Next-Generation Intelligent Monitoring of Oil Wells: Revolutionizing Anomaly Detection with AutoML-Driven Ensemble Intelligence

Abstract

Unplanned shutdowns, equipment damage, and production losses exceeding millions of dollars per day. Traditional anomaly detection methods, often based on fixed thresholds and manual inspection, struggle to keep pace with the volume and complexity of sensor data in modern digital oilfields. This study introduces a fully automated anomaly-detection framework using the AutoGluon Automated Machine Learning (AutoML) platform. Leveraging the publicly available 3W dataset, a multivariate time-series benchmark with realistic challenges (missing readings, frozen channels, severe class imbalance), AutoGluon automates data preprocessing, feature engineering, model selection, and hyperparameter optimization under a time budget, minimizing manual intervention. Data were cast as binary detection using sliding windows (180 points) with a 60/40 train–test split and optimization targeted to F1 to account for imbalance. The resulting weighted ensemble, combining k-Nearest Neighbors, LightGBM, and deep neural networks, was evaluated across both real and simulated scenarios. It achieved 99.99% accuracy, 99.81% precision, 99.95% recall, 99.88% F1-score, and 99.97% ROC AUC, with only 30 false negatives and 113 false positives over ~1.36 million predictions. Compared with traditional single-model and manually tuned baselines, the AutoML ensemble provided earlier and more stable detection across anomaly families while reducing alarm fatigue and operator workload. These findings demonstrate that AutoML-driven ensembling delivers accurate, scalable, and low-maintenance anomaly detection suitable for real-time deployment in digital oilfields. By replacing ad-hoc feature engineering and iterative retuning with end-to-end automation, the proposed approach provides a practical pathway to intelligent, self-adaptive monitoring that improves operational efficiency, reliability, and safety in offshore production, and is readily extensible to streaming SCADA integrations and field-wide rollouts.