# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 1 Introduction
The cluster analysis of EOR projects provides a new idea of machine learning for EOR screening. Fuzzy decision tree and artificial neural networks (ANNs) algorithms are widely used in reservoir EOR technique screening and research on enhanced oil recovery, which is a further application of machine learning. [PERSON] et al. used the fuzzy decision tree method to sort and classify the EOR techniques, considering the fluid viscosity, oil specific gravity, depth, and saturation rate parameters, and designed an expert system that automatically genera [17]. The neural networks model was used to predict oil field production [18]. Based on more than 1000 successful EOR projects, [PERSON] et al. developed models, such as decision trees and neural networks to predict the category of suitable EOR methods for reservoirs, and verified them in actual reservoirs, demonstrating the reliability of EOR screening results using machine learning techniques [5; 19]. For polymer flooding projects, [PERSON] et al. built a proxy model based on the adaptive training process of neural networks to efficiently obtain suitable polymer flooding methods [20]. [PERSON] et al. developed a tool to determine the EOR method with the highest probability of successful implementation using an artificial evolutionary neural network-based approach based on seven reservoir fluid parameters such as porosity and permeability [21]. [PERSON] et al. used an artificial neural network to predict oil production and applied it to reservoir production optimization [22]. Combining the experience of polymer flooding, [PERSON] used the artificial neural network model to establish a predictive overall rheology model, which can accurately predict the viscosity of polymers for researchers [23]. The application of neural network algorithms improves the accuracy and efficiency of classification due to its fast characteristics. The random forest (RF) algorithm and the support vector machines (SVM) algorithm have been tried by researchers in EOR screening decision making, where it enhanced oil recovery and achieved good results. [PERSON] et al. [24] and [PERSON] et al. [25] used reservoir information as input parameters to establish regression or classification models, and the random forest model provided a basis for decision making. In studying the relationship between oil recovery and geological characteristics, researchers established a random forest prediction model to provide a basis for oil reservoir exploitation [26; 27]. [PERSON] et al. established and compared several machine learning models in the study of the relationship between surfactant concentration, porosity, and permeability, and the random forest model achieves better accuracy [28]. The RF algorithm has better performance in classification. Table 1 incorporates a detailed comparison table to summarize and compare existing studies. From the table, we can see what research CEORS and AEORS are currently doing, which makes the current work clearer. The aforementioned content provides an overview of the EOR technique selection process. To offer an efficient screening solution, this study falls under the umbrella of AEORS. AEORS establishes a classification model using the collected dataset, enabling artificial intelligence to discern the intricate relationships between the dataset parameters and the EOR technique. The comprehensive nature and depth of our research underscore its strengths. After conducting a comprehensive review of both CEORS and AEORS techniques, we identified that AEORS represents the current focal point of research. Common challenges encountered in prior EOR research employing machine learning, including data scarcity, ambiguous model evaluation, and unclear model optimization, which have been meticulously addressed in our study. The objective of our work is to pioneer a novel EOR screening methodology by implementing multiple algorithms across global EOR projects. This methodology serves as a valuable tool, aiding engineers in making rapid and informed decisions.