Artificial intelligence and machine learning for production prediction and optimization of oil and gas surface networks

Artificial intelligence (AI) and machine learning (ML) are transforming production prediction and optimization in oil and gas (O&G) surface networks, providing powerful alternatives to traditional physics-based models. This review provides a comprehensive overview of recent advancements in data-driven, hybrid, and physics-informed frameworks for surface network analysis, with a focus on their applications in forecasting, control, and decision-making. It traces the evolution of methods from early steady-state nodal analysis to digital twins and physics-informed AI, highlighting increased integration of real-time data, automation, and adaptive optimization. Key AI/ML techniques, including statistical learning, tree-based ensembles, deep learning, and neural operator architectures, are discussed in terms of predictive accuracy, scalability, and interpretability. The review also examines major challenges in implementation, including data quality, computational demands, model generalizability, and trustworthiness. Emerging areas, such as real-time adaptive control, hybrid AI-physics models, and closed-loop digital twins, are explored as tools for enabling autonomous and sustainable field operations. Overall, this review emphasizes that the future of surface network management will be driven by physics-informed, data-centric intelligence, supporting reliable, efficient, and understandable optimization across diverse oil and gas assets.