Lithofacies Identification from Wireline Logs
Neural-network classifiers, of the multi-layered perceptron type, were trained to identify lithofacies (rock types) from wireline logs in two ways: (1) by a semi-autonomous method based on pre-segmented log intervals and (2) by an autonomous method based on non-segmented data. Good results were achieved in reservoirs from two geologically different environments (siliciclastics and carbonates) without having to fine-tune the network parameters. The performance was substantially better than that of linear discriminant and Gaussian classifiers. The standard neural-network algorithm has been modified to increase its robustness e. g. to over-training. A user-friendly neural-network module has been embedded in an existing computing environment to facilitate the use of neural networks by formation analysts.
KeywordsNeural Network Hide Unit Posteriori Probability Lithofacies Type Gaussian Classifier
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