Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorFonseca, Tiago C.
dc.creatorMendes, José Ricardo P.
dc.creatorSerapião, Adriane B. S.
dc.creatorGuilherme, Ivan R.
dc.date2014-05-27T11:22:20Z
dc.date2016-10-25T18:23:21Z
dc.date2014-05-27T11:22:20Z
dc.date2016-10-25T18:23:21Z
dc.date2006-12-01
dc.date.accessioned2017-04-06T01:22:57Z
dc.date.available2017-04-06T01:22:57Z
dc.identifierProceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, p. 152-157.
dc.identifierhttp://hdl.handle.net/11449/69409
dc.identifierhttp://acervodigital.unesp.br/handle/11449/69409
dc.identifier2-s2.0-56349150914
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/890661
dc.descriptionBit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.
dc.languageeng
dc.relationProceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectARX model
dc.subjectDrilling performance
dc.subjectNeural networks
dc.subjectPetroleum wells drilling
dc.subjectRate of penetration
dc.subjectArtificial intelligence
dc.subjectDrilling
dc.subjectForecasting
dc.subjectImage classification
dc.subjectIntelligent control
dc.subjectOffshore oil fields
dc.subjectOffshore oil wells
dc.subjectOil well drilling
dc.subjectOil well production
dc.subjectOil wells
dc.subjectPetroleum industry
dc.subjectPetroleum refineries
dc.subjectVegetation
dc.subjectVoltage control
dc.subjectArx models
dc.subjectBit performances
dc.subjectChallenging problems
dc.subjectDrilling bits
dc.subjectDrilling performances
dc.subjectDynamic Neural networks
dc.subjectInput signals
dc.subjectModeling problems
dc.subjectOffshore fields
dc.subjectPetroleum wells
dc.subjectPrice rises
dc.subjectRate of penetrations
dc.subjectWell planning
dc.subjectWell drilling
dc.titleApplication of ARX neural networks to model the rate of penetration of petroleum wells drilling
dc.typeOtro


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