In recent years, the proportion of low-permeability, low-pressure, and low-productivity oil and gas wells in China’s oilfields has been increasing year by year. As a key production mode for such wells, intermittent production plays an important role in stabilizing production, reducing energy consumption, and protecting reservoirs. However, the traditional manual intermittent production mode is restricted by fixed working schedules, lagging data monitoring, insufficient real-time response capacity, and strong human subjectivity. It is difficult to adapt to the dynamic changes of reservoir seepage, downhole fluid supply, and production conditions, resulting in problems such as low pump efficiency, serious ineffective energy consumption, unbalanced reservoir energy utilization, and high operation and maintenance costs. To solve these practical problems in field production, this paper proposes an intelligent optimization technology for intermittent production of oil and gas wells based on artificial intelligence control. The technology constructs a full-cycle integrated system including real-time data acquisition, intelligent algorithm analysis, dynamic parameter optimization, and automatic execution control. Based on long-short term memory (LSTM) neural network and genetic algorithm, it establishes a dynamic prediction model for downhole fluid level recovery, pressure attenuation, and productivity change, and realizes adaptive optimization and automatic control of intermittent production cycles. Field application in a low-permeability oilfield shows that the proposed technology can significantly reduce invalid operation time, cut down power consumption by 39.4%, increase daily oil production per well by 28.1%, and lower equipment failure rate by 67.5%. It provides a reliable intelligent technical solution for high-efficiency, low-consumption, and automated production of low-efficiency oil and gas wells, and strongly supports the digital transformation and intelligent development of oilfield production management.
artificial intelligence; intelligent control; oil and gas wells; intermittent production; parameter optimization; low-permeability reservoir; LSTM neural network; genetic algorithm
Zhou L., Gou L., Han J., Yao W. Research on Intelligent AI-Based Control for Intermittent Production Optimization of Oil and Gas Wells. Int. J. Eng. Sci. Technol., 2026, 4 (2), doi: 10.58531/ijest/4/2/1
