Search for a command to run...
CO2 storage could benefit hard-to-abate industries that face significant challenges in reducing their greenhouse gas emissions. To mitigate global warming without affecting industrial production, there is an urgent need to develop large-scale CO2 storage projects, which, among other factors, depend on reliable forecasts of subsurface CO2 behavior.Knowledge of the fluid flow in depleted or producing reservoirs provides an important background, but cannot be directly applied to forecast the shape and position of the injected CO2 mass. In deep saline aquifers, one of the main promising storage sites with relatively high storage capacity, injecting CO2 (a lower-viscose fluid) into brine (a higher-viscose fluid) is sensitive to reservoir heterogeneities. Due to this viscosity contrast, CO2 retention is affected by permeability variations of less than one order of magnitude. Consequently, any permeability contrast within the reservoir can favor structural-stratigraphic and residual trapping mechanisms. Moreover, injection affects not only flow behavior near the wellbore but also geomechanical responses over larger areas, requiring a multiscale approach to represent the deep saline aquifer's heterogeneity. Given these particularities and considering that CO2 project forecasts rely primarily on reservoir modeling, questions commonly arise about which technique to use for constructing 3D geological models.We present a comparative analysis of two stochastic methods for modeling reservoir properties: (1) Sequential Indicator Simulation/Sequential Gaussian Simulation (SIS/SGS) and (2) Multiple-Point Statistics (MPS). Both methods were used to build geological models of the Jureia-Ponta Aguda Formation, a deep saline aquifer in the offshore Santos Basin, Brazil. The formation is a 2,000 m-thick reservoir composed of fluvio-deltaic to shallow marine sediments occurring at depths below 800 m. Based on a dataset of 40 wells and 2D/3D seismic data, an area of 4,000 km2 was modeled at a 1:100,000 mapping scale, with representative geologic elements having a minimum dimension of 1 km. The comparison focuses on the dynamic response of each model under CO2 injection. Key inputs for decision-making in a storage project, including the well injectivity and the area affected by pressure variations and CO2 saturation, are quantified to assess the impact of the reservoir modeling technique on CO2 subsurface behavior.The SIS/SGS model exhibits a more continuous distribution of reservoir properties, whereas the MPS model better captures the geometry of geological elements, resulting in a more discretized spatial distribution of facies, porosity, and permeability. In a direct comparison, the two models produce different fluid-flow behavior, and the MPS technique appears to be the best choice at first glance because it more accurately represents the inputs. However, the sparse subsurface dataset carries a high level of uncertainty, and different geologic scenarios have a greater impact on the CO2 plume geometry, the pressure front size, and well injectivity than the modeling methodology itself.As the choice of modeling algorithm becomes less critical in the uncertainty process, addressing CO2 subsurface behavior should focus on the range of possible geologic scenarios. The structural-stratigraphic-sedimentologic framework, along with capillary pressure and drainage/inhibition permeability curves, is the key factor in reservoir models that support decision-making for a storage project.