Abstract
ABSTRACT 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.