Alkaline materials for coastal ocean alkalinity enhancement: A comparative study of natural silicates and industrial byproducts.

Enhanced the dissolution of fluorogypsum for the co-production of polymorphism CaCO3 through CO2 sequestration

Study on the effect of coexisting SO2 and NO2 on the electrochemical corrosion behaviour of P110 tubing material for CO2 sequestration

Multi-stage evolution of sandstone properties during CO2 sequestration: Mechanisms and implications

• Multi-resolution deep-learning model allows training using upscaled reservoir model. • 90 % reduction in training data generation cost using upscaled reservoir model. • Proposed workflow accelerates optimization by orders of magnitude. • CCS well control at Illinois Basin Decatur Project has been optimized. • Both surface injection and zonal rate allocation optimization are conducted. The injection of CO₂ into subsurface formations entails various risks, necessitating a comprehensive evaluation of several factors such as seismic activity, seal integrity, and CO₂ leakage. Therefore, a CO₂ sequestration project involves multiple objectives which potentially exhibit trade-offs. Traditional multi-objective optimization frameworks require hundreds of forward simulations, which are computationally prohibitive for large-scale field application. In this study, we propose a deep learning-based workflow to efficiently optimize well control in CO₂ sequestration projects including surface injection and zonal rate allocation, enabling scalability to large-scale field applications. We developed a multi-resolution deep learning model based on the Fourier Neural Operator (FNO), which provides super-resolution capability. This capability allows the model to be trained on coarse-scale simulations while accurately predicting fine-scale reservoir pressure and saturation responses from permeability and injection schedules. As a result, data generation costs are substantially reduced, significantly lowering the overall cost of developing deep learning models. The original FNO architecture was modified to improve predictive accuracy across spatial resolutions, resulting in the proposed multi-resolution FNO model. This model functions as a data-driven proxy integrated with a multi-objective genetic algorithm to optimize CO₂ injection control, effectively balancing pressure management and storage efficiency. The power and efficiency of our approach are demonstrated on both synthetic and field applications, including a large-scale CO₂ injection at the Illinois Basin Decatur Project. Application to the synthetic model demonstrates the superior predictive performance of the developed multi-resolution FNO across coarse to fine-scale properties. For the field application, coarse-scale training data reduces training data generation cost by 90%, while the FNO-based proxy accurately predicts fine-scale pressure and saturation distribution, which are verified against a commercial reservoir simulator. The multi-objective optimization workflow, implemented using the FNO-based proxy model, achieves substantial improvements across multiple objectives while delivering performance orders of magnitude faster than traditional simulation-based approaches. We applied this workflow to CO₂ sequestration scenarios, including balancing pressure buildup with CO₂ injection amount, as well as optimizing surface and zonal injection rate allocations. This work introduces a novel multi-resolution FNO-based proxy model, applied to CO₂ injector control optimization. By combining FNO’s super-resolution capability with coarse-scale models, training data generation costs are greatly reduced. The proxy model accelerates forward simulations by orders of magnitude and enables efficient evaluation of multiple optimization scenarios for large-scale field applications.

Relative performance of hydraulic, hydro-mechanical and thermo-hydro-mechanical models on the geological sequestration of CO2 in deep saline aquifers

Study on enhancing the performance of CO2 sequestration materials from coal-based solid wastes via a two-stage carbonization mechanism

Experimental investigation on CO2 hydrate formation and growth in a liquid CO2 droplet system for hydrate-based CO2 sequestration

Low-temperature calcination of ultrafine α-CS from calcium carbide residue and diatomite: Towards enhanced CO2 sequestration and mechanical performance

