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A viable means of CO2 reduction in the atmosphere is to capture and concentrate CO2 from large point sources such as power plants and petroleum refineries and store it by underground injection. The main concern for commercial scale CO2 sequestration in geologic formations is the uncertainties associated with this process. The risks involved in different stages of a CO2 sequestration project are related to geological and operational uncertainties. This paper presents the application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2 sequestration project in which CO2 were injected into a depleted gas reservoir. This project is a part of the National Risk Assessment Partnership (NRAP)’s research on CO2 sequestration process. An SRM is a customized model that accurately mimics reservoir simulation behavior by using Artificial Intelligence & Data Mining techniques. Initial steps for developing the SRM included constructing a reservoir simulation model with a commercial software, history matching the model with available field data and then running the model under different operational scenarios or/and different geological realizations. The process was followed by extracting some static and dynamic data from a handful of simulation runs to construct a spatio-temporal database that is representative of the process bein