Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/104272
Type: Artigo de evento
Title: Classification Of Petroleum Well Drilling Operations Using Support Vector Machine (svm)
Author: Serapiao A.B.S.
Tavares R.M.
Mendes J.R.P.
Guilherme I.R.
Abstract: 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.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/CIMCA.2006.66
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-38849112727&partnerID=40&md5=242b10ecb46d3c3fa2dcd9c6be542c60
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-38849112727.pdf241.84 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.