Selection of Augmented Data for Overcoming the Imbalance Problem in Facies Classification
Dowan Kim, Joongmoo Byun
Abstract
Facies classification refers to the classification of rock types and pore fluids using information obtained from well log data and core samples. A range of elastic properties provide the main input for classification models. The elastic properties are closely related to water saturation, porosity, and shale volume. In addition, if impedance inversion is performed, the same elastic properties can be obtained from the surface seismic area, thus linking well log and surface seismic data. Machine learning (ML)-based facies classification has the advantage of minimizing the subjectivity associated with human interpretations and maximizing the time efficiency. However, due to the insufficiency of well log data, class imbalance and absolute data shortages can easily arise. Therefore, in this study, we used a cycle-consistent generative adversarial network (CycleGAN) to augment the synthetic data simulating well log data. In addition, we determined which classes of data required augmentation when using CycleGAN and proposed criteria for selecting the augmented data to be used for class-balanced training. The developed algorithm was verified using the Vincent oil field data. The classification results were improved, and more physically valid predictions were achieved in the surface seismic survey area. The data augmentation scheme developed in this study will be useful for facies classification in environments where well log data are very limited.