Automatic lithology prediction from well logging using kernel density estimation

Gamma-ray from wireline logging is selected as the variable to describe two lithology groups of shale and not-shale. Data from six wells in the Norwegian Continental Shelf were extracted and examined with aids of explorative data analysis and hypothesis testing, and then divided into a training- and test data set. The selected algorithm processed the training data into models, and later each element of test data was assigned to the models to get the prediction. The results were validated with cutting data, and it was proved that the models predicted the lithology effectively with misclassification rates less than 15% at its lowest and average of ± 31%. Moreover, the results confirmed that the method has a promising prospect as lithology prediction tool, especially in real-time operation, because the non-parametric approach allows real-time modeling with fewer data assumptions required.