Zhang Ruichao, Chen Dechun, Xiao Liangfei
Shandong Institute of Petroleum and Chemical Technology, Dongying 257061, China.
China University of Petroleum (East China), Qingdao 266580, China.
ACS Omega. 2023 Mar 2;8(10):9475-9485. doi: 10.1021/acsomega.2c08170. eCollection 2023 Mar 14.
A mathematical simulation model of a beam pumping system with frequency conversion control is established, considering the influence of the real-time frequency variation on the motion law of a pumping unit, the longitudinal vibration of a sucker rod string, the crankshaft torque, and the motor power. On this basis, the key links such as state space, action space, and reward function are defined by using deep reinforcement learning theory, and an intelligent model to optimize the frequency modulation for a beam pumping system based on deep reinforcement learning is constructed. The simulation and field application results show that the frequency optimization model can significantly reduce the fluctuation amplitude of the polished rod load, crankshaft torque, motor power, and input power of the system, making the operation of the pumping system more stable and energy-saving. More importantly, the model can realize the independent learning and control of the corresponding parameters without manual intervention to ensure the normal operation of the system and improve the level of information and intelligent management of oil wells.
建立了一种具有变频控制的游梁式抽油机系统的数学仿真模型，考虑了实时频率变化对抽油机运动规律、抽油杆柱纵向振动、曲轴扭矩和电机功率的影响。在此基础上，运用深度强化学习理论定义了状态空间、动作空间和奖励函数等关键环节，并构建了基于深度强化学习的游梁式抽油机系统调频优化智能模型。仿真和现场应用结果表明，频率优化模型能够显著降低光杆载荷、曲轴扭矩、电机功率和系统输入功率的波动幅度，使抽油系统运行更加稳定且节能。更重要的是，该模型无需人工干预即可实现对相应参数的自主学习和控制，确保系统正常运行，提高油井的信息化和智能管理水平。
