AI-Driven Reservoir Management: GANs and GMM for Enhanced Control

This research explores the novel application of ensemble generative adversarial networks (Ensemble GANs) for modeling data distributions and detecting anomalies within well log datasets, traditionally a challenging task for Gaussian mixture models (GMMs). The datasets encompass critical geophysical measurements such as gamma ray (GR) and deep resistivity (ILD). Ensemble GANs leverage the power of multiple GANs to enhance the stability and accuracy of the generated data distributions, addressing the limitations faced by single GANs and traditional methods. Performance metrics, including precision, recall, and F1 score, were used to compare Ensemble GANs with GMMs. The results reveal that Ensemble GANs significantly outperform GMMs across the GR and ILD datasets. For the GR dataset, Ensemble GANs achieved a precision of 0.97, recall of 0.97, and an F1 score of 0.98, compared to GMM's precision of 0.37, recall of 0.95, and F1 score of 0.54. In the ILD dataset, Ensemble GANs recorded a precision of 0.43, recall of 0.86, and an F1 score of 0.57, while GMMs achieved a precision of 0.45, recall of 1.00, and an F1 score of 0.62. These findings underscore the enhanced capability of Ensemble GANs in accurately approximating complex data distributions and effectively detecting anomalies in structured datasets. The novel application of Ensemble GANs demonstrates their potential as a robust tool for anomaly detection in geoscientific data and other structured data-intensive fields, offering significant improvements in precision, recall, and F1 scores over traditional methods like GMMs. This study highlights the promise of Ensemble GANs in enhancing control and optimization in AI-driven reservoir management.