# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 1 Introduction  
Oil is a dominant energy fuel to many industries and maximizing oil production is crucial to meet the growing energy demands [1]. It is estimated that about two-thirds of the crude oil remain in the reservoir after primary and secondary recovery during oil production [2], and enhanced oil recovery (EOR) techniques offer the prospect of producing 30% to 60% or more of a reservoir's original oil [3]. Therefore, EOR techniques are important to ensure oil production and meet the global energy demand. Currently, more than 20 different EOR techniques have been successfully implemented in oilfields [4], where finding the most suitable EOR technique for a new candidate reservoir has become a big challenge for reservoir engineers because this decision requires extensive knowledge on geological engineering, reservoir engineering, economics, etc. Researchers have conducted a lot of analysis and discussion in the screening process of the EOR technique, and the directions of the discussion can be roughly classified into Conventional EOR Screening (CEORS) and Advanced EOR Screening (AEORS) [5]. The CEORS is a method of finding a suitable EOR technique based on the corresponding range of reservoir and fluid parameters via statistical analysis methods combined with Taber tables, etc. In 1997, [PERSON] et al. proposed screening criteria by analyzing the previous EOR projects. These criteria are expressed in the form of an acceptable range of certain reservoir/fluid properties to select the appropriate EOR technique based on the oilfield reservoir/fluid properties [6]. In 2011, [PERSON] et al. updated the screening criteria proposed by [PERSON] et al. based on EOR projects after 1998 [7]. Statistical tools such as histograms, scatter plots, and box plots are of great use in analyzing parameter ranges in the data from polymer projects and steam flooding projects [8; 9; 10]. The CEORS has the capability to provide valuable and detailed field guidelines for the candidate reservoirs, but considering the limited number of real projects and the different formulations of standards, the results obtained via the conventional method does not has discriminative power because the results could suggest multiple EOR schemes at the same time for a new EOR candidate reservoir [11]. In consideration of efficiency and economy in the field of oil production, the AEORS provides a new way of thinking for field personnel. The AEORS is a method of using machine learning (ML) technology to learn the hidden patterns or relationships in the collected dataset and to predict the suitable EOR technique for candidate reservoirs. In the past ten years, with the development of computer technology, machine learning technology has been widely used in the EOR screening decision-making process. Researchers have used machine learning methods such as neural networks, adaptive fuzzy inference systems, support vector machines, random forest, and gradient enhancement to screen for EOR techniques. In 2002, [PERSON] et al. proposed a method of using a machine learning algorithm to draw screening criteria for EOR [12] and analyzed six cluster analysis results according to the dataset to judge the items in each cluster category. This is the earlier application of machine learning in EOR screening. [PERSON] et al. used the principal component analysis method to reduce the dimension of parameters and established a Bayesian EOR selection model based on reservoir/fluid properties to obtain the target technology for further research to establish a hierarchical clustering model [13; 14]. [PERSON] et al. used a hierarchical clustering algorithm combined with principal component analysis to analyze global steam-flooding EOR projects [15]. The physicochemical characteristics of the injected reservoir fluid were further correlated with rock characteristics, porosity, and reservoir-specific information, and a Bayesian classifier was used to build the model, achieving 100% accuracy in the validation set [16].