Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Harrison Osei; Cornelius B Bavoh; Bhajan Lal

doi:10.1021/acsomega.3c04825

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

ACS Omega. 2024 Jan 19;9(4):4210-4228. doi: 10.1021/acsomega.3c04825. eCollection 2024 Jan 30.

Authors

Harrison Osei^{1

2}, Cornelius B Bavoh^{2

3}, Bhajan Lal^{3

4}

Affiliations

¹ Department of Petroleum Engineering, University of Mines and Technology, P.O. Box 237, Tarkwa, Ghana.
² School of Petroleum Studies, University of Mines and Technology, P.O. Box 237, Tarkwa, Ghana.
³ Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak Darul Ridzuan, Malaysia.
⁴ Research Centre for CO2 Capture (CO2RES), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia.

Abstract

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, CO₂ 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.

Publication types

Review