Prediction of failure probability of oil wells
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/27094 |
Resumo: | We consider parametric accelerated failure time models with random effects to predict the probability of possibly correlated failures occurring in oil wells. In this context, we first consider empirical Bayes predictors (EBP) based on aWeibull distribution for the failure times and on a Gaussian distribution for the random effects.We also obtain empirical best linear unbiased predictors (EBLUP) using a linear mixed model for which the form of the distribution of the random effects is not specified. We compare both approaches using data obtained from an oil-drilling company and suggest how the results may be employed in designing a preventive maintenance program. |
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Carvalho, João B.Valença, Dione M.Singer, Julio M.2019-05-17T13:18:40Z2019-05-17T13:18:40Z2014CARVALHO, João B.; VALENÇA, Dione M.; SINGER, Julio M. Prediction of failure probability of oil wells. Brazilian Journal of Probability and Statistics , v. 28, n.2 p. 275-287, 2014. Disponível em:< https://projecteuclid.org/euclid.bjps/1396615441>. Acesso em: 06 dez. 2017.0103-0752https://repositorio.ufrn.br/jspui/handle/123456789/2709410.1214/12-BJPS206Brazilian Statistical AssociationAccelerated failure time modelsCorrelated dataEmpirical Bayes predictorsEmpirical best linear unbiased predictorsRandom effects modelsPrediction of failure probability of oil wellsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleWe consider parametric accelerated failure time models with random effects to predict the probability of possibly correlated failures occurring in oil wells. In this context, we first consider empirical Bayes predictors (EBP) based on aWeibull distribution for the failure times and on a Gaussian distribution for the random effects.We also obtain empirical best linear unbiased predictors (EBLUP) using a linear mixed model for which the form of the distribution of the random effects is not specified. We compare both approaches using data obtained from an oil-drilling company and suggest how the results may be employed in designing a preventive maintenance program.info:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTPredictionOfFailure_2014.pdf.txtPredictionOfFailure_2014.pdf.txtExtracted texttext/plain28960https://repositorio.ufrn.br/bitstream/123456789/27094/3/PredictionOfFailure_2014.pdf.txt07930ba8a842ff102c868b6164428785MD53THUMBNAILPredictionOfFailure_2014.pdf.jpgPredictionOfFailure_2014.pdf.jpgGenerated Thumbnailimage/jpeg1516https://repositorio.ufrn.br/bitstream/123456789/27094/4/PredictionOfFailure_2014.pdf.jpg1b0d8f1897e668fdb754892e5e9f6e91MD54ORIGINALPredictionOfFailure_2014.pdfPredictionOfFailure_2014.pdfapplication/pdf211693https://repositorio.ufrn.br/bitstream/123456789/27094/1/PredictionOfFailure_2014.pdfb0afa63a4b41e48993a59d58fbf3f4a6MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/27094/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/270942019-05-26 02:18:42.352oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2019-05-26T05:18:42Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Prediction of failure probability of oil wells |
title |
Prediction of failure probability of oil wells |
spellingShingle |
Prediction of failure probability of oil wells Carvalho, João B. Accelerated failure time models Correlated data Empirical Bayes predictors Empirical best linear unbiased predictors Random effects models |
title_short |
Prediction of failure probability of oil wells |
title_full |
Prediction of failure probability of oil wells |
title_fullStr |
Prediction of failure probability of oil wells |
title_full_unstemmed |
Prediction of failure probability of oil wells |
title_sort |
Prediction of failure probability of oil wells |
author |
Carvalho, João B. |
author_facet |
Carvalho, João B. Valença, Dione M. Singer, Julio M. |
author_role |
author |
author2 |
Valença, Dione M. Singer, Julio M. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Carvalho, João B. Valença, Dione M. Singer, Julio M. |
dc.subject.por.fl_str_mv |
Accelerated failure time models Correlated data Empirical Bayes predictors Empirical best linear unbiased predictors Random effects models |
topic |
Accelerated failure time models Correlated data Empirical Bayes predictors Empirical best linear unbiased predictors Random effects models |
description |
We consider parametric accelerated failure time models with random effects to predict the probability of possibly correlated failures occurring in oil wells. In this context, we first consider empirical Bayes predictors (EBP) based on aWeibull distribution for the failure times and on a Gaussian distribution for the random effects.We also obtain empirical best linear unbiased predictors (EBLUP) using a linear mixed model for which the form of the distribution of the random effects is not specified. We compare both approaches using data obtained from an oil-drilling company and suggest how the results may be employed in designing a preventive maintenance program. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014 |
dc.date.accessioned.fl_str_mv |
2019-05-17T13:18:40Z |
dc.date.available.fl_str_mv |
2019-05-17T13:18:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CARVALHO, João B.; VALENÇA, Dione M.; SINGER, Julio M. Prediction of failure probability of oil wells. Brazilian Journal of Probability and Statistics , v. 28, n.2 p. 275-287, 2014. Disponível em:< https://projecteuclid.org/euclid.bjps/1396615441>. Acesso em: 06 dez. 2017. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/27094 |
dc.identifier.issn.none.fl_str_mv |
0103-0752 |
dc.identifier.doi.none.fl_str_mv |
10.1214/12-BJPS206 |
identifier_str_mv |
CARVALHO, João B.; VALENÇA, Dione M.; SINGER, Julio M. Prediction of failure probability of oil wells. Brazilian Journal of Probability and Statistics , v. 28, n.2 p. 275-287, 2014. Disponível em:< https://projecteuclid.org/euclid.bjps/1396615441>. Acesso em: 06 dez. 2017. 0103-0752 10.1214/12-BJPS206 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/27094 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Brazilian Statistical Association |
publisher.none.fl_str_mv |
Brazilian Statistical Association |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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Universidade Federal do Rio Grande do Norte (UFRN) |
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UFRN |
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UFRN |
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Repositório Institucional da UFRN |
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Repositório Institucional da UFRN |
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