Open URL

Resource Info

Last Updated: unknown
Format: HTML
Creative Commons Attribution


CO2 Online Solubility Tool

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.

Belongs to Submission

CO2 Online Solubility Tool

Revision Information

File Name Date
CO2 Online Solubility Tool 03-24-2014 | 01:00 PM Eastern

Download Stats for All Revisions

Additional Information

Field Value
created March 24, 2014
folder id root
format HTML
id 9d21965c-98e1-4e3a-83cb-7a93538d88a1
license type cc-by
revision id c45fe714-2bf2-b161-adfd-3f1f0cf9a384
state active
typeofgep Not Applicable