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|Type:||Artigo de periódico|
|Title:||Comparison Between The Maf And Pca Multivariate Techniques Apply In Classification Of Electrofacies [comparação Entre As Técnicas Multivariadas Maf E Pca Aplicadas Na Classificação De Eletrofácies]|
|Abstract:||A vast amount of data is obtained during the development of a petroleum field. Seismic data, well logs, core and production data, all contribute to a better reservoir characterization and modeling. Several methods of multivariate data analysis can be used to support its interpretation, helping in important tasks as the identification of lithological facies. The most used and widely known of those methods is Principal Component Analysis (PCA) which intends to reduce data dimension while keeping as much as possible of their variance. Data dimension reduction can also be performed with the method of Maximum Autocorrelation Factors (MAF) which seeks to keep the spatial autocorrelation in data. In this work both methods were applied to data from well logs of the Namorado field, testing their performances in the classification of electrofacies. Following data dimension reduction, supervised classification methods known as k-nearest neighbors (k-NN) and weighted k-nearest neighbors (wk-NN) were applied, and the results obtained were compared by cross-validation. MAF showed to be more efficient than PCA in reducing data dimension, while keeping relevant information. The wk-NN performed a little better in classifying electrofacies than the usual k-NN. According to these results, the combination of MAF and wk-NN can be a valuable tool for classifying the facies of uncored wells from their logs. © 2011 Sociedade Brasileira de Geofísica.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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