Large-scale CO2 Research Articles

Preparation activated tailings by pH swing process: Towards yielding cemented tailings backfill and in-situ CO2 mineralization

In-situ CO2 mineralization of tailings has shown great potential for large-scale CO2 sequestration, but its development is seriously limited by the low CO2 conversion rate. This study proposed an innovative pH swing process to produce activated tailings through magnesium extraction from raw tailings and subsequent leachate precipitation. By the combination of activated tailings and high-belite calcium sulfoaluminate cement, a new type of cemented activated tailings backfill (CATB) was developed. The results demonstrate that 82.33% of magnesium is extracted from raw tailings and precipitates in the form of Mg(OH)2 serving as the dominating carbonation active phase during the pH swing process. Besides the aragonite forms in the carbonated cemented raw tailings backfill (CRTB), carbonated CATB also contains calcite, nesquehonite, and hydromagnesite. Substituting activated tailings for raw tailings led to the higher CO2 absorption capability (ranging from 8.88% to 14.17% with various binder-to-tailings ratios). These indicate that the activated tailings have a promising application scenario in large scale in-situ CO2 mineralization.

Risk assessment and management strategy of geologic carbon storage in multi-well site

Many oil and gas production areas may serve as potential geologic carbon storage(GCS) sites, but large-scale CO2 injection in these sites with multiple legacy wells poses a leakage risk. Therefore, it is crucial to analyze the well leakage risk of these GCS sites and develop effective risk management strategies to minimize CO2 and brine leakage. In this study, we hypothesized a GCS site based on the Jingbian area in the Ordos Basin, which has 177 legacy wells. We conducted a three-dimensional geological modeling of the site and simulated the injection and migration of CO2. Using the National Risk Assessment Partnership’s open-source Integrated Assessment toolkit, we outlined a workflow for assessing the risk of CO2 and brine leakage in multi-well site. Simulations of CO2 and brine leakage through 177 existing wells into the USDW or the atmosphere are used as an indicator of potential risk. Finally, we proposed risk management strategies based on well integrity, injection rate, and pollution range, verifying their effectiveness. The results of the 95th percentile (P95) model implementation showed that the leakage risk of GCS sites was low over 150 years, with a CO2 leakage of 15,996.4 tonnes(0.16 % of the 107 t injected). The risk-based strategy outperformed both distance-based strategy and hybrid strategy overall. Reducing injection rates alone proved ineffective as a risk management strategy. Only high-risk wells near the injection zone may cause pollution to USDW. Meanwhile, legacy wells have negligible impact on atmospheric pollution. The management strategy based on pollution range can be used as a supplement to the risk-based strategy in practical engineering.

Optimized demand-based charging networks for long-haul trucking in Europe

Abstract Battery electric trucks (BETs) are the most promising option for fast and large-scale CO2 emission reduction in road freight transport. Yet, the limited range and longer charging times compared to diesel trucks make long-haul BET applications challenging, so a comprehensive fast charging network for BETs is required. However, little is known about optimal truck charging locations for long-haul trucking in Europe. Here we derive optimized truck charging networks consisting of publicly accessible locations across the continent. Based on European truck traffic flow estimates for 2030 and actual truck stop locations we construct a long-term charging network that minimizes the total number of required locations. Our approach introduces an origin-destination pair sampling method and includes local capacity constraints to compute an optimized stepwise network expansion along the highest demand routes in Europe. For an electrification target of 15% BET share in long-haul and without depot charging, our results suggest that about 91% of electric long-haul truck traffic across Europe can be enabled already with a network of 1,000 locations, while 500 locations would suffice for about 50%. We furthermore show how the coverage of origin-destination flows scales with the number of locations and the size of the stations. Ideal locations to cover many truck trips are at highway intersections and along major European road freight corridors (TEN-T core network).

Fabrication of yeast engineered porous 13X adsorbent layers for CO2 capture

This study investigates a novel environmentally friendly coating technique using yeast fermentation to obtain super porous Zeolite 13X adsorbent layers through freeze-drying technique for CO2 capture applications. Several mass ratios of sugar to yeast were used to control the porosity and pore sizes in the adsorbent layer. A unique peeling process was integrated to obtain foamy, hollow structures to have deeper layer accessibility of adsorbent for adsorption applications. These coated adsorbent layers were experimentally characterized as a function of the mass ratio of sugar to yeast and adsorbent solid fraction. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FT-IR) are used to analyze the coated layers. The void fraction for coated samples is found to vary between 0.48 and 0.56, making the samples applicable for adsorption applications. Adsorption performance experiments are performed to test the coated samples' performance compared to 13X powder. Due to their simplistic fabrication, cost-effectiveness, rapid pore formation, and high adsorption capacity, these porous 13X layers hold potential for large-scale fabrication and CO2 capture applications.

