Toward Estimating CO2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO2 Dissolution Trapping in Saline Aquifers

Xinyuan Zou; Yingting Zhu; Jing Lv; Yuchi Zhou; Bin Ding; Weidong Liu; Kai Xiao; Qun Zhang

doi:10.1021/acsomega.3c07962

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers

ACS Omega. 2024 Jan 17;9(4):4705-4720. doi: 10.1021/acsomega.3c07962. eCollection 2024 Jan 30.

Authors

Xinyuan Zou^{1

2}, Yingting Zhu^{2

3}, Jing Lv^{2

3}, Yuchi Zhou⁴, Bin Ding^{2

3}, Weidong Liu^{2

3}, Kai Xiao⁵, Qun Zhang^{1

2}

Affiliations

¹ State Key Laboratory of Enhanced Oil Recovery, Research Institute of Petroleum Exploration and Development, CNPC, Beijing 100083, China.
² Research Institute of Petroleum Exploration & Development, Beijing 100083, China.
³ Key Laboratory of Oilfield Chemistry of CNPC, Beijing 100083, China.
⁴ Oil and Gas engineering research Institute, Petrochina Jilin Oilfield Company, Songyuan 138000, China.
⁵ State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China.

Abstract

Predicting carbon dioxide (CO₂) solubility in water and brine is crucial for understanding carbon capture and storage (CCS) processes. Accurate solubility predictions inform the feasibility and effectiveness of CO₂ dissolution trapping, a key mechanism in carbon sequestration in saline aquifers. In this work, a comprehensive data set comprising 1278 experimental solubility data points for CO₂-brine systems was assembled, encompassing diverse operating conditions. These data encompassed brines containing six different salts: NaCl, KCl, NaHCO₃, CaCl₂, MgCl₂, and Na₂SO₄. Also, this databank encompassed temperature spanning from 273.15 to 453.15 K and a pressure range spanning 0.06-100 MPa. To model this solubility databank, cascade forward neural network (CFNN) and generalized regression neural network (GRNN) were employed. Furthermore, three optimization algorithms, namely, Bayesian Regularization (BR), Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton, and Levenberg-Marquardt (LM), were applied to enhance the performance of the CFNN models. The CFNN-LM model showcased average absolute percent relative error (AAPRE) values of 5.37% for the overall data set, 5.26% for the training subset, and 5.85% for the testing subset. Overall, the CFNN-LM model stands out as the most accurate among the models crafted in this study, boasting the highest overall R² value of 0.9949 among the other models. Based on sensitivity analysis, pressure exerts the most significant influence and stands as the sole parameter with a positive impact on CO₂ solubility in brine. Conversely, temperature and the concentration of all six salts considered in the model exhibited a negative impact. All salts exert a negative impact on CO₂ solubility due to their salting-out effect, with varying degrees of influence. The salting-out effects of the salts can be ranked as follows: from the most pronounced to the least: MgCl₂ > CaCl₂ > NaCl > KCl > NaHCO₃ > Na₂SO₄. By employing the leverage approach, only a few instances of potential suspected and out-of-leverage data were found. The relatively low count of identified potential suspected and out-of-leverage data, given the expansive solubility database, underscores the reliability and accuracy of both the data set and the CFNN-LM model's performance in this survey.