Osei Harrison, Bavoh Cornelius B, Lal Bhajan
Department of Petroleum Engineering, University of Mines and Technology, P.O. Box 237, Tarkwa, Ghana.
School of Petroleum Studies, University of Mines and Technology, P.O. Box 237, Tarkwa, Ghana.
ACS Omega. 2024 Jan 19;9(4):4210-4228. doi: 10.1021/acsomega.3c04825. eCollection 2024 Jan 30.
The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.
由于机器学习工具所需数据的存在，气体水合物模型的复杂建模精度最近有所提高。在本综述中，我们讨论了在各种与水合物相关的领域中使用的大多数机器学习工具，如相行为预测、水合物动力学、二氧化碳捕集以及天然气水合物的自然分布和饱和度。还详细讨论了机器学习模型与传统天然气水合物模型之间的性能比较。本综述表明，机器学习方法改进了水合物相性质预测，可应用于当前及新的天然气水合物模拟软件，以获得更好、更准确的结果。
