Hydrocarbon reservoir management faces significant challenges in history matching and production forecasting due to complex multiphase fluid flow, high-dimensional parameter spaces, and the substantial computational cost of conventional numerical simulators. This study presents a systematic review of more than 70 key studies to provide a scientific roadmap for the application of Machine Learning (ML) and Deep Learning (DL) in reservoir engineering. The reviewed methods are categorized according to their learning paradigms and architectural frameworks, with emphasis on their impact on reservoir modeling and operational decision-making.

The analysis shows that recent developments integrating Maximum Entropy Deep Reinforcement Learning (DRL) with latent space representations can substantially reduce the computational burden by automating data assimilation in history matching workflows. In production forecasting, advanced architectures such as CNN-LSTM networks and ensemble learning approaches have demonstrated improved predictive performance for complex reservoir processes, including Steam-Assisted Gravity Drainage (SAGD) and enhanced recovery operations. Furthermore, AI-driven estimation of geochemical properties has improved petrophysical characterization in unconventional reservoirs. Emerging physics-informed approaches, particularly Physics-Informed Neural Networks (PINNs) and Fourier Neural Operators (FNO), address data scarcity and enable more efficient simulations for applications such as carbon capture and storage (CCS). In addition, smart proxy models have enhanced decision support, production optimization, and produced water management.

Overall, current research indicates a clear transition from purely data-driven models toward hybrid physics–machine learning frameworks, paving the way for the development of physics-informed digital twins for next-generation reservoir management.