Advances in machine learning for petroleum production engineering: A review

Machine learning (ML) has become an increasingly important tool in the petroleum industry due to its ability to handle complex, high-dimensional data and support predictive decision-making. While numerous studies and reviews have examined ML applications in upstream petroleum engineering, a focused review of ML methods applied specifically to petroleum production engineering remains limited. This review examines ML applications in core production engineering workflows, including hydrocarbon production forecasting, well performance evaluation, flow assurance, artificial lift systems, and well stimulation, for both conventional and unconventional reservoirs. The surveyed literature spans more than twenty countries worldwide; however, the majority of studies originate from China and the United States, followed by Russia, Saudi Arabia, Brazil, Iran, Egypt, and Nigeria. Topics such as drilling operations, hydraulic fracturing design, carbon capture and storage, hydrogen energy, and economic or price forecasting are outside the scope of this review. The reviewed studies span a wide range of ML approaches, from traditional regression and ensemble methods to deep learning architectures and emerging physics-informed neural networks. Rather than presenting performance metrics in isolation, this review discusses where ML methods have shown practical advantages and their limitations, including overfitting, data quality constraints, limited generalizability, and challenges related to physical consistency and operational deployment. By consolidating recent advances and reported challenges, this review aims to provide a balanced perspective on the current capabilities of ML in petroleum production engineering and to identify areas where further methodological development and integration with domain knowledge are required for reliable field-scale applications.