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

Area of Science:

• Petroleum Engineering
• Artificial Intelligence
• Control Systems

Background:

• Beam pumping systems are crucial for oil extraction but face challenges with operational stability and energy consumption.
• Traditional frequency control methods often lack real-time adaptability to dynamic system variations.

Purpose of the Study:

• To develop an intelligent model for optimizing frequency modulation in beam pumping systems using deep reinforcement learning.
• To enhance the stability and energy efficiency of oil well operations through adaptive control.

Main Methods:

• Established a mathematical simulation model for beam pumping systems incorporating frequency conversion control.
• Applied deep reinforcement learning theory to define state space, action space, and reward functions for an intelligent control model.
• Integrated real-time frequency variation effects on system dynamics, including pumping unit motion, sucker rod vibration, and motor power.

Main Results:

• The deep reinforcement learning-based frequency optimization model significantly reduced fluctuations in polished rod load, crankshaft torque, and motor power.
• Demonstrated substantial energy savings and improved operational stability of the beam pumping system.
• Validated through simulation and field application, confirming the model's effectiveness.

Conclusions:

• The intelligent frequency optimization model offers a robust solution for enhancing beam pumping system performance.
• The model enables independent learning and control, reducing manual intervention and improving intelligent management of oil wells.
• This approach leads to more stable, energy-efficient, and autonomously managed oil extraction operations.