Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/102939
Type: Artigo de evento
Title: Application Of Arx Neural Networks To Model The Rate Of Penetration Of Petroleum Wells Drilling
Author: Fonseca T.C.
Mendes J.R.P.
Serapiao A.B.S.
Guilherme I.R.
Abstract: Bit 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.
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Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-56349150914&partnerID=40&md5=7107786357ebec417c534d35d5d2f78e
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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