Abstract
With the increasing complexity of oilfield development environments, traditional water injection strategies relying on empirical rules and simple numerical simulations have become ineffective in addressing reservoir heterogeneity, dynamic variations, and geological uncertainties, thereby limiting injection control accuracy and production efficiency. This paper explores the application of intelligent optimization algorithms, including deep reinforcement learning, genetic algorithms, and particle swarm optimization, in optimizing water injection strategies. It further examines the role of data fusion, real-time monitoring, and dynamic optimization techniques—such as digital twins, long short-term memory time series prediction, and Kalman filtering—in enhancing the scientific rigor and reliability of water injection decisions. Additionally, this paper analyzes the advantages of multi-objective optimization algorithms, such as NSGA-II, in balancing recovery rates, energy consumption, and economic benefits. The study aims to provide valuable academic insights for developing an efficient, precise, and low-carbon intelligent oilfield management system.