A parallel compositional reservoir simulator for large-scale CO2 geological storage modeling and assessment

Storing CO2 in deep aquifers and depleted gas reservoirs is an effective way to achieve carbon neutrality. However, the numerical simulation of CO2 storage in these formations is challenging due to the complexity of gases-brine systems. The number of gas species included in the gases-brine fluid models of existing simulators cannot meet the rapidly evolving CO2 sequestration scenarios. To address this intricate issue, we developed a three-dimensional fully implicit parallel CO2 geological storage simulator (PRSI-CGCS) on distributed-memory computers based on our in-house parallel platform. This simulator uses a compositional fluid model with a diverse range of gas species, including CO2, C1 ~ C3, N2, H2S, as well as newly added gases H2 and O2, which may be encountered in geological CO2 storages. Besides, we provide more suitable scaling factors for different gases in the stability analysis bypassing (SAB) method to accelerate the gases-brine phase equilibrium calculations. PRSI-CGCS does not incorporate energy conservation equations, and salt precipitation or dissolution is also not considered. Numerical experiments show that our simulator is scalable, robust and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster. Besides, after our modification on scaling factors, the SAB method can reduce the number of stability analyses by 61.39 % to 88.71 %, thereby reducing simulation time. Furthermore, case studies indicate that injecting O2 and H2 along with CO2 reduces the stability or capacity of CO2 storage and increases the pressure required for injection. However, this impact is not significant when the impurity content is less than 10 %.

Optimizing large-scale CO2 pipeline networks using a geospatial splitting approach

CO2 transport infrastructure is the backbone of carbon capture and storage (CCS) technology for the mitigation of carbon emissions and project deployment viability. In conventional large-scale CO2 pipeline network designs, the storage sites are generally assumed as the centroids of the major geologic basins, however, this approach might provide suboptimal solutions since the large extension of some storage formations significantly increases the length of the CO2 transportation networks. To address this situation and obtain optimal pipeline routes, we present a novel geospatial splitting framework that partitions large basins into multiple sub-sinks. In our approach, we used a large number of reservoir models varying petrophysical properties and CO2 injection rates to compute pressure plumes through numerical simulations, leading to the calculation of the number of subregions for each basin as a function of the extension of pressure interference areas and boundaries. Finally, we applied K-means clustering and Voronoi polygon algorithms to partition large basins into subregions and obtain their sink coordinates. To demonstrate the capability of the developed workflow, we investigated two CO2 pipeline network modeling case studies using our splitting approach: one regional case study focusing on the Intermountain West (I-West) region and one nationwide case study covering the lower 48 states in the U.S. In both case studies, we compared the optimal pipeline routes using the original and new storage locations and examined the major differences. The use of the developed geospatial approach resulted in both cases in a shortening of the total pipeline network length by 13% and 10%, compared to the pipeline modeling with the original basins, leading to cost reductions of 25% and 17%, respectively, demonstrating that the location of point sinks has a critical impact on the length and expenses of pipelines to efficiently transport CO2 to distant storage sites. Therefore, the workflow presented here contributes to the proper and realistic modeling of case studies that support decision-making in CCS deployment.

Energy mechanism of gas channeling channels in complex fracture networks for CO2 injection in ultra-low permeability reservoirs

The injection of CO2 into ultra-low permeability can enhance production as well as accomplish storage of carbon. However, the gas channeling phenomenon is serious and there is a lack of explanation of the energy mechanism. Based on the current state of CO2 gas channeling in the Y29-105 well group located in the Huang 3 block of Jiyuan Oilfield, this study utilises the ABAQUS finite element cohesion model to explore the mechanical evolution of the CO2 channel expansion process. And according to the size of the cross-flow channel, intra-fracture pressure, damage displacement and expansion pressure, it is divided into the unconnected stage, the inter-well connected stage and the well group connected stage. Furthermore, we also established energy judgment criteria for the expansion behaviour of gas channeling channels, including stopping at natural fractures, extending along natural fractures, and directly crossing natural fractures. Upon comparison with theoretical and experimental results, it was determined that the energy discrimination criterion is appropriate for expansion along natural fractures and cross natural fractures. It can be seen that our research results can offer energy mechanism support for preventing, controlling, and predicting large-scale CO2 injection in ultra-low permeability reservoirs.

