Leveraging associated gas for power generation is a critical pathway to enhance comprehensive resource utilization during the development of offshore oilfields. However, this process currently suffers from significant gas wastage and inefficient pipeline distribution. Meanwhile, compressor pressure and valve settings remain heavily reliant on manual experience, especially under the complex multi-platform architectures, which severely impedes the intelligent transition of this industry. To address these challenges, this paper proposes a digital twin-based predictive control strategy for multi-platform natural gas distribution. The strategy integrates four core modules: (1) a Digital Twin Module, which employs a hybrid mechanism-data modeling approach to construct high- fidelity digital twin models for simulating and monitoring gas distribution systems; (2) an Operating Condition Prediction Module, which establishes a benchmark library of operating conditions based on unit equipment models. Combining with power generation demands and actual platform gas flow, the library enables rapid, accurate prediction of stop valve openings, platform pressures and the selected control valve to be adjusted; (3) an Intelligent Distribution Module, integrating the steady-state system model with a PID parameter self-tuning algorithm to autonomously generate a control valve control scheme; (4) a Scheme Verification Module, which validates correctness of the control scheme with the dynamic-state model. A case study applying this control strategy to gas distribution in an offshore oilfield in China demonstrated that, according to the desired power station load, predictive control schemes are generated within one minute. The discrepancy between the gas flow of the dynamic system model and the desired one was less than 5%, verifying the engineering applicability of this strategy. Against the accelerating trend towards unmanned development of offshore oilfields, the proposed strategy provides a reliable solution for the intelligent allocation of natural gas resources from multiple platforms.
Xu et al. (Wed,) studied this question.