## 2 Methodology
### Data Preprocessing  
This dataset includes 369,600 data points collected using the black oil mathematical model simulations. It contains parameters such as porosity, pressure, gas viscosity, oil viscosity, and average oil saturation. The initial dataset, as in Table 2, contains model parameters such as oil viscosity (VISC_OIL), gas viscosity (VISC_GAS), porosity (POROSITY), pressure (pressure), water saturation (Sw_avg), oil saturation (So avg), and oil recovery factor (oil_recovery_factor) values. These data are used to train the model, which is then used to predict the oil recovery factor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline \(k\) & Absolute permeability & 0.001 \\ \(\mu_{w}\) & Water viscosity & 0.09 \\ \(\mu_{o}\) & Oil viscosity & 0.3 \\ \(\mu_{g}\) & Gas viscosity & 0.01 \\ \(\varnothing\) & Porosity & 0.25 \\ \(P_{inj}\) & Injection pressure & 0.5 \\ \(P_{init}\) & Initial pressure & 0.3 \\ \(P_{prod}\) & Produced pressure & 0.1 \\ \(S_{w\_inj}\) & Water injection saturation & 1.0 \\ \(S_{w\_init}\) & Water initial saturation & 0.2 \\ \(S_{o\_init}\) & Oil initial saturation & 0.7 \\ \(S_{g\_init}\) & Gas initial saturation & 0.1 \\ \(B_{w}\) & Water formation volume factor & 1 \\ \(B_{o}\) & Oil formation volume factor & 1.2 \\ \(B_{g}\) & Gas formation volume factor & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of model. There is a relevant correlation between the variables in the dataset. The correlation matrix in Figure 2 represents the relationships between the various parameters in the dataset. Each cell in the matrix indicates the correlation coefficient between the pairs of variables, where values close to \(1\) or \(-1\) suggest strong positive or negative correlations, respectively, and values near \(0\) indicate a lack of correlation. There is a strong positive correlation between the pressure and both the oil recovery factor and porosity, with \(0.84\) and \(1.00\) Pearson correlation coefficients, respectively, which means that higher average reservoir pressures are associated with increased oil recovery and greater porosity. The gas recovery factor exhibits a strong negative correlation (\(-0.73\)) with porosity and pressure, implying that in scenarios where porosity and pressure are high, the gas recovery factor tends to be lower. The average water saturation shows a near-perfect negative correlation (\(-0.96\)) with the average oil saturation, which is expected as increases in one typically result in decreases in the other within the reservoir. The correlations between the viscosity of the oil and other parameters are generally weak, with the strongest negative correlation being \(-0.43\) with the gas recovery  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**k**} & \multirow{2}{*}{**Visc\_oil**} & \multirow{2}{*}{**Visc\_gas**} & \multirow{2}{*}{**Porosity**} & \multicolumn{2}{c}{**Oil\_Recover**} & \multicolumn{2}{c}{**Gas\_Recovery**} & \multirow{2}{*}{**Sw\_avg**} & \multirow{2}{*}{**So\_avg**} & \multirow{2}{*}{**Sg\_avg**} & \multirow{2}{*}{**Pressure**} \\  & & & & & & & & & & & \\
0.0001 & 0.3 & 0.  
factor, suggesting the minimal influence of oil viscosity on the gas recovery under the conditions modeled. The target variable, the oil recovery factor, exhibits strong correlations with several other features within the dataset, which is advantageous for the model training. This indicates a robust predictive foundation and increases the likelihood of developing an effective model that accurately forecasts the oil recovery based on these interrelated parameters. In order to understand the models that will later be trained, it is important for the types of relationships exhibited between the variables to be understood. The scatterplots shown in Figure 3 help us to identify the relationships between multiple variables in the dataset. From the scatterplots, some variables exhibit linear relationships.