Abstract: As water-flooded mature oilfields enter the stage of deep and refined development, the complex dynamics of subsurface oil-water distribution impose higher demands on reservoir management. This paper systematically reviews the research progress of streamline simulation technology for reservoir development, outlining its developmental trajectory from two-dimensional tracing algorithms to three-dimensional multiphase flow models, and further to the intelligent stage. The algorithmic principle of enhancing computational efficiency through dimensionality reduction (decomposing three-dimensional flow fields into one-dimensional streamlines) is elucidated. The streamline technology is categorized into two components: streamline simulation and streamline characterization. The former reveals fluid flow patterns in porous media by solving pressure and saturation fields, while the latter accomplishes visual modeling of flow trajectories through velocity field reconstruction, streamline path tracing, and multiphysical parameter transfer. The study comprehensively summarizes applications of streamline simulation in aiding history matching and model optimization, reservoir model upscaling and computational acceleration, characterization of complex displacement mechanisms, reservoir management and optimization, water injection and well pattern optimization, as well as dynamic analysis and uncertainty quantification. However, current streamline simulation technology faces challenges in multi-physical field coupling modeling, across-scale computational efficiency, and data dependency. Breakthroughs in machine learning, adaptive mesh optimization, and high-performance computing integration are urgently needed to support sustainable energy development.