Utilizing conditional generative adversarial networks for data augmentation in logging evaluation

Logging is critical for reservoir and fluid characterization by integrating the geophysical properties of rock layers. Traditional evaluation methods struggle with parameter selection, compromising predictive accuracy and generalizability. Machine learning, particularly conditional generative adversarial networks (CGAN), offers a robust alternative, addressing the disparity between labeled and unlabeled logging data that can lead to evaluation discrepancies. This study applies CGAN to augment data, enhancing input features for improved logging interpretation. We implemented this approach in the Jiyang Depression, Eastern China, using a dataset comprising 194 data points, each consisting of 18 logging curve features and one corresponding total organic carbon measurement from well NY1. The methodology aims to bolster the accuracy and reliability of logging interpretations through targeted data augmentation. We validated the augmented data's reliability through comprehensive analysis, including data characteristic assessments, statistical tests, mutual information analysis, similarity measurements, and consistency testing. The results confirm the efficacy of our data enhancement strategy, providing a robust framework for logging interpretation and future reservoir assessment. The method's effectiveness and reliability suggest broad applicability in fields such as seismic assessment and engineering development. This research not only bridges the gap between labeled and unlabeled data but also harnesses advanced machine learning to enhance geophysical evaluation outcomes. It underscores the importance of innovative data augmentation in the advancement of reservoir characterization and geophysical exploration.