<p>The integration of computer vision (CV) techniques into reservoir modeling and data assimilation is reshaping petroleum engineering, driven by advances in machine learning and the growing availability of high-resolution imaging data. Traditional reservoir characterization is challenged by uncertainty arising from sparse, heterogeneous data sources, such as well logs, core samples, and seismic volumes, as well as subjective interpretations and limited resolution. While CV techniques do not eliminate these sources of uncertainty, they provide automated, data-driven methods that can reduce interpreter bias, improve consistency, and enhance feature extraction across various scales. In digital rock physics, the combination of micro-CT and SEM imaging with segmentation and regression models enables improved estimation of petrophysical properties such as porosity and permeability. In seismic interpretation, deep learning-based CV models automate fault detection, horizon picking, and facies classification, offering faster and more consistent outputs. Generative models, including GANs and diffusion networks, further contribute by synthesizing realistic geological images for data augmentation and training. CV techniques are also emerging in dynamic reservoir management, where image-based features support history matching and CNN-based surrogate models offer fast approximations of fluid flow behavior, supporting real-time decision-making. Despite these advances, challenges remain, including the scarcity of labeled datasets, poor generalizability of models across geological settings, and limited integration with physics-based simulations. This review synthesizes recent progress in applying computer vision to reservoir modeling and data assimilation. It highlights current capabilities, outlines persistent sources of uncertainty, and discusses future directions for building scalable, reliable, and data-informed reservoir management systems.</p>