This review describes the main geological and geomechanical aspects of CO2-injection projects in the Brazilian Pre-Salt reservoirs, focusing on the storage potential and geomechanical aspects of CO2 injection. The Pre-Salt reservoirs in the Santos Basin offer favorable conditions for CCS due to their geological characteristics and existing infrastructure. The thick evaporite caprock, primarily composed of halite, acts as an efficient seal against CO2 migration. The CO2-injection in the Pre-Salt has been active since 2010, with significant amounts of CO2 already stored in the reservoirs. The volumetric assessment estimates the static storage capacity of the Pre-Salt reservoirs to be over 3.3 Gt of CO2, considering only the four fields currently undergoing injection. Geomechanical constraints, including the maximum injection pressure and caprock integrity, are crucial considerations for safe CCS operations. The high stress regime and the hydrostatic state of the caprock minimize the risk of fracturing during injection. Furthermore, dynamic storage capacity calculations indicate the feasibility of injecting CO2 into Pre-Salt reservoirs. This review provides insights into the current state and future prospects of CO2-injection projects in the Brazilian Pre-Salt, contributing to the development of sustainable carbon mitigation strategies in the region.@en

Featured Application: This study emphasizes the importance of comprehensive monitoring, calibration, and optimization of storage strategies in a saline aquifer. It also highlights the need to manage geomechanical risks and uncertainties. By understanding these risks and employing suitable monitoring techniques, the integrity and safety of GCS can be ensured, contributing to the reduction of CO ₂ emissions. Geological Carbon Storage (GCS) involves storing CO ₂ emissions in geological formations, where safe containment is challenged by structural and stratigraphic trapping and caprock integrity. This study investigates flow and geomechanical responses to CO ₂ injection based on a Brazilian offshore reservoir model, highlighting the critical interplay between rock properties, injection rates, pressure changes, and ground displacements. The findings indicate centimeter-scale ground uplift and question the conventional selection of the wellhead as a monitoring site, as it might not be optimal due to the reservoir’s complexity and the nature of the injection process. This study addresses the importance of comprehensive sensitivity analyses on geomechanical properties and injection rates for advancing GCS by improving monitoring strategies and risk management. Furthermore, this study explores the geomechanical effects of modeling flow in the caprock, highlighting the role of pressure dissipation within the caprock. These insights are vital for advancing the design of monitoring strategies, enhancing the predictive accuracy of models, and effectively managing geomechanical risks, thus ensuring the success of GCS initiatives.

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This study investigates the integration of machine learning (ML) and data assimilation (DA) techniques, focusing on implementing surrogate models for Geological Carbon Storage (GCS) projects while maintaining the high fidelity physical results in posterior states. Initially, we evaluate the surrogate modeling capability of two distinct machine learning models, Fourier Neural Operators (FNOs) and Transformer UNet (T-UNet), in the context of CO₂ injection simulations within channelized reservoirs. We introduce the Surrogate-based hybrid ESMDA (SH-ESMDA), an adaptation of the traditional Ensemble Smoother with Multiple Data Assimilation (ESMDA). This method uses FNOs and T-UNet as surrogate models and has the potential to make the standard ESMDA process at least 50% faster or more, depending on the number of assimilation steps. Additionally, we introduce Surrogate-based Hybrid RML (SH-RML), a variational data assimilation approach that relies on the randomized maximum likelihood (RML) where both the FNO and the T-UNet enable the computation of gradients for the optimization of the objective function, and a high-fidelity model is employed for the computation of the posterior states. Our comparative analyses show that SH-RML offers a better uncertainty quantification when compared to the conventional ESMDA for the case study.

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This study presents a method to address the significant uncertainties in subsurface modeling that impact the efficiency of energy transition applications such as geothermal energy extraction and CO₂ geological sequetsration. The approach combines a physics-based geomechanical proxy model with an ensemble smoother with multiple data assimilation (ES-MDA), aimed at enhancing uncertainty quantification through the integration of vertical displacement measurements from fluid production and injection. The data from wells is limited in spatial coverage, while these measurements offer extensive spatial information, improving the understanding of subsurface behavior by reflecting changes in reservoir pressure and temperature. The open-DARTS simulator for fluid flow and a geomechanical proxy are used to perform data assimilation with ES-MDA. By generating an ensemble of model realizations with varied permeability, calculating vertical displacements at the surface, and applying ES-MDA, we effectively identify the probability distribution of the vertical displacement of the model conditioned to observed subsidence data. Entropy is used as a statistical measure to quantify the reduction of uncertainty of subsurface models based on observations. Our approach was tested on a 2D conceptual and 3D realistic datasets, demonstrating its capability to provide data assimilation. This workflow represents an advancement in subsurface modeling, supporting informed decision-making in geothermal energy production and CO₂ sequestration by offering an improved alternative for data assimilation and enhancing tools for uncertainty quantification.

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Naturally fractured reservoirs can pose challenges for energy operations such as hydrocarbon production, CO2 storage, and geothermal energy production. Fluid flow in these reservoirs is greatly affected by fracture properties such as orientation and aperture, whose magnitude is mainly influenced by the stresses on the reservoir rocks. Simulating fractures and their behavior tends to be computationally intensive, but recent advances in Discrete Fracture Models (DFM) have successfully overcome computational complexity and allow for the explicit inclusion of discrete fractures in reservoir simulations. However, there are still challenges in dealing with uncertainties, including fracture aperture and the effect of in-situ stresses on the fracture surface and their effect on the fluid behavior. This study explores the use of data-assimilation techniques to help quantify these uncertainties. We combine a recent implementation of DFM on the Delft Advanced Research Terra Simulator (DARTS) with both ensemble and gradientbased data-assimilation methods. Our results show that data assimilation can help to understand the dynamic behavior of fluids in fractured reservoirs. Using this technique, we obtain a more accurate representation of the stresses acting on the reservoir and how they affect the fracture aperture. This information is essential for more efficient reservoir management.@en