Α Python-based Evaluation of Kazakhstan's Fields for Carbon Capture, Utilization, and Storage Projects

The purpose of this study is to evaluate the feasibility of different oil fields in Kazakhstan for Carbon Capture, Utilization, and Storage (CCUS) projects using advanced algorithms in Python. Using automated methods, the approach greatly simplifies and accelerates the selection process, allowing efficient analysis of large data sets. Taking into account key geological and operational parameters, with particular emphasis on the importance of the Dykstra-Parsons coefficient, the study presents a comprehensive ranking system for evaluating reservoir suitability. This coefficient is critical to accurately assess the fluid displacement efficiency, which significantly influences the selection of candidates for Enhanced Oil Recovery (EOR). The results show that the inclusion of the Dykstra-Parsons coefficient improves the accuracy of field evaluation by accounting for key reservoir heterogeneity factors along with conventional properties. The comparative analysis shows that this approach provides more reliable field selection compared to the existing methods that do not consider this parameter, thereby improving the efficiency of CO2 storage projects.