Research on Intelligent Control Technology of Oil Production Engineering Under the Background of Low Carbon and Energy Saving

This paper deeply explores the intelligent control technology of oil production engineering, aiming to reduce energy consumption and improve mining efficiency. First, the high energy consumption problem faced by current oil production engineering and the current application status of intelligent control technology are explained. An adaptive particle swarm-deep reinforcement learning hybrid algorithm (APSO-DRL) based on multi-objective optimization is innovatively proposed. This algorithm combines the global search capability of the particle swarm algorithm with the dynamic decision-making capability of deep reinforcement learning. By adaptively adjusting the particle swarm parameters, it can achieve precise control of the pumping unit operating parameters, water injection system flow, etc. in a complex oil production environment, so as to achieve the dual goals of reducing energy consumption and increasing oil production. The experimental simulation model is built based on actual oil production data. It is shown that compared with traditional control algorithm, APSO-DRL can reduce energy consumption 23.5 %, water injection system energy consumption 18.7 %, and oil yield 15.2 %. These results provide strong technical support and theory basis for intelligent development of low carbon and low energy consumption.