A Critical Review of Physics-Informed Machine Learning Applications in
  Subsurface Energy Systems
Abdeldjalil Latrach Mohamed Lamine Malki Misael Morales Mohamed Mehana Minou Rabiei
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 in the
oil and gas industry. The review highlights the successful utilization of PIML
for tasks such as seismic applications, reservoir simulation, hydrocarbons
production forecasting, and intelligent decision-making in the exploration and
production stages. 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; and geothermal systems by providing more
accurate and reliable predictions for resource management and operational
efficiency.