Lithofacies Clustering Using Principal Component Analysis and Neural Network: Applications to Wireline Logs |
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| Authors: | Y Zee Ma |
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| Institution: | 1.Schlumberger Ltd.,Greenwood Village,USA |
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| Abstract: | Both statistical methods and artificial neural network (ANN) have been used for lithology or facies clustering. ANN, in particular,
has increasingly gained popularity for clustering of categorical variables as well as for predictions of continuous variables.
In this article, we discuss several counter examples that show deficiencies of these techniques when used for automatic lithofacies
clustering. Our examples show that the lithofacies clustered by ANN alone or ANN in combination with principal component analysis
(PCA), as commonly used, are highly inconsistent with the benchmark charts based on laboratory results. We propose several
techniques to overcome these problems and improve the clustering of lithofacies, including (1) classification of lithofacies
using the minor or intermediate principal component(s), (2) rotation of a principal component before using ANN for clustering,
(3) cascading two or more PCAs and ANNs for clustering lithofacies or electrofacies, and (4) classifying lithofacies with
demarcated stratigraphic reference classes. |
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