Prediction of failure probability of oil wells

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...

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Principais autores: Carvalho, João B., Valença, Dione M., Singer, Julio M.
Formato: article
Idioma:pt_BR
Publicado em: Brazilian Statistical Association
Assuntos:
Accelerated failure time models
Correlated data
Empirical Bayes predictors
Empirical best linear unbiased predictors
Random effects models
Endereço do item:https://repositorio.ufrn.br/jspui/handle/123456789/27094
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spelling ri-123456789-270942019-05-26T05:18:42Z Prediction of failure probability of oil wells Carvalho, João B. Valença, Dione M. Singer, Julio M. Accelerated failure time models Correlated data Empirical Bayes predictors Empirical best linear unbiased predictors Random effects models 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. 2019-05-17T13:18:40Z 2019-05-17T13:18:40Z 2014 article 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 https://repositorio.ufrn.br/jspui/handle/123456789/27094 10.1214/12-BJPS206 pt_BR Acesso Aberto application/pdf Brazilian Statistical Association
institution Repositório Institucional
collection RI - UFRN
language pt_BR
topic Accelerated failure time models
Correlated data
Empirical Bayes predictors
Empirical best linear unbiased predictors
Random effects models
spellingShingle Accelerated failure time models
Correlated data
Empirical Bayes predictors
Empirical best linear unbiased predictors
Random effects models
Carvalho, João B.
Valença, Dione M.
Singer, Julio M.
Prediction of failure probability of oil wells
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.
format article
author Carvalho, João B.
Valença, Dione M.
Singer, Julio M.
author_facet Carvalho, João B.
Valença, Dione M.
Singer, Julio M.
author_sort Carvalho, João B.
title Prediction of failure probability of oil wells
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
publisher Brazilian Statistical Association
publishDate 2019
url https://repositorio.ufrn.br/jspui/handle/123456789/27094
work_keys_str_mv AT carvalhojoaob predictionoffailureprobabilityofoilwells
AT valencadionem predictionoffailureprobabilityofoilwells
AT singerjuliom predictionoffailureprobabilityofoilwells
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