Intelligent Method to Optimize the Frequency Modulation for Beam Pumping System Based on Deep Reinforcement Learning

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.

Automated summary

A mathematical simulation model is established to study the effect of real-time frequency variation on a beam pumping system. Deep reinforcement learning theory is used to define key elements and construct an intelligent model for frequency modulation optimization. The results show that the model can significantly reduce fluctuations in various parameters and improve the stability and energy efficiency of the system. It also enables independent learning and control of parameters, enhancing the information and intelligent management of oil wells.