Research On Comprehensive Optimization Control Method Of Oil And Gas Gathering And Transportation Pipeline Network Operation Based On Reinforcement Learning

Oilfield intelligence is the future development trend of the oil industry,and the advancement of artificial intelligence technology has brought new hope to the industrial upgrading of all walks of life.The oil and gas gathering and transportation system is the core part of the entire oil surface engineering.The traditional oil and gas gathering and transportation system control relies on manual PID parameter setting and fine management.The production process requires manual detection of data and manual adjustment of control parameters.It is difficult to operate and has potential safety hazards.And it is not universal,and it is easy to cause high energy consumption and low resource utilization during the oil and gas transportation process,which makes the production cost rise sharply.Therefore,in view of the above problems,this method proposes a comprehensive control method for oil and gas gathering and transportation network operation based on reinforcement learning.This method can make the system control level intelligent without the need for manual expert analysis.The system can automatically adjust the control actions according to the real-time operating environment.And can guarantee the normal operation of the system.Main tasks as follows:1)Propose a comprehensive control method for oil and gas gathering and transportation network operation based on reinforcement learning.The key links such as control environment,control actions,reward function and update mechanism are defined in detail,and a complete intelligent control method is constructed.This method enables online control state evaluation and control action decision-making,enabling the system to operate normally without violating constraints.According to the experiment,as the training progresses,this method can guide the system to operate normally while maintaining a low energy consumption state.2)Propose an intelligent control model for the transportation system that introduces a safety mechanism.In order to improve the universal migration ability and higher control requirements of this control system,this method proposes a "security layer" design at the network structure level under the framework of deep reinforcement learning,proposes an intelligent control method of the security mechanism.This method can eliminate the hidden dangers of wax precipitation and backflow problems in the oil and gas gathering and transportation process of the system,and further ensure the safe operation of the system.3)Build a simulation system of visual control action simulation environment and comprehensive optimization control method of oil and gas gathering and transportation network.The system can visually show the real-time running status of control actions and can observe the current control actions and system running status in real time through temperature and pressure curves.