  This paper addresses the general problem of accurate identification of oil
reservoirs. Recent improvements in well or borehole logging technology have
resulted in an explosive amount of data available for processing. The
traditional methods of analysis of the logs characteristics by experts require
significant amount of time and money and is no longer practicable. In this
paper, we use the semi-supervised learning to solve the problem of
ever-increasing amount of unlabelled data available for interpretation. The
experts are needed to label only a small amount of the log data. The neural
network classifier is first trained with the initial labelled data. Next,
batches of unlabelled data are being classified and the samples with the very
high class probabilities are being used in the next training session,
bootstrapping the classifier. The process of training, classifying, enhancing
the labelled data is repeated iteratively until the stopping criteria are met,
that is, no more high probability samples are found. We make an empirical study
on the well data from Jianghan oil field and test the performance of the neural
network semi-supervised classifier. We compare this method with other
classifiers. The comparison results show that our neural network
semi-supervised classifier is superior to other classification methods.
