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.