Semi-Supervised Classification for oil reservoir

TL;DR

This paper presents a semi-supervised neural network approach for oil reservoir identification, effectively leveraging limited labeled data and large unlabeled datasets to improve classification accuracy in oil well logs.

Contribution

The paper introduces a novel semi-supervised learning method using neural networks for oil reservoir classification, reducing the need for extensive expert labeling.

Findings

Semi-supervised neural network outperforms traditional classifiers

Effective use of high-confidence unlabeled data enhances accuracy

Method validated on Jianghan oil field data

Abstract

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.