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Processamento Inteligente de Sinais de Press?o e Temperatura Adquiridos Atrav?s de Sensores Permanentes em Po?os de Petr?leo

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Previous issue date: 2012-02-06 / Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds
vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large
noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform.
Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite
satisfactory for offshore wells and matched real requisites for utilization / Originalmente voltadas ao monitoramento da opera??o, as medi??es cont?nuas de press?o e temperatura no fundo de po?o, realizadas atrav?s de PDGs (do ingl?s, Permanent Downhole Gauges), encontram vasta aplicabilidade no gerenciamento de reservat?rios. Para tanto, permitem o monitoramento do desempenho de po?os e a estimativa de par?metros de reservat?rios no longo prazo. Contudo, a despeito de sua inquestion?vel utilidade, os dados adquiridos de PDG apresentam grande conte?do de ru?do. Outro aspecto igualmente desfavor?vel reside na ocorr?ncia de valores esp?rios (outliers) imersos entre as medidas registradas pelo PDG. O presente trabalho aborda o tratamento inicial de sinais de press?o e temperatura, mediante t?cnicas de suaviza??o, mapas auto-organiz?veis e transformada wavelet discreta. Ademais, prop?e-se um sistema de detec??o de transientes relevantes para an?lise no longo hist?rico de registros, baseado no acoplamento entre clusteriza??o fuzzy e redes neurais feed-forward. Os resultados alcan?ados mostraram-se de todo satisfat?rios para po?os marinhos, atendendo a requisitos reais de utiliza??o dos
sinais registrados por PDGs

Identiferoai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/12970
Date06 February 2012
CreatorsPires, Paulo Roberto da Motta
ContributorsCPF:10749896434, http://lattes.cnpq.br/1987295209521433, Mata, Wilson da, CPF:09453210404, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4781404Z6, Machado, Marcos Vitor Barbosa, CPF:09930197411, http://lattes.cnpq.br/6630008146475338, Melo, Jorge Dantas de, D?ria Neto, Adri?o Duarte
PublisherUniversidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Ci?ncia e Engenharia do Petr?leo, UFRN, BR, Pesquisa e Desenvolvimento em Ci?ncia e Engenharia de Petr?leo
Source SetsIBICT Brazilian ETDs
LanguagePortuguese
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis
Formatapplication/pdf
Sourcereponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN
Rightsinfo:eu-repo/semantics/openAccess

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