Classification of petroleum well drilling operations using Support Vector Machine (SVM)
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/CIMCA.2006.66 http://hdl.handle.net/11449/70012 |
Resumo: | During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE. |
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Repositório Institucional da UNESP |
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Classification of petroleum well drilling operations using Support Vector Machine (SVM)Data reductionData storage equipmentPetroleum engineeringSupport vector machinesHydraulic parametersMud-logging systemOil well drillingDuring the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.SãO Paulo State University UNESP/IGCE/DEMAC, Avenida 24-A, 1515, CP.178, Rio Claro, SP - 13506-700State University of Campinas UNICAMP/FEM/DEP, CP.6122, Campinas, SP, 13083-970SãO Paulo State University UNESP/IGCE/DEMAC, Avenida 24-A, 1515, CP.178, Rio Claro, SP - 13506-700Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Serapião, Adriane B. S. [UNESP]Tavares, Rogério M.Mendes, José Ricardo P.Guilherme, Ivan R. [UNESP]2014-05-27T11:22:39Z2014-05-27T11:22:39Z2007-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/CIMCA.2006.66CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ....http://hdl.handle.net/11449/7001210.1109/CIMCA.2006.662-s2.0-3884911272769978143431898600000-0001-9728-7092Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ...info:eu-repo/semantics/openAccess2021-10-23T21:44:32Zoai:repositorio.unesp.br:11449/70012Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:13:28.640092Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
title |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
spellingShingle |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) Serapião, Adriane B. S. [UNESP] Data reduction Data storage equipment Petroleum engineering Support vector machines Hydraulic parameters Mud-logging system Oil well drilling |
title_short |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
title_full |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
title_fullStr |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
title_full_unstemmed |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
title_sort |
Classification of petroleum well drilling operations using Support Vector Machine (SVM) |
author |
Serapião, Adriane B. S. [UNESP] |
author_facet |
Serapião, Adriane B. S. [UNESP] Tavares, Rogério M. Mendes, José Ricardo P. Guilherme, Ivan R. [UNESP] |
author_role |
author |
author2 |
Tavares, Rogério M. Mendes, José Ricardo P. Guilherme, Ivan R. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Serapião, Adriane B. S. [UNESP] Tavares, Rogério M. Mendes, José Ricardo P. Guilherme, Ivan R. [UNESP] |
dc.subject.por.fl_str_mv |
Data reduction Data storage equipment Petroleum engineering Support vector machines Hydraulic parameters Mud-logging system Oil well drilling |
topic |
Data reduction Data storage equipment Petroleum engineering Support vector machines Hydraulic parameters Mud-logging system Oil well drilling |
description |
During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-12-01 2014-05-27T11:22:39Z 2014-05-27T11:22:39Z |
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/CIMCA.2006.66 CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies .... http://hdl.handle.net/11449/70012 10.1109/CIMCA.2006.66 2-s2.0-38849112727 6997814343189860 0000-0001-9728-7092 |
url |
http://dx.doi.org/10.1109/CIMCA.2006.66 http://hdl.handle.net/11449/70012 |
identifier_str_mv |
CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies .... 10.1109/CIMCA.2006.66 2-s2.0-38849112727 6997814343189860 0000-0001-9728-7092 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ... |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808129035764498432 |