The geological sequestration of CO2 in deep saline aquifers is one of the most effective strategies to reduce greenhouse emissions from the stationary point sources of CO2. However, it is a complex task to quantify the storage capacity of an aquifer as it is a function of various geological characteristics and operational decisions. This study applies physics-based proxy modeling by using multiple machine learning (ML) models to predict the CO2 trapping scenarios in a deep saline aquifer. A compositional reservoir simulator was used to develop a base case proxy model to simulate the CO2 trapping mechanisms (i.e., residual, solubility, and mineral trapping) for 275 years following a 25-year CO2 injection period in a deep saline aquifer. An expansive dataset comprising 19,800 data points was generated by varying several key geological and decision parameters to simulate multiple iterations of the base case model. The dataset was used to develop, train, and validate four robust ML models--multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). We analyzed the sequestered CO2 using the ML models by residual, solubility, and mineral trapping mechanisms. Based on the statistical accuracy results, with a coefficient of determination (R2) value of over 0.999, both RF and XGB had an excel-lent predictive ability for the cross-validated dataset. The proposed XGB model has the best CO2 trapping performance prediction with R2 values of 0.99988, 0.99968, and 0.99985 for residual trap-ping, mineralized trapping, and dissolution trapping mechanisms, respectively. Furthermore, a feature importance analysis for the RF algorithm identified reservoir monitoring time as the most crit-ical feature dictating changes in CO2 trapping performance, while relative permeability hysteresis, permeability, and porosity of the reservoir were some of the key geological parameters. For XGB, however, the importance of uncertain geologic parameters varied based on different trapping mech-anisms. The findings from this study show that the physics-based smart proxy models can be used as a robust predictive tool to estimate the sequestration of CO2 in deep saline aquifers with similar reservoir characteristics.


© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).



Date of publication

Summer 6-14-2022



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