Carbon Capture Storage (CCS) and Carbon Capture Utilization and Storage (CCUS) have recently gained global attention as promising techniques to mitigate net CO2 emissions. Within this framework, the Saudi Arabian 2030 vision targets the large-scale deployment of CCS and CCUS projects to promote its circular carbon economy. This study evaluates the potential for underground sequestration of CO2 emitted from industrial sources near Riyadh, Saudi Arabia, which emit 46 Mton/year. A deterministic geologic model corresponding to the Unayzah Formation was constructed using published data incorporating sedimentary facies distribution, porosity, permeability, and connectivity. Compositional simulations were performed to assess the CO2 plume flow in the presence of conduits, barriers, and baffles. Similarly, injectivity and injection rate effects on solubility and residual trapping were evaluated. A sensitivity analysis and an uncertainty quantification study were carried out to obtain a probabilistic assessment of the total storage capacity and trapping contributions. The geological evaluation indicates that the area under Riyadh is unsuitable because the Triassic sandstones are too shallow, and the Paleozoic section was entirely removed by erosion during the Carboniferous. Alternatively, the Hawtah area, at 150 km south of Riyadh, is deemed suitable for CO2 sequestration. These sandstones are porous, permeable, tightly sealed, and correspond to hydrocarbon reservoirs in anticlinal structures along the Hawtah, Nuayyim, and Dilam trends. They are favorable for CO2 disposal outside oil and gas fields due to lateral and vertical permeability barriers and up-dip pinch-out against the Batin arch. Simulation results, fifty years after CO2 injection and two hundred fifty years of monitoring, show that the Unayzah Formation satisfies the conditions of capacity, injectivity, and seal efficiency required for technical feasibility. Furthermore, lower injection rates promote higher solubility and residual trapping due to gravity-controlled flow exceeding viscous and capillary forces. Residual trapping contributes ~ 50% to the storage, while solubility adds 10%. The variables that have a higher impact on secure trapping are residual gas saturation, water salinity, and permeability. The current CO2 storage capacity in the area evaluated exceeds 300 Megatons (Mt), and the assessment is still ongoing, with no vertical leakage through the caprocks of the Khuff and Sudair Formations. Overall, the novelty in this research focuses on the unprecedented use of public domain data to construct a detailed geological model of the Unayzah Formation in the Hawtah and Nuayyim area that allowed a better understanding of CO2 flow mechanisms in the reservoir and its capacity to store CO2. This study concludes that the Unayzah reservoir in the Kharj-Hawtah area is a viable candidate for secure CO2 disposal from industrial sources in Riyadh.

Seismic inversion is the prime method to estimate subsurface properties from seismic data. However, such inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of the data. Consequently, the data misfit term must be augmented with appropriate regularization that incorporates prior information about the sought-after solution. Conventionally, model-based regularization terms are problem-dependent and hand-crafted; this can limit the modeling capability of the inverse problem. Recently, a new framework has emerged under the name of Plug-and-Play (PnP) regularization, which suggests reinterpreting the effect of the regularizer as a denoising problem. Convolutional neural networks-based denoisers are state-of-the-art methods for image denoising: their adoption in the PnP framework has led to algorithms with improved capabilities over classical regularization in computer vision and medical imaging applications. In this work, we present a comparison between standard model-based and data-driven regularization techniques in post-stack seismic inversion and give some insights into the optimization and denoiser-related parameters tuning. The results on synthetic seismic data indicate that PnP regularization using a bias-free CNN-based denoiser with an additional noise map as input can outperform standard model-based methods.

Reservoir characterization is a critical component in any oil and gas, geothermal, and CO 2 sequestration project. A fundamental step in the process of characterizing the subsurface is represented by the inversion of petrophysical parameters from seismic data. However, this problem suffers from various uncertainty sources originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model and Zoeppritz equation that constitute the modelling operator, further complicates the inversion process. In this work, we propose a novel data-driven approach where well-log information is used to obtain optimal basis functions that link band-limited petrophysical reflectivities to pre-stack seismic data. Subsequently, the inversion of such band-limited reflectivities for petrophysical parameters is framed in a Bayesian framework where a generative adversarial network is used to produce a geologically realistic prior distribution. The trained prior distribution is updated using the Stein Variational Gradient Descent and a set of representative solutions is produced that is consistent with the uncertainties in the data and the nonlinear operators.

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