Stabilized Cuδ+-OH species on in situ reconstructed Cu nanoparticles for CO2-to-C2H4 conversion in neutral media

Achieving large-scale electrochemical CO2 reduction to multicarbon products with high selectivity using membrane electrode assembly (MEA) electrolyzers in neutral electrolyte is promising for carbon neutrality. However, the unsatisfactory multicarbon products selectivity and unclear reaction mechanisms in an MEA have hindered its further development. Here, we report a strategy that manipulates the interfacial microenvironment of Cu nanoparticles in an MEA to suppress hydrogen evolution reaction and enhance C2H4 conversion. In situ multimodal characterizations consistently reveal well-stabilized Cuδ+-OH species as active sites during MEA testing. The OH radicals generated in situ from water create a locally oxidative microenvironment on the copper surface, stabilizing the Cuδ+ species and leading to an irreversible and asynchronous change in morphology and valence, yielding high-curvature nanowhiskers. Consequently, we deliver a selective C2H4 production with a Faradaic efficiency of 55.6% ± 2.8 at 316 mA cm−2 in neutral media.

Magnetically Enhanced Electroconversion of CO2 to Valuable Products at High Current Densities

Carbon dioxide reduction has emerged as a highly promising area of focus, particularly considering the perpetual CO2 emissions from the industrial sector. The electrochemical reduction pathway stands out as one of the most auspicious methodologies for CO2 utilization, owing to its compelling attributes. These include, but are not limited to, operation under mild conditions, the ability to tailor CO2 conversion towards desired products, and the potential for seamless integration with renewable electricity sources (e.g., derived from solar and wind), thus offsetting the associated carbon footprint. Moreover, the application of magnetic fields has demonstrated effectiveness in numerous electrocatalytic reduction processes. Presently, there is a paucity of literature addressing the utilization of magnetic fields for CO2 reduction, as it represents a recently introduced concept. This presentation aims to contribute to the advancement of this novel idea, paving the way for its scalability in the near future. Our focus is on addressing mass transfer limitations and optimizing the process to handle high current density, thereby rendering it viable for large-scale CO2 reduction.

Experimental study on the ash deposition and NO emission of high-alkali coal under the staged O2/CO2 and O2/RFG conditions

The oxy-fuel combustion contributes to carbon capture, while the recirculation of flue gas brings about high concentrations of SO2 and H2O, which can affect the transformation of minerals in high-alkali coal. The staged oxy-fuel combustion, as an effective method for NOx reduction, can also change the ash deposition behavior of high-alkali coal. Two kinds of diluting agents, including pure CO2 for O2/CO2 combustion and simulated “recycled flue gas” (CO2, SO2, and H2O) for O2/RFG combustion, were employed in the present work. The ash deposition and NO emission of high-alkali coal during the staged oxy-fuel combustion were simultaneously studied under O2/CO2 and O2/RFG conditions. The conversion ratios of fuel-nitrogen to NO (CNO) and ash deposition efficiencies (Ed) at different stoichiometric ratios in primary combustion zone (SR1) and different oxygen concentrations were obtained. Afterwards, a series of tests were performed to further analyze the ash deposits. The experimental results show that as SR1 increases from 0.6 to 1.2, CNO jumps from 2.0 % to 23.5 % (O2/CO2 combustion) and from 1.9 % to 19.9 % (O2/RFG combustion). The additions of SO2 and H2O can reduce the NO emission. With the rising SR1, Ed under the O2/CO2 and O2/RFG conditions decreases from 4.0 % to 2.6 % and from 4.8 % to 2.1 %, respectively. At high SR1, the CaSO4 amount declines and the iron contributes less to the ash deposition. In O2/RFG combustion, the small sticky particles of sodium aluminosilicates on large particle surfaces reduce, and the large particles of calcium aluminosilicates shrink because some calcium produces CaSO4. Moreover, the exposure of ferrous iron to H2O helps its oxidization so iron is harder to cause severe adhesion. As O2 concentration rises from 21 % to 40 %, CNO shows an upward trend. Meanwhile, Ed under the O2/CO2 and O2/RFG conditions increases from 2.6 % to 3.7 % and from 2.3 % to 2.7 %, respectively. The present work is expected to provide some conducive information for the clean utilization of high-alkali coal and secure operation of boiler, as well as large-scale CO2 capture.

