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Machine learning has emerged as a powerful tool in various fields, includingcomputer vision, natural language processing, and speech recognition. It canunravel hidden patterns within large data sets and reveal unparalleledinsights, revolutionizing many industries and disciplines. However, machine anddeep learning models lack interpretability and limited domain-specificknowledge, especially in applications such as physics and engineering.Alternatively, physics-informed machine learning (PIML) techniques integratephysics principles into data-driven models. By combining deep learning withdomain knowledge, PIML improves the generalization of the model, abidance bythe governing physical laws, and interpretability. This paper comprehensivelyreviews PIML applications related to subsurface energy systems, mainly in theoil and gas industry. The review highlights the successful utilization of PIMLfor tasks such as seismic applications, reservoir simulation, hydrocarbonsproduction forecasting, and intelligent decision-making in the exploration andproduction stages. Additionally, it demonstrates PIML's capabilities torevolutionize the oil and gas industry and other emerging areas of interest,such as carbon and hydrogen storage; and geothermal systems by providing moreaccurate and reliable predictions for resource management and operationalefficiency.

A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems