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

TL;DR

The study tackles anomaly detection in well-log data by learning data distributions with Ensemble Generative Adversarial Networks (EGANs) and benchmarking them against Gaussian Mixture Models (GMMs). Using GR, DT, NPHI, and RHOB data from two North Sea wells, the authors apply clustering and Isolation Forest pre-processing, train both GMM and EGAN models, and evaluate performance with precision, recall, and F1. Results show that EGANs consistently achieve higher precision and F1 across datasets, indicating superior handling of complex, high-dimensional well-log distributions. The work highlights the practical impact of EGANs for improved anomaly detection, with implications for more reliable reservoir management and optimized drilling strategies.

Abstract

Although generative adversarial networks (GANs) have shown significant success in modeling data distributions for image datasets, their application to structured or tabular data, such as well logs, remains relatively underexplored. This study extends the ensemble GANs (EGANs) framework to capture the distribution of well log data and detect anomalies that fall outside of these distributions. The proposed approach compares the performance of traditional methods, such as Gaussian mixture models (GMMs), with EGANs in detecting anomalies outside the expected data distributions. For the gamma ray (GR) dataset, EGANs achieved a precision of 0.62 and F1 score of 0.76, outperforming GMM's precision of 0.38 and F1 score of 0.54. Similarly, for travel time (DT), EGANs achieved a precision of 0.70 and F1 score of 0.79, surpassing GMM 0.56 and 0.71. In the neutron porosity (NPHI) dataset, EGANs recorded a precision of 0.53 and F1 score of 0.68, outshining GMM 0.47 and 0.61. For the bulk density (RHOB) dataset, EGANs achieved a precision of 0.52 and an F1 score of 0.67, slightly outperforming GMM, which yielded a precision of 0.50 and an F1 score of 0.65. This work's novelty lies in applying EGANs for well log data analysis, showcasing their ability to learn data patterns and identify anomalies that deviate from them. This approach offers more reliable anomaly detection compared to traditional methods like GMM. The findings highlight the potential of EGANs in enhancing anomaly detection for well log data, delivering significant implications for optimizing drilling strategies and reservoir management through more accurate, data-driven insights into subsurface characterization.

Figures (7)

• Figure 1: GR dataset
• Figure 2: DT dataset
• Figure 3: RHOB dataset
• Figure 4: NPHI dataset
• Figure 6: Workflow for anomaly detection in well log data using (a) Gaussian Mixture Model (GMM) and (b) Ensemble Generative Adversarial Networks (EGANs).