## 3 Results and Discussion
### Analysis of Machine Learning Results  
To compare the CMG numerical simulation results with the machine learning regression prediction results, this work builds a database of 216 CMG numerical simulation models, of which 70% are used as the training set and 30% as the test set. The numerical simulation results of the test set are employed as the true values for verifying the accuracy of the machine learning prediction. The result plots for the three labels of cumulative oil production, CO\({}_{2}\) storage amount, and CO\({}_{2}\) storage efficiency are shown in Figure 12. The predicted curve made by the random forest regression algorithm is very close to the true value curve in terms of cumulative oil production, CO\({}_{2}\) storage amount, and CO\({}_{2}\) storage efficiency.  
Figure 12: _Cont._The relative deviation of cumulative oil production, CO\({}_{2}\) storage amount, and CO\({}_{2}\) storage efficiency for each scheme are calculated and shown in Figure 13 for further discussion. The relative deviation of cumulative oil production between prediction and true values ranges from \(-4.76\%\) to \(2.51\%\), the maximum relative deviation is \(-4.76\%\), and the average absolute relative deviation is \(1.10\%\). The maximum relative deviation of CO\({}_{2}\) storage amount between prediction and true values is \(-15.15\%\). Except for this, the relative deviation for all other scenarios ranges from \(-10.67\%\) to \(5.23\%\). The average absolute relative deviation for predicting CO\({}_{2}\) storage amount is \(3.04\%\). For the prediction of CO\({}_{2}\) storage efficiency, except for three scenarios with large relative deviations of \(-13.35\%\), \(-11.93\%\), and \(8.72\%\), respectively, the relative deviation for all other scenarios ranges from \(-6.31\%\) to \(4.55\%\). The average absolute relative deviation for predicting CO\({}_{2}\) storage efficiency is \(2.24\%\).  
Figure 12: Comparison of the predicted values by random forest algorithm with the true values in (**a**) cumulative oil production, (**b**) CO\({}_{2}\) storage amount, and (**c**) CO\({}_{2}\) storage efficiency.  
Figure 13: _Cont._  
According to the curve between the predicted value and the true value, and their relative deviation, the random forest regression algorithm can accurately predict the CO2-WAG production. Therefore, the cumulative oil production, CO2 storage amount, and CO2 storage efficiency can be accurately estimated by this method for the cases of various periods, fluid injection rates, and water-gas ratios.  
Moreover, compared to the simulation method, the machine learning-assisted prediction method can save the time of adjusting the CMG model and running data files. It takes about 30 s to run a case of the CMG model and 108 min for all of the 216 cases in this study. The machine learning method employed in this work only spends 10 s predicting all of the scenarios, which shows a big advantage in computational efficiency compared to the CMG simulation method. If the grid of the CMG model is refined, the timesaving advantage of the machine learning method will be much huger. Therefore, the machine learning-assisted method can greatly improve the prediction efficiency of CO2-WAG and will be suitable for injection parameter optimization.  
Figure 13: Relative deviation between the predicted value and true value in (**a**) cumulative oil production, (**b**) CO2 storage amount, and (**c**) CO2 storage efficiency.