A critical review of physics-informed machine learning applications in subsurface energy systems

Abstract

Machine learning has emerged as a powerful tool in various fields, including computer vision, natural language processing, and speech recognition. It can unravel hidden patterns within large data sets and reveal unparalleled insights, revolutionizing many industries and disciplines. However, machine and deep learning models lack interpretability and limited domain-specific knowledge, especially in applications such as physics and engineering. Alternatively, physics-informed machine learning (PIML) techniques integrate physics principles into data-driven models. By combining deep learning with domain knowledge, PIML improves the generalization of the model, abidance by the governing physical laws, and interpretability. This paper comprehensively reviews PIML applications related to subsurface energy systems, mainly within the context of the oil and gas industry. The review highlights the successful utilization of PIML for tasks such as seismic applications, reservoir and fluid flow simulation, and hydrocarbons production forecasting. Additionally, it demonstrates PIML’s capabilities to revolutionize the oil and gas industry and other emerging areas of interest, such as carbon and hydrogen storage, by providing more accurate and reliable predictions for resource management and operational efficiency.