# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 5 Conclusions  
Due to the efficient and accurate nature of machine learning algorithms, we have integrated them into the process of selecting EOR techniques in reservoir engineering. Aiming at the EOR screening scheme, this study developed several prediction mechanisms with machine learning algorithm models as the core. The main results are as follows.  
* We have compiled a comprehensive dataset comprising 956 successful global EOR projects, incorporating essential reservoir fluid attributes and supplemented with complete net thickness information.
* We established models based on RF, ANNs, naive Bayes, SVM, and decision trees to predict EOR project categories for the given input parameters. Subsequently, we first compared the accuracy of these algorithms and we conducted a comprehensive analysis and comparison of RF, ANNs, and naive Bayes, evaluating the models from the perspectives of precision, recall, and the F1 score.
* In the process of model optimization, we considered the scale of the dataset and various optimization techniques. Consequently, we chose random search as the method to determine the optimal parameters.
* After the comparison of these models, the RF model has the highest accuracy on the test set, reaching 91.15%, which provides an important basis for the real-time and efficient enhanced oil recovery screening decision of the oil field.
* Overall, this study provides reservoir engineers with a convenient and quick way to confirm the EOR technique solution suitable for the reservoir. In future research, advanced deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) can be applied in modeling EOR screening to enhance the predictive accuracy of EOR screening models. Furthermore, there is a need for research focusing on the environmental and economic implications of different EOR methods. Taking this into account in EOR modeling, scholars can make a significant contribution to the development of EOR modeling and promote implementing oil and sustainable solutions in the oil and gas industry.  
Methodology, N.Z.; Validation, S.S., H.W. and M.Z.; Resources, A.Z.; Data curation, P.W.; Writing--original draft, S.S.; Writing--review & editing, N.Z.; Funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Science Foundation of China (grants 52204041 and 71971130), Natural Science Foundation of Shandong Province (grant ZR2021 QF076), Project of Shandong Province Higher Educational \"Youth Innovation Science and Technology Plan\" (grant 2021 KJ060), and Taishan Scholar Young Talent Program (No.tsqn202306202). And the APC was funded by Natural Science Foundation of Shandong Province (grant ZR2021 QF076).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors acknowledge Natural Science Foundation of Shandong Province.  
Author [PERSON] was employed by the company Changqing Oilfield Branch of PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.