Modeling solubility of carbon dioxide in reservoir brine via smart techniques: Application to carbon dioxide storage

Abstract

Nowadays, reduction of carbon dioxide (CO2) in the atmosphere via sequestration in deep saline aquifers is studied by numerous researchers. Solubility of CO2 in reservoir brine is a most important parameter in CO2 sequestration saline aquifers. Owing to the importance of this issue, in this paper different methods based on the concept of artificial intelligence, such as particle swarm optimization (PSO), artificial neural network (ANN) and hybrid approaches, are evolved to specify solubility of CO2 in brine at different conditions. The developed intelligent approaches are examined by comparing with precise actual data reported in previous publications. The results gained from the developed intelligent approaches are contrasted with the corresponding real CO2-brine solubility data. The average relative absolute error between the real and the corresponding model prediction data is found to be less than 1% for the hybrid PSO and genetic algorithm model.