Enhanced anomaly detection in well log data through the application of ensemble GANs

Detecting subtle anomalies in well log data can significantly enhance subsurface characterization and inform reservoir decision-making. Well logs provide detailed records of rock and fluid properties. However, identifying anomalous patterns in these data remains a persistent challenge due to the complexity of geological systems and limitations in traditional statistical models. Classical anomaly detection approaches, such as Gaussian mixture models (GMMs), tend to oversimplify the intricacies of well log responses and often misinterpret geological heterogeneities as tool noise. This misclassification leads to high false positive rates and unreliable anomaly identification. In this context, generative models, particularly generative adversarial networks (GANs), have shown promise in structured data domains; however, they remain underutilized in geoscience applications. This study aims to benchmark the performance of ensemble GANs (EGANs) against GMMs for anomaly detection in well log data. The proposed EGANs framework aggregates multiple independently trained GANs to enhance model stability and robustness. Anomalies are detected based on discriminator scoring, while performance is evaluated using precision, recall, and F1-score across four key logs: gamma ray, travel time, bulk density, and neutron porosity. The results demonstrate that EGANs consistently outperform GMMs across all logs, achieving higher precision (up to 0.70) and F1-scores (up to 0.79), with statistically significant improvements confirmed via paired t-tests. These findings highlight the ability of EGANs to model complex subsurface patterns and detect subtle deviations more effectively than conventional probabilistic methods. This study introduces the first application of EGANs to petrophysical anomaly detection, bridging deep learning with geoscience workflows. It offers a scalable framework for integrating data-driven anomaly detection into reservoir modeling, quality control, and near-real-time decision-making in drilling operations. Future work will focus on multivariate analysis, cross-basin validation, and real-time deployment, advancing toward a more intelligent, adaptive reservoir monitoring system.