Carbon capture, utilization, and storage (CCUS) refers to technologies that capture carbon dioxide (CO₂) emissions from sources such as power plants, industrial facilities, and transportation, and either store it underground or use it for beneficial purposes. CCUS can play a role in reducing greenhouse gas emissions and mitigating climate change, as CO₂ is a major contributor to global warming. In the Baltic Sea region countries (BSR), patent searches from 2000 to 2020 reveal that CCUS technologies are focused on CO₂ storage, monitoring, utilization, and transport. However, the adoption and deployment of these technologies has been slow due to a variety of factors, including a lack of government action on climate change, public skepticism, increasing costs, and advances in other options such as renewables and shale gas. Overall, CCUS has the potential to significantly reduce CO₂ emissions and contribute to climate change mitigation efforts, but more work is needed to overcome the barriers to its widespread adoption in the BSR and elsewhere. This could include policy measures to incentivize the use of CCUS technologies, public education and outreach efforts to increase understanding and support for CCUS, and research and development to improve the efficiency and cost-effectiveness of these technologies.

In recent years machine learning algorithms have been gaining momentum in resolving subsurface flow issues related to hydrocarbon flows, Carbon capture utilization and storage, hydrogen storage, geothermal flows, and enhanced oil recovery. This paper presents and attempts to solve subsurface flow problem using neural upscaling method. The neural upscaling method, described in the present work, is a machine learning approach to calculate effective properties in each grid block for subsurface flow modeling. This method is intended to be more accurate than traditional analytical upscaling methods (which are only accurate for layered or homogeneous media) and numerical upscaling methods (which are more accurate for heterogeneous media but involve higher computational cost and are dependent on boundary conditions). The neural upscaling method is based on learning from a large number of geological realizations, which allows it to account for uncertainty in geology. It is also computationally fast and accurate. The method is demonstrated through a series of 2D test cases, and its accuracy is compared to that of analytical and numerical upscaling methods.

The aim of this study is to review and identify H₂ storage suitability in geological reservoirs of the Republic of Lithuania. Notably, Lithuania can store clean H₂ effectively and competitively because of its wealth of resources and well-established infrastructure. The storage viability in Lithuanian geological contexts is highlighted in this study. In addition, when it comes to injectivity and storage capacity, salt caverns and saline aquifers present less of a challenge than other kinds of storage medium. Lithuania possesses sizable subterranean reservoirs (Cambrian rocks) that can be utilized to store H₂. For preliminary assessment, the cyclic H₂ injection, and production simulation is performed. A 10-year simulation of hydrogen injection and recovery in the Syderiai saline aquifer demonstrated the feasibility of UHS, though efficiency was reduced by nearly 50% when using a single well for both injection and production. The study suggests using separate wells to improve efficiency. However, to guarantee economic injectivity and containment security, a detailed assessment of the geological structures is required specifically at the pore scale level. The volumetric approach estimated a combined storage capacity of approximately 898.5 Gg H₂ (~11 TWh) for the Syderiai and Vaskai saline aquifers, significantly exceeding previous estimates. The findings underscore the importance of detailed geological data and further research on hydrogen-specific factors to optimize UHS in Lithuania. Addressing technical, geological, and environmental challenges through multidisciplinary research is essential for advancing UHS implementation and supporting Lithuania’s transition to a sustainable energy system. UHS makes it possible to maximize the use of clean energy, reduce greenhouse gas emissions, and build a more sustainable and resilient energy system. Hence, intensive research and advancements are needed to optimize H₂ energy for broader applications in Lithuania.

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO ₂ sequestration [1]. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations [2]. This study presents a comprehensive methodology for assessing CO ₂ storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes [3, 4]. These petrophysical properties were derived from core samples collected from these formations. Utilizing machine learning algorithms, porosity was estimated while the Lattice Boltzmann method (LBM) was applied to determine permeability [5]. The methodology employed for estimating these petrophysical parameters was initially validated using samples from formations analogous to Lithuanian formations. Subsequently, it was applied to rock samples specifically obtained from Lithuanian formations. The estimated petrophysical properties were compared with peer-reviewed data from published literature. When fluids such as CO ₂ or H ₂ are injected into sub-surface reservoirs, they can alter pore and grain characteristics. Therefore, it is crucial to extract representative element volumes (REVs) from segmented volumes to study the impact of fluids on porosity and their distribution [6]. These mini models, representing small portions of the larger formation, assist in predicting fluid flow within the formation, which is vital for assessing the efficiency and safety of carbon capture and storage (CCS) operations. Subsequently, numerical modelling was conducted using the petrophysical parameters as inputs to assess the storage capacity of the Lithuanian formations using tNavigator software [7]. This research contributes to an enhanced understanding of pore space distribution and its role in various aspects of long-term CO ₂ storage. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO ₂ storage. This study shall aid in enhanced understanding of pore space distribution and their contribution towards various aspects of long-term storage. The results can be extended to study the geochemical reactions and geo-mechanical behaviour of the rocks. Such studies shall further facilitate identification of reservoir(s) wherein sequestration potential can be reliably explored.

As the global concern over greenhouse gas emissions grows, CO₂ storage in deep saline aquifers and depleted reservoirs has become crucial for climate change mitigation. This study evaluates the feasibility of Lithuanian deep saline aquifers, specifically, Syderiai and Vaskai, for effective CO₂ storage. Unlike previous theoretical analyses, it provides experimental data on static and dynamic reservoir parameters that impact CO₂ injection and retention. Using micro X-ray computed tomography (MXCT) and multi-resolution scanning at 8 µm and 22 µm, digital rock volumes (DRVs) from core samples were created to determine porosity and permeability. The method, validated against analogous samples, identified a representative element volume (REV) within sub-volumes, showing a homogeneous distribution of petrophysical properties in the Lithuanian samples. The results show that DRVs can accurately reflect pore-scale properties, achieving 90–95% agreement with lab measurements, and offer a rapid, efficient means for analyzing storage potentials. These insights confirm that Lithuanian aquifers are promising for CO₂ sequestration, with recommendations for further long-term monitoring and applications of this technique across the region.

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO ₂ sequestration. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations. This study presents a comprehensive methodology for assessing CO ₂ storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes of samples from analogous formations. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO ₂ storage.