ABSTRACT The utilisation of associated gas for power generation in offshore oilfields is essential for improving resource efficiency, yet operations remain hampered by substantial gas flaring and heavy reliance on manual experience. This paper presents a digital twin‐based predictive control strategy for multiplatform natural gas distribution, comprising four modules: (1) Digital twin module: Builds high‐fidelity digital models with a hybrid mechanistic‐data approach to simulate and monitor gas distribution systems; (2) operating condition prediction module: Uses a benchmark library from equipment models, combined with power demands and real‐time data, to predict valve openings, platform pressures and identify the control valve requiring adjustments; (3) intelligent distribution module: Integrates steady‐state models with self‐tuning PID algorithms to autonomously generate valve control schemes and (4) scheme verification module: Validates control schemes using dynamic models, providing feedback for parameter optimisation. A case study at a Chinese offshore oilfield demonstrates the strategy's effectiveness. The Lambda tuning method enabled PID self‐tuning, generating valve control schemes within 1 minute in response to target power loads, ensuring rapid and stable control. Gas flow deviations between dynamic simulation and setpoints remained below ± 5%, confirming practical applicability. This strategy offers a reliable solution for intelligent gas distribution as offshore operations advance towards unmanned platforms.
Li et al. (Thu,) studied this question.