Development of a Control-Aware Digital Twin Framework for Real-Time Monitoring and Optimization of Oil and Gas Production Systems.

A control-aware digital twin framework for tracking and improving oil and gas production systems is suggested in this paper. The method combines spatiotemporal graph neural networks, neural 4D-Var data assimilation, reinforcement learning-based control, and physics-informed neural operators (FNO and Neural operator model). Benchmark cyber-physical system datasets (SWaT and WADI) and a simulated oil and gas dataset are used to assess the framework. The results, which were confirmed by several independent runs using average measures, demonstrate enhanced performance in state estimation, anomaly detection, and control optimization. However, the assessment is restricted to benchmark and simulated datasets; additional validation using actual industry data is needed to verify practical applicability.