Comparative Analysis of Parametric and Non-Parametric Machine Learning Models for CO2 Solubility Estimation
Authors: Darkwah-Owusu Victor , Turkson Joshua Nsiah , Yusof Muhammad Aslam Md , Tackie-Otoo Bennet Nii , Sokama-Neuyam Yen Adams , Fjelde Ingebret , Gbillah Louis Kwesi , Otchere Daniel Asante 
Published: February 2025

This study investigates the application of parametric and non-parametric supervised machine learning techniques for CO2 solubility estimation in brine. CO2 solubility in brine is crucial in estimating the CO2 storage capacity of geological formations, assessing CO2 surface mixing, and evaluating the performance of CO2-enhanced oil recovery projects. Hence this research aims to provide an alternative approach for accurate CO2 solubility prediction, critical for various applications in CO2 geo-storage and EOR projects. Independent variables including pressure (0.1–40 MPa), temperature (273–474 K), salinity (0–6 mol/kg), and salt type (NaCl, CaCl2, and MgCl2) were sourced from the credible scientific literature. To ensure data quality, a comprehensive data exploration process was conducted. Subsequently, the dataset was then split into training (70%) and testing (30%) sets for model development and evaluation. Hyperparameter tuning was employed to optimize model performance. Statistical metrics and visualizations were also used to evaluate model performance. The model reliability and the statistical validity of the dataset were assessed using William’s plot. Sensitivity analysis and feature importance were also explored using correlation and model-intrinsic methods. The findings of this study showed that non-parametric models (random forest (RF) and decision tree (DT)) significantly outperformed the parametric model (multiple linear regression (MLR)). Specifically, RF and DT exhibited exceptional general behaviour and robustness, achieving an overall coefficient of determination&gt;0.97, mean absolute error&lt;0.08 mol/kg, and mean squared error&lt;0.02 mol2/kg2 while MLR yielded 0.76, 0.272 mol/kg, and 0.155 mol2/kg2, respectively. This disparity is attributed to the inherent limitations of parametric models in capturing complex relationships between CO2 solubility and its influencing factors. The RF model was adjudged the best-performing model and demonstrated considerable prediction accuracy compared to the Søreide-Whitson with improved binary interaction parameter (m-SW) and the activity-fugacity models during external validation. The paradigm also exhibited superior predictive performance over genetic algorithm-derived correlation and radial basis function neural network proposed by other scholars. The correlation and model intrinsic methods also revealed that pressure exerted the greatest positive influence on CO2 solubility in aqueous systems, while temperature and salinity showed negative effects. Additionally, pressure and salt type were identified as the most and least influential variables, respectively. William’s plot analysis indicated ~2% of the total dataset as vertical suspect and good high-leverage instances. This signifies the authenticity and reliability of the database and constructed models. The investigation uncovers insights into the impact of pressure, temperature, and salinity on CO2 dissolution. This research represents a significant step forward in understanding and enhancing CO2 storage optimization strategies while ensuring efficient resource utilization.