Comparação de métodos de Segmentação de Fraturas em Imagem Acústica de Perfilagem Petrofísica

Luciana Olivia Dias, Clécio R. Bom, Márcio P. de Albuquerque, Marcelo P. de Albuquerque, Elisângela L. Faria, Maury D. Correia

Resumo




A análise das imagens acústicas de poço tem como um de seus objetivos reconhecer e caracterizar fraturas naturais. A interpretação dessas imagens em conjunto com diversos outros dados e análises, oferecem um panorama para determinar a sua viabilidade de exploração de petróleo e gás em um reservatório. A grande quantidade de dados de um único poço faz com que a análise visual consuma tempo relevante de profissionais da área. Neste contexto, para auxiliar o desenvolvimento de métodos de reconhecimentos de padrões automatizados ou supervisionados para fraturas, avaliamos a performance de 5 métodos de segmentação: Gradiente Direcional, K-means, Otsu, Niblack e filtro Entropia Local. Para uma análise consistente, construímos um simulador de imagens acústicas com fraturas sintéticas representando diversos níveis de complexidade e varrendo o espaço de parâmetros do nosso modelo. Avaliamos a performance utilizando o percentual de sobreposição e correlação entre a segmentação e a região da fratura definida pela simulação. Em imagens sem ajuste de contraste para correção de iluminação o método K-means apresentou o melhor desempenho. No entanto, com utilização do ajuste de contraste os resultados com Otsu foram semelhantes ao K-means. O teste de hipótese unilateral à esquerda, para hipótese nula da média de correlação superior à 50%, a hipótese não foi rejeitada apenas pelos métodos K-means e Otsu, com valores respectivos de p ~ 0.60 e p ~ 0.61 dentro do intervalo σ.



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DOI: http://dx.doi.org/10.7437/nt-cbpf.v8i3.320