Advances in Joule-Thomson cooling effects in CO2 storage: A systematic review of modeling techniques and implications for reservoir stability

The Joule-Thomson (JT) cooling effect is an important phenomenon during CO2 injection into depleted oil and gas reservoirs. This effect can significantly influence reservoir dynamics by impacting injectivity, reservoir stability, CO2 plume migration, chemical reactivity, and the formation of hydrates, posing challenges to effective CO2 storage. Different techniques including analytical models, experimental, numerical simulations, and machine learning methods, have emerged for investigating JT cooling effects and have shown promising results. However, these methods have not been reasonably synthesized to date, making it difficult for researchers to make sense of the current advancement in the field. To bridge this gap, we systematically evaluated the methodologies employed to investigate the JT cooling effects during CO2 injection in geological formations. The review synthesizes findings from thirty relevant studies published between 2007 and 2024, highlighting the diversity of approaches. Each method was assessed for its strengths, limitations, and applicability in predicting JT cooling-related phenomena. The findings reveal that while analytical methods provide preliminary insights, experimental studies yield accurate real-world data, and numerical simulations offer detailed reservoir dynamics. Machine learning techniques demonstrate promising predictive capabilities, enhancing the efficiency of data analysis. However, the review identifies critical gaps in the current literature. It proposes future research directions, emphasizing the integration of machine learning with real-world data, the study of impurity impacts, and the scaling of experimental methods. This comprehensive analysis aims to advance the understanding of JT cooling effects and improve the design and optimization of CO2 storage strategies, ultimately contributing to the effective mitigation of climate change.