Machine Learning-Driven Prediction of CO 2 Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach

Seyed Hossein Hashemi, Farshid Torabi () and Paitoon Tontiwachwuthikul
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Seyed Hossein Hashemi: Energy Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada
Farshid Torabi: Energy and Process Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada
Paitoon Tontiwachwuthikul: Clean Energy Technologies Research Institute (CETRi), Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada

Energies, 2025, vol. 18, issue 15, 1-18

Abstract: The solubility of CO 2 in brine systems is critical for both carbon storage and enhanced oil recovery (EOR) applications. In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. Among the tested kernels, the ARD Matern 3/2 and ARD Matern 5/2 kernels achieved the highest predictive accuracies, with R 2 values of 0.9961 and 0.9960, respectively, on the test data. This demonstrates superior performance in capturing CO 2 solubility trends. The GWO algorithm effectively tuned the hyperparameters for all kernel configurations, while the ARD capability successfully quantified the influence of key physicochemical parameters on CO 2 solubility. The outstanding performance of the ARD Matern 3/2 and ARD Matern 5/2 kernels suggests their particular suitability for modeling complex thermodynamic behaviors in brine systems. Furthermore, this study integrates fundamental thermodynamic principles into the modeling framework, ensuring all predictions adhere to physical laws while maintaining excellent accuracy (test R 2 > 0.98). These results highlight how machine learning can improve CO 2 injection processes, both for underground carbon storage and enhanced oil production.

Keywords: CO 2 solubility; Gaussian process regression; Grey Wolf Optimizer; ARD kernels; carbon storage; enhanced oil recovery (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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