Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classification

Abstract

The petrophysical facies classification in the field of hydrocarbon exploration is one of the important tasks for reservoir characterization. To predict the facies of the seismic area, deep learning has recently been applied. However, when applying machine learning (ML) to the facies classification, there is a problem that the data available for training are very limited. When using training data acquired under such limited conditions, such as well log data, there can be a severe imbalance in the number of training samples for the facies because the amount of data acquired in the hydrocarbon area of interest is relatively less than that acquired in the nonhydrocarbon area. Thus, the facies classification results often show weighted predictions of a specific facies due to the imbalance issue of training data.