A comprehensive review of efficient capacity estimation for large-scale CO2 geological storage.

Geological carbon storage and sequestration (GCS), a key method within carbon capture and sequestration (CCS), is globally recognized as an effective strategy to reduce atmospheric carbon dioxide (CO 2) levels and combat the greenhouse effect. However, discrepancies between projected and actual storage capacities, especially in large-scale CO 2 storage, have raised concerns among stakeholders regarding potential overestimations. This paper reviews the definitions and methods used to estimate storage capacity, highlighting variations and providing a practical guide for predictions while suggesting directions for future research. We discuss numerous analytical and numerical models that account for dynamic constraints such as safety considerations, trapping mechanisms, and reservoir performance, primarily focusing on local scales. These models enhance the accuracy of capacity estimations over conventional static models by quantifying CO 2 storage capacity both spatially and temporally. Additionally, this review underscores the need for sophisticated evaluations of large-scale storage. We introduce two pivotal tools designed for basin-scale capacity estimation and discuss the challenges associated with expanding dynamic capacity assessments to larger scales. In conclusion, the paper explores the burgeoning use of machine learning-based models, advocating for future research efforts to leverage machine learning in developing integrated tools that offer more comprehensive and precise capacity estimations for GCS. • Reviews CO2 storage capacity definitions, noting the discrepancies. • Dynamic capacity: safety, trapping, reservoir performance limits beyond static. • Reviews models, uncertainties and influencing factors for capacity estimation. • Focusing on large-scale storage, introduces two efficient estimation tools. • Addresses needs, challenges and techniques of scaling up dynamic capacity. [ABSTRACT FROM AUTHOR]