This paper discusses the energy-saving control method of a pipeline network system based on reinforcement learning and a genetic algorithm and compares it with traditional control methods such as constant-pressure control and non-frequency conversion control. The purpose is to improve the operational efficiency of an offshore oil and gas field water injection system. This paper simulates and verifies the experimental platform of a water injection system pipe network in offshore oil and gas fields and evaluates the optimization effect of different control strategies under different flow rates. The experimental results reveal that under a varying flow rate, the water injection system harnessing the GA and RL exhibits a remarkable energy-saving advantage over traditional control methods. Specifically, the GA strategy achieves an average energy-saving rate of 22.51%, with a maximum energy-saving rate of 38.14% under low flow rate, while the RL strategy attains an average energy-saving rate of 18.39%. These methodologies not only furnish novel solutions for the real-time optimal scheduling of water injection systems in offshore oil and gas fields but also proffer practical guidance, thereby paving the way for technological advancement and sustainable development in the industry.