# A New CO\({}_{2}\)-EOR Methods Screening Model Based on Interdependency Parameters
## 3 Results and Discussion  
A total of 131 datasets on miscible CO\({}_{2}\)-EOR projects in the US were sorted from 1100 duplicating datasets into 145 datasets on project success and, finally, into 131 datasets for successful and profitable projects. There were some missing data in oil viscosity, oil saturation, net thickness, and MMP, as shown in Figure 4a.  
Moreover, 37 project datasets of immiscible CO\({}_{2}\)-EOR were obtained from OGJ by constructing, extracting, and sorting 164 duplicated initial datasets. Furthermore, an additional 27 project datasets were collected from SPE and Elsevier publications, bringing a total of 64 project datasets originating from several countries. Afterward, 57 datasets from 1986 to 2020 were analyzed, and the remaining 7 datasets were used to implement the newly established screening model. Missing data in Figure 4b were identified for several screening parameters, including oil viscosity, temperature, oil gravity, oil saturation, net thickness, and MMP. The mean imputation and Yellig-Metcalfe empirical correlation were applied to fill the missing data.  
The coefficients of correlation among screening parameters are shown in Figure 5a for miscible CO\({}_{2}\)-EOR and Figure 5b for immiscible CO\({}_{2}\)-EOR. A high coefficient value  
Figure 4: Amount of missing data: (**a**) miscible CO\({}_{2}\)-EOR, (**b**) immiscible CO\({}_{2}\)-EOR.  
Figure 3: Integration of box plot and swarm plot.  
tends to have a significant correlation based on a \(p\)-value smaller than 5% of the t-statistic test shown in Figure 6. Statistically, the MMP screening parameter was significantly correlated to porosity, depth, oil gravity, viscosity, and temperature. Permeability was related substantially to porosity. Oil gravity correlated with porosity, depth, viscosity, and temperature. Oil saturation had an insignificant correlation with all the screening parameters and so, the net thickness. Combining statistical methods and expert domains was needed to match the results from the correlations to the actual physical phenomena. Besides, the expert domain approach served as the basis for determining the correlation and dependency of the screening parameters shown in Figure 7 with the following explanation:  
1. MMP correlates with temperature, oil gravity, and depth, whereas oil gravity correlates with oil viscosity. Therefore MMP, temperature, oil gravity, depth, and oil viscosity are intercorrelated and depend on each other.
2. Porosity correlates with permeability based on reservoir rock properties.
3. Oil saturation and net thickness insignificantly correlate to other screening parameters.  
Figure 7 shows four processes in the resulting screening model. The first calculation is done with the simultaneous model A, then, if appropriate, the process moves to the simultaneous model B and finally to the two individual models. If the four processes meet all the criteria, the reservoir is suitable for implementing CO\({}_{2}\)-EOR.  
As mentioned, this research focused on developing a screening model for miscible and immiscible CO\({}_{2}\)-EOR. These two models are significantly different and have their own uniqueness. The following sections discuss the development and implementation of the screening model for different miscibility conditions of CO\({}_{2}\)-EOR.