Development of a new CO2 EOR screening approach focused on deep-depth reservoirs

Carbon dioxide (CO2) enhanced oil recovery (EOR) is an important technology with the dual benefits of increasing oil production and reducing greenhouse gas emissions. Reservoir screening is crucial for the successful application of CO2 EOR; however, determining CO2 EOR feasibility for a specific field can be a time- and resource-intensive process. Traditional screening criteria can be overly arbitrary, making it difficult to accurately assess whether a field is suitable for CO2 EOR—failing to meet a single requirement may result in an unsuitable judgment. Existing intelligent screening tools primarily focus on classification algorithms to develop predictive models for CO2 EOR feasibility. While these tools are accurate for shallow-depth reservoirs, they struggle to predict CO2 EOR in mid- and deep-depth reservoirs accurately. In this study, an EOR database was created based on 464 EOR projects from 18 countries. Reservoir data from the Williston Basin, TORIS (Tertiary Oil Recovery Information System), and Alberta Basin were also collected to further validate the feasibility of the proposed approach. A new screening criterion was developed based on the boxplot analysis results of the collected worldwide EOR projects and existing CO2 EOR screening guidelines. Weight factors for parameters were determined using the importance permutation technique and the proposed classification algorithms to minimize bias. An innovative CO2 EOR scoring approach was developed using membership functions, composite screening scores, and the six machine learning algorithms. The results showed high prediction accuracy for the worldwide EOR projects database with R-squared values ranging from 0.91 to 0.98. The proposed screening system was further employed to evaluate the prediction accuracy for the mid- and deep-depth reservoirs. The results showed prediction rates ranging from 86% to 92% compared to the analytical solutions. Among these six regression models, random forest outperformed the others with the most stable performance in both the testing phase and case studies with the R-squared value and root mean square error of 0.97 and 4.8, respectively. The proposed screening tool can be further applied to provide recommendations on the feasibility of CO2 EOR in mid- and deep-depth reservoirs.