Advancements in Machine Learning–Driven Proxy Modelling Techniques for Reservoir Engineering: A Systematic Review
Motaei E. Ganat T. Tabatabai M. Umer M. Krishna S.
April 2026John Wiley and Sons Inc
Journal of Petroleum Geology
2026#49Issue 2516 - 540 pp.
Application of machine learning (ML) in proxy models has grown in recent years and is evident across all disciplines in petroleum engineering for various tasks such as asset optimisation, production forecasting and production optimisation. This study reviews the literature and examines advancements in artificial intelligence/ML (AI/ML) usage in proxy models. A systematic literature review approach is applied to study the published relevant papers, and 83 papers were screened out of 1503 in the related area. Our findings indicate that there is a lack of validation processes in the workflow of proxy model creation in a majority of published works, which reduces the reliability of models for real-time applications. This makes the proxy models static designed for a specific problem only and results in single-use models. Various research tools are used to analyse the papers and understand the input parameters of the proxy models, their targeted parameters, and the approach utilised for proxy model creation. The study shows that neural networks are the most utilised and promising ML approaches for complex reservoir models with high performance metrics.
complex systems , computational efficiency , optimisation , proxy models , uncertainty quantification
Text of the article Перейти на текст статьи
Petroleum Engineering Department, Petronas Carigali Sdn. Bhd., Kuala Lumpur, Malaysia
Department of Petroleum Chemical Engineering, Sultan Qaboos University, Muscat, Oman
Reservoir Engineering, Halliburton Energy Asia Service (M) Sdn. Bhd., Kuala Lumpur, Malaysia
School of Mining and Geosciences, Department of Petroleum Engineering, Nazarbayev University, Astana, Kazakhstan
Department of Petroleum Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar, India
Petroleum Engineering Department
Department of Petroleum Chemical Engineering
Reservoir Engineering
School of Mining and Geosciences
Department of Petroleum Engineering
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026