Site screening is a critical step in Carbon Capture and Storage (CCS) projects, particularly in saline aquifers, where CO2 plume migration is influenced by reservoir heterogeneity. While classical methods like the Dykstra-Parsons index quantify heterogeneity, they do not focus on spatial arrangement of permeability contrasts, which significantly affect CO2 flow paths. To address this, we propose an interdisciplinary approach that integrates the Dijkstra algorithm, a computational tool widely used in solving shortest path problems, to map directed tortuosity in reservoirs.

By transforming a 2D reservoir grid into a graph, where nodes represent grid cells and edges represent permeability relationships, the Dijkstra algorithm identifies the shortest path from injection points to the reservoir top. This allows for a fast and effective evaluation of tortuosity, offering a computationally efficient alternative to traditional numerical simulations. The methodology highlights how spatially organized heterogeneities influence CO2 trapping mechanisms and provides valuable insights for site screening, well placement, and reservoir comparison in CCS projects.

This approach demonstrates the potential of combining classical reservoir characterization techniques with advanced computational algorithms to optimize CCS site evaluation and support energy transition initiatives.

Regional-scale saline aquifers are promising candidates for geological CO2 storage but present significant modeling challenges due to their vast extent, heterogeneity, and limited subsurface data. This study introduces a multiscale modeling framework that was applied to assess CO2 storage in the Ponta Aguda saline aquifer (Santos Basin, Brazil, 40,000 km2 area). Consistency of the multiscale models is checked by combining boundaries conditions for pressure match and verification of trapping mechanisms representativity. Four different methods were evaluated regarding the trapping mechanisms accuracy in coarse models: Local Grid Refinement, Effective Values, and Algebraic Dynamic Multilevel. Compositional simulations conducted with CMG-GEM and DARSim2 demonstrate that coarse-scale models systematically overestimate CO2 trapping due to numerical artifacts, particularly in solubility and hysteresis behavior. These artifacts arise from mismatched CO2/brine volumes in large cells, leading to artificially enhanced trapping efficiency. Among the evaluated methods, Algebraic Dynamic Multilevel delivers the most reliable predictions, providing a general solution that aligns closely with fine-scale reference simulations while remaining computationally feasible. The results show the importance of scale-consistent modeling approaches for accurate CO2 storage assessment and highlight the risks of relying on overly simplified coarse models in the design and optimization of carbon storage projects in large saline aquifers.

Site screening is the process of defining the place where a Carbon, Capture, and Storage (CCS) project could be developed. Applied in the early stages of evaluation, this process involves several critical variables that should be analyzed. For example, the distance of CO2 sources, injection forecasting, flow assurance, storage capacity, well injectivity, and the feasibility of monitoring methods. Once these major factors indicate a potential viable project, quantifying reservoir heterogeneities and their impacts on the geometry of CO2 plume is essential to develop optimized monitoring plans and select areas that are favorable for lower monitoring costs. This paper presents a methodology for quantifying heterogeneities in 3D reservoir models of a deep saline formation and correlated them with CO2 plume radius obtained from flow simulations. The variance of the geometric mean permeability, calculated according to the dimensions of geological elements, was used as a reservoir heterogeneity index that impacts CO2 plume behavior. Based on this analysis, it was possible to prioritize two subregions of 150 km² each as candidates for further detailed mapping within an area of nearly 4,000 km² in the shallow waters of the Santos Basin, Brazil.

Brazil’s industrial emissions are 180 million tons of CO2 per year, and approximately 60% of these emissions are coming from industrial clusters located in the southeast. Tthe development of new offshore storage locations in this region is hence of strategic importance for future Carbon Capture and Storage (CCS) projects in Brazil. This study presents an evaluation of CO2 storage in the deep saline aquifers of the Jureia-Ponta Aguda formation, a gigaton-scale storage resource located in the shallow waters of Santos Basin that has the potential to support the development of at least three larger CCS hubs, each with a target injection of 1Gt of CO2. We show how Lorenz Coefficient map allows us to screen the pressure influence areas for each hub by linking reservoir heterogeneity to the spatio-temporal evolution of the pressure front, thereby identifying potential risks of pressure interference between neighboring CCS hubs.

Disused oil and gas reservoirs and saline aquifers are promising candidates for Carbon Dioxide (CO2) storage, which is key for reducing emissions to the atmosphere. Ensuring safe storage of CO2 requires a thorough assessment of leakage risks and cap rock efficiency, along with continuous monitoring of stress field changes through seismic data. In Brazil, shallow-water, sandstone-rich aquifers near industrial hubs present strong potential for Carbon Capture and Storage (CCS). The Ponta Aguda deep saline formation, located in the Santos Basin, represents a gigaton-scale storage resource that could support CO2 storage hubs in the country’s most industrialized region. However, challenges remain in reservoir geomechanics and high-resolution seismic monitoring due to environmental legislation restrictions, complicating predictive modeling efforts. To address these challenges, this study employs simulator-to-seismic modeling, integrating static and dynamic reservoir properties. Fluid substitution was applied to model the elastic properties of CO2-saturated rocks, and a Petro-elastic Model (PEM) was designed to capture pressure and saturation effects. A 3D convolution modeling approach using point-spread functions (PSFs) was then implemented to enhance seismic resolution while accounting for acquisition geometry and overburden effects. This methodology aims to optimize monitoring strategies, reduce uncertainty, and ensure the long-term environmental safety of CO2 storage projects.