An Intelligent System for Petroleum Well Drilling Cutting Analysis

Detalhes bibliográficos
Autor(a) principal: Marana, Aparecido Nilceu [UNESP]
Data de Publicação: 2009
Outros Autores: Chiachia, Giovani [UNESP], Guilherme, Ivan Rizzo [UNESP], Papa, João Paulo [UNESP], Miura, Kazuo, Ferreira, Marystela [UNESP], Torres, Francisco
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICAIS.2009.16
http://hdl.handle.net/11449/8299
Resumo: Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.
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spelling An Intelligent System for Petroleum Well Drilling Cutting AnalysisCutting analysispetroleum well drilling monitoringoptimum-path forestCutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.São Paulo State Univ UNESP, Dept Comp, High Performance Comp Lab, Bauru, BrazilSão Paulo State Univ UNESP, Dept Comp, High Performance Comp Lab, Bauru, BrazilInstitute of Electrical and Electronics Engineers (IEEE), Computer SocUniversidade Estadual Paulista (Unesp)Marana, Aparecido Nilceu [UNESP]Chiachia, Giovani [UNESP]Guilherme, Ivan Rizzo [UNESP]Papa, João Paulo [UNESP]Miura, KazuoFerreira, Marystela [UNESP]Torres, Francisco2014-05-20T13:25:58Z2014-05-20T13:25:58Z2009-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject37-42http://dx.doi.org/10.1109/ICAIS.2009.16Proceedings 2009 International Conference on Adaptive and Intelligent Systems, Icais 2009. Los Alamitos: IEEE Computer Soc, p. 37-42, 2009.http://hdl.handle.net/11449/829910.1109/ICAIS.2009.16WOS:00029070330000660277137509426899039182932747194Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings 2009 International Conference on Adaptive and Intelligent Systems, Icais 2009info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/8299Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:26:57.556843Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv An Intelligent System for Petroleum Well Drilling Cutting Analysis
title An Intelligent System for Petroleum Well Drilling Cutting Analysis
spellingShingle An Intelligent System for Petroleum Well Drilling Cutting Analysis
Marana, Aparecido Nilceu [UNESP]
Cutting analysis
petroleum well drilling monitoring
optimum-path forest
title_short An Intelligent System for Petroleum Well Drilling Cutting Analysis
title_full An Intelligent System for Petroleum Well Drilling Cutting Analysis
title_fullStr An Intelligent System for Petroleum Well Drilling Cutting Analysis
title_full_unstemmed An Intelligent System for Petroleum Well Drilling Cutting Analysis
title_sort An Intelligent System for Petroleum Well Drilling Cutting Analysis
author Marana, Aparecido Nilceu [UNESP]
author_facet Marana, Aparecido Nilceu [UNESP]
Chiachia, Giovani [UNESP]
Guilherme, Ivan Rizzo [UNESP]
Papa, João Paulo [UNESP]
Miura, Kazuo
Ferreira, Marystela [UNESP]
Torres, Francisco
author_role author
author2 Chiachia, Giovani [UNESP]
Guilherme, Ivan Rizzo [UNESP]
Papa, João Paulo [UNESP]
Miura, Kazuo
Ferreira, Marystela [UNESP]
Torres, Francisco
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Marana, Aparecido Nilceu [UNESP]
Chiachia, Giovani [UNESP]
Guilherme, Ivan Rizzo [UNESP]
Papa, João Paulo [UNESP]
Miura, Kazuo
Ferreira, Marystela [UNESP]
Torres, Francisco
dc.subject.por.fl_str_mv Cutting analysis
petroleum well drilling monitoring
optimum-path forest
topic Cutting analysis
petroleum well drilling monitoring
optimum-path forest
description Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.
publishDate 2009
dc.date.none.fl_str_mv 2009-01-01
2014-05-20T13:25:58Z
2014-05-20T13:25:58Z
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 http://dx.doi.org/10.1109/ICAIS.2009.16
Proceedings 2009 International Conference on Adaptive and Intelligent Systems, Icais 2009. Los Alamitos: IEEE Computer Soc, p. 37-42, 2009.
http://hdl.handle.net/11449/8299
10.1109/ICAIS.2009.16
WOS:000290703300006
6027713750942689
9039182932747194
url http://dx.doi.org/10.1109/ICAIS.2009.16
http://hdl.handle.net/11449/8299
identifier_str_mv Proceedings 2009 International Conference on Adaptive and Intelligent Systems, Icais 2009. Los Alamitos: IEEE Computer Soc, p. 37-42, 2009.
10.1109/ICAIS.2009.16
WOS:000290703300006
6027713750942689
9039182932747194
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings 2009 International Conference on Adaptive and Intelligent Systems, Icais 2009
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 37-42
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE), Computer Soc
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE), Computer Soc
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
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