An eigenvalue-driven framework for the ranking and selection of optimal geological CO2 storage sites

• An eigenvalue-driven framework was applied to select the best geological CO 2 storage sites. • The impacts of various parameters related to petrophysical, and geomechanical properties were analyzed. • Pollution per capita was determined to be the most influential factor in selecting the most appropriate sites. Underground CO 2 storage is vital to carbon capture and storage (CCS) strategies designed to combat climate change by lowering atmospheric CO 2 levels. This method involves capturing CO 2 emissions from industrial sources and storing them in geological formations, such as depleted oil and gas reservoirs and saline aquifers. New techniques, such as Multi-Criteria Decision Making (MCDM), machine learning, and mathematical methods, are increasingly recognized as practical approaches for comparing and selecting suitable sites for underground CO 2 storage. This study employs the mathematical method of the eigenvalue matrix to identify the best locations for CO 2 storage. To accomplish this, 10 parameters related to reservoir characteristics, petrophysical properties, and geomechanical features of 60 sites worldwide were analyzed. The influence coefficients for each of the 10 parameters were determined using the eigenvalue matrix. Among these parameters, pollution per capita had the highest coefficient at 0.259, while pressure had the lowest coefficient at 0.029. Each of the 60 investigated sites was assigned a score based on the coefficients assigned to various parameters. These sites were then classified into three categories: high potential, medium potential, and low potential. The findings of this study can assist in selecting the most suitable site for underground CO 2 storage across different locations worldwide.