Graph-Based Deep Learning for Intelligent Detection of Energy Losses, Theft, and Operational Inefficiencies in Oil & Gas Production Networks

ArXi:2603.14406v1 Announce Type: new Early detection of energy losses, theft, and operational inefficiencies remains a critical challenge in oil and gas production systems due to complex interdependencies among wells and facilities, evolving operating conditions, and limited labeled anomaly data. Traditional machine learning approaches often treat production units independently and struggle under temporal distribution shifts. This study proposes a spatiotemporal graph-based deep learning framework for anomaly detection in oil and gas production networks.