Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis

Stuck pipe events remain a critical challenge in oil and gas drilling operations, leading to increased non-productive time and substantial financial losses. Traditional detection methods rely on manual monitoring and expert judgment, which are prone to delays and human error. This study proposes a deep learning autoencoder-based anomaly diagnosis approach to enhance the detection of stuck pipe incidents. Using high-resolution time series drilling data from the Volve field, a deep learning autoencoder model was trained exclusively on normal drilling conditions to learn operational patterns and detect deviations indicative of stuck pipe events. The proposed model leverages reconstruction error as an anomaly detection metric, effectively distinguishing between normal and stuck cases. The results demonstrate that the model achieves a detection accuracy of 99.06%, with an area under the receiver operating characteristic curve (AUC) of 0.958. Additionally, the model attained a precision of 97.12%, a recall of 91.34%, and a F1-score of 94.15%, significantly reducing false positives and false negatives. The findings highlight the potential of deep learning-based approaches in improving real-time anomaly detection, offering a scalable and cost-effective solution for mitigating drilling disruptions. This research contributes to advancing intelligent monitoring systems in the oil and gas industry, reducing operational risks, and enhancing drilling efficiency.