All-solid-waste-derived CaO-based sorbents for simultaneously enhanced calcium looping CO2 capture and thermochemical energy storage

Calcium looping (CaL) process relying on CaO as high-temperature CO2 sorbents is a prospective alternative technology for simultaneously cyclic CO2 capture and thermochemical energy storage. However, the cyclic stability and the energy storage density of the CaO-based sorbents decay drastically over repeated carbonation-calcination cycles. Herein, we yielded CaO-based sorbents derived from all industrial solid wastes with carbide slag (CS) as precursor and coal fly ash (CFA) as stabilizer featuring superior carbonation characteristics, stable CO2 cyclic uptake and high thermochemical energy storage density over multiple operation cycles. The optimal sorbent (CCS–H-G-CFA-D) exhibited a high initial CO2 uptake of 0.38 gCO2/gsorbent, superior cyclic stability with an average decay rate of 1.05% per cycle and remarkably stable energy storage density of 1375 kJ/kg, retaining 89.5% after 10 repeated cycles. Thereby, the simultaneously enhanced cyclic CO2 capture and thermochemical energy storage are attributed to adding CFA stabilizers in CS-derived CaO-based sorbents, forming stable skeleton to alleviate sintering, improving the uniform mixing degree between sorbent particles and promoting the carbonation reaction via synergistic effect. This simple and eco-friendly approach of cost-effective sorbents totally derived from industrial solid wastes provides a new avenue for large-scale practical CO2 capture and energy storage processes.

Capillary sealing capability alteration of shale caprock induced by CO2-brine-rock interaction: Implication for CO2 geological storage

The capillary sealing mechanism plays a pivotal role in the sealing capability of caprock in CO2 geological storage. The interactions between CO2, brine, and rock induced by CO2 injection may potentially impact the capillary sealing capability of the caprock. To characterize the capillary sealing capability of Yanchang shale, breakthrough pressure (Pb) tests were conducted before and after CO2-brine-rock interaction while analyzing the contributions of pore structure and wettability to Pb alteration. The results revealed that Pb of Yanchang shale samples decreased by 7.14%–22.22% after CO2-brine-rock interaction, indicating a deterioration in their capillary sealing capability. This degradation can be attributed to changes in pore structure and wettability, as they collectively influence the capillary sealing capability of shale caprock. The increase of small-pore (<200 nm) porosity and contact angle promoted the reduction of Pb. The alteration of shale caprock's capillary sealing capability due to CO2-brine-rock interaction significantly impacts CO2 storage potential and long-term safety, which should be considered prior to large-scale CO2 injection.

Atacama Large Aperture Submillimeter Telescope (AtLAST) science: Gas and dust in nearby galaxies

Understanding the physical processes that regulate star formation and galaxy evolution are major areas of activity in modern astrophysics. Nearby galaxies offer unique opportunities to inspect interstellar medium (ISM), star formation (SF), radiative, dynamic and magnetic ( B → ) physics in great detail from sub-galactic (kpc) scales to sub-cloud (sub-pc) scales, from quiescent galaxies to starbursts, and from field galaxies to overdensities. In this case study, we discuss the major breakthroughs in this area of research that will be enabled by the Atacama Large Aperture Submillimeter Telescope (AtLAST), a proposed 50-m single-dish submillimeter telescope. The new discovery space of AtLAST comes from its exceptional sensitivity, in particular to extended low surface brightness emission, a very large 2° field of view, and correspondingly high mapping efficiency. This paper focuses on four themes which will particularly benefit from AtLAST: 1) the LMC and SMC, 2) extragalactic magnetic fields, 3) the physics and chemistry of the interstellar medium, and 4) star formation and galaxy evolution. With ~ 1000 − 2000 hour surveys each, AtLAST could deliver deep dust continuum maps of the entire LMC and SMC fields at parsec-scale resolution, high-resolution maps of the magnetic field structure, gas density, temperature and composition of the dense and diffuse ISM in ~ 100 nearby galaxies, as well as the first large-scale blind CO survey in the nearby Universe, delivering molecular gas masses for up to 106 galaxies (3 orders of magnitude more than current samples). Through such observing campaigns, AtLAST will have a profound impact on our understanding of the baryon cycle and star formation across a wide range of environments.

