Variable-Condition Operational Optimization of Oil and Gas Gathering Systems Considering Renewable Energy...

The accelerating global energy transition imposes a pressing need for decarbonizing upstream oil and gas production. Characterized by significant thermal-hydraulic inertia, gathering systems possess immense potential as "flexible loads" for accommodating local wind and solar energy. However, this potential is constrained by stringent Flow Assurance (FA) boundaries, primarily due to nonlinear rheological transitions and blockage risks during the variable-condition transport of high-water-cut waxy crude oil. Addressing this bottleneck, this review elucidates advances in utilizing crude oil-water blending as a pivotal physical control lever to synergize system safety and renewable accommodation. An integrated "mechanism-modeling-control" framework is established, analyzing oil-water interfacial evolution, Wall-Sticking Onset Temperature (WSOT) drift, and blockage hysteresis to define actionable safety boundaries. Dynamic modeling techniquesfrom High-Fidelity Physics-Based Models (PBMs) to Physics-Informed Neural Networks (PINN) and Digital Twins (DT)are critically evaluated, highlighting hybrid paradigms resolving the accuracy-efficiency trade-off. Furthermore, hierarchical optimization strategies integrating Robust/Stochastic Programming with Deep Reinforcement Learning (DRL) are systematically presented to reconcile renewable uncertainty with rigid safety constraints. The review indicates that transforming gathering systems into "Flexible Prosumers" requires an integrated paradigm encompassing transient multiphase flow mechanisms, high-fidelity hybrid modeling, and adaptive robust control, providing a scientific basis for maximizing renewable utilization while ensuring flow assurance.