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.

翻译：机器学习已成为包括计算机视觉、自然语言处理和语音识别等多个领域的强大工具。它能够从大型数据集中揭示隐藏的模式，提供前所未有的洞察力，从而彻底改变了众多行业和学科。然而，机器学习和深度学习模型缺乏可解释性，且领域知识有限，尤其在物理和工程等应用领域。物理信息机器学习（PIML）技术将物理原理融入数据驱动模型，通过结合深度学习与领域知识，提升了模型的泛化能力、对物理定律的遵循程度以及可解释性。本文全面综述了PIML在地下能源系统中的应用，主要聚焦于石油和天然气行业。评述重点突出了PIML在地震应用、油藏模拟、碳氢化合物产量预测以及勘探和生产阶段的智能决策等任务中的成功应用。此外，本文展示了PIML通过为资源管理和运营效率提供更准确、可靠的预测，从而彻底改变石油天然气行业及其他新兴领域（如碳氢储存和地热系统）的潜力。