Guilherme, I. R. [UNESP]Colombo Cenpes, D.Afonso, L. C. S. [UNESP]Papa, J. P. [UNESP]IEEE2021-06-25T11:43:17Z2021-06-25T11:43:17Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject82018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2018.2161-4393http://hdl.handle.net/11449/208923WOS:000585967402022Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2018 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2021-10-23T19:23:24Zoai:repositorio.unesp.br:11449/208923Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:46:31.078357Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
title |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
spellingShingle |
Pattern Analysis in Drilling Reports using Optimum-Path Forest Sousa, G. J. [UNESP] Optimum-Path Forest Drilling report Petroleum Engineering |
title_short |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
title_full |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
title_fullStr |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
title_full_unstemmed |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
title_sort |
Pattern Analysis in Drilling Reports using Optimum-Path Forest |
author |
Sousa, G. J. [UNESP] |
author_facet |
Sousa, G. J. [UNESP] Pedronette, D. C. G. [UNESP] Baldassin, A. [UNESP] Privatto, P. I. M. [UNESP] Gaseta, M. [UNESP] Guilherme, I. R. [UNESP] Colombo Cenpes, D. Afonso, L. C. S. [UNESP] Papa, J. P. [UNESP] IEEE |
author_role |
author |
author2 |
Pedronette, D. C. G. [UNESP] Baldassin, A. [UNESP] Privatto, P. I. M. [UNESP] Gaseta, M. [UNESP] Guilherme, I. R. [UNESP] Colombo Cenpes, D. Afonso, L. C. S. [UNESP] Papa, J. P. [UNESP] IEEE |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Petroleo Brasileiro SA Petrobras |
dc.contributor.author.fl_str_mv |
Sousa, G. J. [UNESP] Pedronette, D. C. G. [UNESP] Baldassin, A. [UNESP] Privatto, P. I. M. [UNESP] Gaseta, M. [UNESP] Guilherme, I. R. [UNESP] Colombo Cenpes, D. Afonso, L. C. S. [UNESP] Papa, J. P. [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Optimum-Path Forest Drilling report Petroleum Engineering |
topic |
Optimum-Path Forest Drilling report Petroleum Engineering |
description |
Well 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 real-time. 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. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2021-06-25T11:43:17Z 2021-06-25T11:43:17Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2018. 2161-4393 http://hdl.handle.net/11449/208923 WOS:000585967402022 |
identifier_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 8 p., 2018. 2161-4393 WOS:000585967402022 |
url |
http://hdl.handle.net/11449/208923 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2018 International Joint Conference On Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
8 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128414654136320 |