Without mandated demand for greenhouse gas removal – High integrity GtCO2-scale global deployment will be jeopardized: Insight from US economic policy 2020–23

Greenhouse gas removals (GGRs) will be essential at GtCO2-scale to offset residual emissions and achieve global net zero by 2050. Engineered GGRs have the greatest long-term, large-scale CO2 storage potential and, therefore, offer opportunities to realize high-integrity removals. However, the most prominent engineered solutions are capital-intensive, generate expensive offset credits, and have achieved minimal market penetration. The United States has established policies subsidizing GGR deployment value chains which generate high-integrity removals to bring down engineered GGR costs. The US regime has the potential to result in substantial GGR cost reductions which will likely have implications on the policy frameworks for global GGR technology deployment. To date, research addressing the impact of US policies on the commercial imperatives and investment uncertainties for the global GGR sector is limited. Using a mixed methods approach, this contribution unpacked the motivations of early private sector actors. It did this by identifying characteristics of the most investable GGR business models and policy implementation requirements to establish the nascent sector, which will be critical to the GGR sector becoming a global multi-trillion-dollar, self-sustaining market. The analysis reveals that cost reduction alone will be insufficient to establish a high-integrity global GtCO2-scale sector. Systemic market risks, especially certainty of offtake through demand-side incentives, need to be `addressed. Therefore, the development of a stable and sustainable GGR sector, both in the US and globally, will remain problematic due to the inability to attract large-scale private capital investment.

Improving physical properties, microstructure and actual carbon sequestration of steel slag based autoclaved aerated concrete by accelerated carbonation

This study presents an autoclaved aerated concrete (AAC) with steel slag (SS) as carbonate-able binder. The traditional autoclaving process was augmented with a carbonation stage to improve the physical properties, microstructure and actual carbon sequestration of steel slag based autoclaved aerated concrete (SS-AAC). The macroscopic and microscopic test results showed that the carbonation process stimulated the reactivity of SS to generate gel products, allowing the additional formation of tobermorite during the autoclaving process. Under the synergistic positive effect of the densification-induced micro-pore refinement caused by calcium carbonate precipitation and the excellent gas permeability of AAC, the intervention of carbonation process resulted in SS-AAC with higher compressive strength, more homogeneous carbonation, and more outstanding actual CO2 absorption efficiency. The research provides a potential method for preparing high-volume solid waste AAC with large-scale CO2 utilization to encourage the concrete sector to adopt a sustainable stance.

A combined experimental and simulations assessment of CO2 capture and CO2/H2 separation performance of aminosilane-grafted MCM-41 and pore-expanded MCM-41

Amine-functionalized mesoporous silica materials have attracted considerable attention as robust adsorbents for large-scale CO2 capture and direct air capture (DAC). Amine grafting increases selective affinity at low pressures, yet also often reduces the adsorbent's pore volume and surface area, affecting the kinetics and capacity upon CO2 adsorption. MCM-41 and pore-expanded MCM-41 (PE-MCM-41) were developed here, functionalized using 3-aminopropyl triethoxy silane (APTES), and comparably evaluated for their carbon capture performance at different conditions by a combinational experimental, equilibrium and kinetic modelling, and molecular simulations approach. In specific, CO2 adsorption capacity, CO2/N2 selectivity, CO2 adsorption at humid conditions, heat of adsorption, and regeneration/cyclability were assessed, coupled to molecular simulation insights on the physisorption and chemisorption contribution to the overall adsorption uptake. Indicatively, a capacity value of 3.07 and 2.44 mmol/g was obtained for APTES-PE-MCM-41, and 2.27 and 1.89 mmol/g for APTES-MCM-41, under wet and dry conditions, respectively. The CO2 selective adsorption from H2 rich gas streams was also examined over APTES-MCM-41 in order to assess the potential application in H2 stream purification process. The latter is of high significance in order to use hydrogen as fuel e.g. in anion exchange membrane fuel cells for power production since the presence of CO2 in the feed composition is considered a major drawback in their commercialization.

