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Multimodel Predictive System for Carbon Dioxide Solubility in Saline Formation Waters
- Wang, Zan, Small, Mitchell
J., Karamalidis, Athanasios K.
- Environmental Science & Technology 2013 v.47 no.3 pp. 1407-1415
- artificial intelligence, carbon dioxide, carbon sequestration, mathematical models, prediction, salt concentration, sodium chloride, solubility, statistical analysis, temperature
- The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO₂ solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304–433 K, pressure range 74–500 bar, and salt concentration range 0–7 m (NaCl equivalent), using 173 published CO₂ solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO₂ solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO₂ solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications.