Detection and classification of anomalies in oil well production using Open-World Learning

Accurate anomaly detection and classification are critical for operational safety and efficiency in oil well production. While machine learning methods can identify known anomalies, detecting and classifying previously unseen anomalies remains a challenge. This paper presents the first Open-World Learning strategy applied to anomalies in oil well production data. The strategy detects anomalous behavior, classifies whether it belongs to a known labeled anomaly, and, if not, clusters it into newly proposed anomaly classes and learns to classify them. The approach integrates autoencoder reconstruction error for anomaly detection, autoencoder-based dimensionality reduction to extract latent features, binary classifiers to classify known anomalies, and clustering methods to group similar unseen anomalies. If an anomaly is detected via reconstruction error, the binary classifiers determine whether it belongs to a known class. If it does not, the clustering method groups similar events into new classes, which are validated by human experts. This validation enables the training of specific binary classifiers for the new classes and updates existing ones. Experiments on real anomalous oil well production data demonstrate that the discovered clusters align well with ground-truth labels. The clustering methodology achieves 81% accuracy overall, exceeding 95% for certain anomalies, while updated binary classifiers reach up to 99% accuracy. These findings demonstrate the proposed method’s effectiveness in dynamically adapting to novel anomalies, improving classification accuracy, and enhancing oil well monitoring.