# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 4 EOR Screening Modeling
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Set Category** & **Method** & _Precision_ & _Recall_ & _F1 Score_ & **Number** \\ \hline class0 & Steam & 0.99 & 0.98 & 0.98 & 81 \\ class1 & Combustion & 1.00 & 1.00 & 1.00 & 4 \\ class2 & Hot water & 0.00 & 0.00 & 0.00 & 1 \\ class3 & CO\({}_{2}\) miscible & 0.86 & 0.72 & 0.78 & 25 \\ class4 & CO\({}_{2}\) immiscible & 1.00 & 0.33 & 0.50 & 15 \\ class5 & Nitrogen miscible & 0.00 & 0.00 & 0.00 & 1 \\ class6 & Nitrogen immiscible & 0.00 & 0.00 & 0.00 & 1 \\ class8 & Nitrogen immiscible & 0.00 & 0.00 & 0.00 & 1 \\ class9 & Microbial & 1.00 & 1.00 & 1.00 & 1 \\ class10 & Surfactant & 1.00 & 1.00 & 1.00 & 1 \\ class11 & Polymer & 0.63 & 0.68 & 0.65 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Optimized random forest model classification’s _Precision, Recall_, and _F1 Score_. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Set Category** & **Method** & _Precision_ & _Recall_ & _F1 Score_ & **Number** \\ \hline class0 & Steam & 0.99 & 0.98 & 0.98 & 81 \\ class1 & Combustion & 1.00 & 1.00 & 1.00 & 4 \\ class2 & Hot water & 0.00 & 0.00 & 0.00 & 1 \\ class1 & Combustion & 1.00 & 1.00 & 1.00 & 4 \\ class2 & Hot water & 0.00 & 0.00 & 0.00 & 1 \\ class3 & CO\({}_{2}\) miscible & 0.86 & 0.72 & 0.78 & 25 \\ class4 & CO\({}_{2}\) immiscible & 1.00 & 0.33 & 0.50 & 15 \\ class5 & Nitrogen miscible & 0.00 & 0.00 & 0.00 & 1 \\ class6 & Nitrogen immiscible & 0.00 & 0.00 & 0.00 & 1 \\ class7 & Nitrogen immiscible & 0.00 & 0.00 & 0.00 & 1 \\ class8 & Hydrocarbon & & & & \\ class9 & Microbial & 1.00 & 1.00 & 1.00 & 1 \\ class9 & Microbial & 1.00 & 1.00 & 1.00 & 1 \\ class10 & Surfactant & 1.00 & 1.00 & 1.00 & 1 \\ class11 & Polymer & 0.63 & 0.68 & 0.65 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Optimized ANNs model classification’s _Precision, Recall_, and _F1 Score_. The confusion matrices in Figures 13-15 illustrate the prediction results of the test set, where the predictions are presented by different EOR techniques (left) and EOR categories (right), respectively. In the comparison of results, we can see that random forest has the best prediction accuracy for EOR sample classification in the test set. The results show that the test set contains ten EOR techniques, covering three EOR categories: thermal flooding, gas injection flooding, and chemical flooding. In the prediction of the thermal flooding category, steam flooding and combustion reached an accuracy of 1. Due to the small number of samples, techniques such as hot water were wrongly predicted as steam flooding, which still belonged to the thermal flooding category, which does not have significant influence on the enhanced oil recovery in the field of engineering applications. For gas injection flooding, the prediction accuracy of the hydrocarbon gas miscible flooding technique is 92%, and in the prediction category of the CO\({}_{2}\) miscible flooding and CO\({}_{2}\) immiscible flooding technique, more than 85% of the probability predictions are still in the gas drive technique. Due to the small number of actual samples and the prediction accuracy of nitrogen miscible flooding and nitrogen immiscible flooding, the impact on the overall prediction results of gas flooding is small. After the random forest model prediction, 96% of the samples are predicted to be in the gas drive category. Due to the size of the data and reality, a small number of samples are predicted to be in the thermal drive category. In chemical flooding, only microbial flooding and polymer flooding techniques are included. Among the 26 samples, only three samples are predicted to be other types, which achieves an 88% prediction accuracy. The stability and reliability of the aforementioned model in classification tasks are intricately tied to the quality of the dataset we meticulously curated. The authenticity and precision of the data are pivotal, rendering the machine learning model practically feasible.