An improved neural operator framework for large-scale CO2 storage operations

Geologic carbon storage (GCS) is a practical solution to mitigate the impact of climate change and achieve net-zero carbon emissions. However, due to complex subsurface characteristics and operational constraints, GCS can potentially induce seismic events and leakage to groundwater resources. Moreover, predicting subsurface response in pressure and saturation changes during GCS requires high-fidelity models with high computational times. Machine learning (ML) offers a promising solution to alleviate these challenges. Yet, ML-driven approaches in subsurface physics face significant obstacles, such as the substantial computational resources required for training large-scale models and the accuracy requirement as a reliable surrogate model. To tackle these issues, we propose an improved neural operator (INO) that has improved a DeepONet architecture to handle a realistic reservoir model efficiently, with computational efficiency and high accuracy.Our proposed INO framework has three key benefits: precision, adaptability, and cost-efficiency. For precision, it achieves an average root mean square error (RMSE) of 0.05% (1.5 psi) compared to the average reservoir pressure (3,150 psi) and a RMSE value of 0.016 in void fraction for CO2 saturation among testing cases. Although it is sensitive to high-error areas with steep state variable gradients, proper domain decomposition, ensemble methods, and/or integrating domain-specific knowledge could improve this limitation. For adaptability, the INO framework can be trained with a subset of the computational domain during each backpropagation to achieve better training adaptability, even for problems with high-dimensional inputs (i.e., heterogeneous fields). For example, it can be trained with random subsampling of only 0.2% of the full domain but can predict pressure and saturation in any space and time within the entire domain. For cost efficiency, training procedures with a subset of the whole computational domain significantly improve computational time. For example, for 90 training cases with 1.73 million cells and 50-time steps, the overall training time was about 2.5 h using a single GPU. A trained INO model takes 1 s per evaluation over 1.73 million and 50 time stamps, much faster than a high-fidelity simulator. Overall, the proposed INO approach with these improved benefits will enable us to perform large-scale GCS applications, enhancing the potential of ML models in the subsurface and other multiphysics problems.

Sustainable energy solutions: Well retrofit analysis and emission reduction for a net-zero future in the Intermountain West, United States of America

To achieve net-zero emissions by 2050, we need economic means of sequestering carbon dioxide (CO2) and reducing greenhouse gas emissions (GHG). We analyze the sequestration potential of the Intermountain West (I-West) region, US, as a primary energy transition hub through analysis of wellbore retrofit potential and emission reduction in both fugitive gas abatement and flare gas. We selected the I-West region due to its abundant energy sources and oil and gas production legacy. Preliminary analysis hints that well retrofits can breathe new life into a well at a fraction of the cost of a new drill. With millions of potential candidates in the US, even a modest fraction (1% or less) suitable for retrofit could accelerate the shift to large-scale CO2 sequestration. Fugitive gas, the unintentional release of wellbore gases such as methane, is a significant emissions source. Through conservative analysis, it is estimated that wellhead leakage alone may account for 5 million tonnes of carbon dioxide equivalent (CO2e) emissions. We conclude by assessing the CO2 emissions from flaring, which is the burning of associated gas during well operations, conservative analysis indicates flaring contributes another 2 million tonnes of CO2 emissions to the region. We find that with targeted retrofit and better controls on emissions sources, the I-West region can make a significant impact in the nation's push to become net-zero. This study outlines economic feasibility and actionable items to achieve the critical reductions in emissions and increases in sequestration necessary to attain net zero.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

The role of cryogenic carbon capture in future carbon-neutral societies

Utilizing CO2 capture technologies is an essential part of achieving a future carbon-neutral Society. So far, amine-based technologies, which are the most mature post-combustion CO2 capture technologies, have been predominantly applied in large-scale CO2 capture applications. However, the cryogenic process has also been proven to be a potential CO2 capture technology suitable for large-scale applications. Cryogenic carbon capture offers two potential advantages over amine-based technology. First, the efficiency is higher and thus the energy penalty is lower. Next, the flexibility of system integration is also higher, and thus the technology carries the potential of better balancing variable renewable electricity productions. By using the software tool EnergyPLAN and dedicated scenarios of achieving a carbon-neutral Denmark, this paper quantitatively estimates these benefits. It is observed that, from a system perspective, utilizing cryogenic technologies to capture 90 % of CO2 emissions in 2045 can reduce the demand for wind power by approximately 47 %, leading to a decrease in annual system costs by nearly 45 %.