Several detailed studies have shown that residual oil zones (ROZs) can present significant resources for additional hydrocarbon recovery as well as subsurface carbon dioxide (CO 2 ) sequestration via enhanced oil recovery by injecting CO 2 (CO 2 -EOR). Field development strategies included new wells drilled dedicated to main pay zones (MPZ) and ROZs, or existing wells in MPZs deepened to ROZs for commingled injection-production using different well patterns. The latter presented a challenge when discerning the injection and production from each of the zones, and for subsequent quantification of CO 2 sequestration and EOR potential from different patterns and from the field. In this paper, an innovative method for analyzing commingled injections and productions from MPZs and ROZs, with application to pattern-based data from four staggered line drive patterns in Wasson Field’s Denver Unit, Texas, USA, was developed. Decline curve and ratio-trend methods were used as means of history-matching and forecasting. Cumulative production-time and cumulative production-rate data for oil, gas, and water, as well as water-oil ratio (WOR) and gas-oil ratio (GOR), were analyzed along with injection data for time intervals covering major injection events in MPZ, or MPZ and ROZ combined. A combined analysis enabled inference of allocation of fluids into different zones during WAG (water alternating gas) injection and thereby estimation of CO 2 storage, utilization, and retention in different zones as a function of total injection. Results show that ROZs generally present higher CO 2 sequestration potential compared to MPZs, and a comparable incremental oil recovery factor of ∼20%, on average. Results based on ratio analysis further show that while the WOR trend of the pattern production is mostly dominated and controlled by ROZ, GOR is controlled by both intervals. Although the method relying on decline curves and the approach used in zonal fluid allocations are subject to their limitations, this study presents a practical and innovative well-pattern-based method to infer and forecast CO 2 sequestration and oil recovery quantities and fluid ratios from MPZs and ROZs in commingled operations and highlight the added potential offered by ROZs. • Residual oil zones (ROZs) can present significant resources for CCS and incremental oil recovery. • Deepening existing wells of mature fields to ROZs makes it difficult to quantify CCS and EOR potential due to commingled injection/production. • Four patterns from Wasson Field’s Denver Unit were analyzed to highlight the added potential offered by ROZs for CCS. • Results show that ROZs present higher CO 2 sequestration potential compared to main pays, and a comparable incremental oil recovery factor of ∼20%, on average.

Balancing toughness and adhesion of covalently-bonded organic-inorganic hydrogel for caprock leakage remediation in geological CO2 sequestration

A new insight into PCDD/Fs degradation from MSWI fly ash during CaCO3 oligomer crystallization via low-temperature thermal induction.

The increasing concentration of atmospheric CO2 demands the development of advanced and sustainable materials for carbon capture. Peptide-based nanostructures have emerged as promising candidates due to their tunable chemistry, biocompatibility, and ability to self-assemble into ordered supramolecular architectures. In this work, we investigate the adsorption behavior of CO2 on self-assembled A6H and A6R peptide membranes through classical molecular dynamics simulations. The A6H and A6R sequences consist of six alanine residues capped by a terminal histidine or arginine residue, respectively, and self-assemble into stable β-sheet membrane structures whose surface charge distribution and hydration organization are governed by the nature of the terminal residue. After equilibrating the membranes in an aqueous medium, water molecules were removed, and CO2 was introduced into the simulation box to evaluate gas-surface interactions under idealized gas-phase contact conditions. The results reveal distinct adsorption mechanisms governed by headgroup chemistry: the imidazole-terminated A6H interface exhibits preferential electrostatic and hydrogen-bond-driven interactions with CO2, whereas the guanidinium-terminated A6R membrane, characterized by a higher surface charge density, promotes enhanced electrostatic attraction and a larger number of CO2 binding events. These findings highlight how the chemistry of peptide terminal residues modulates CO2 affinity at ordered, self-assembled membrane interfaces, underscoring the potential of bioinspired peptide membranes as tunable platforms for carbon capture. By focusing on experimentally validated supramolecular architectures rather than peptide aggregates or hybrid systems, this study provides molecular-level insights that can inform the rational design of peptide-based sorbent materials for sustainable CO2 sequestration.

The rising atmospheric concentration of carbon dioxide (CO2) is assumed to be a key factor in global climate change, requiring robust and sustainable carbon conversion technologies. While carbonic anhydrase (CA) is a highly efficient enzyme for CO2 sequestration, its industrial application is limited by stability, cost, and scalability challenges. To address these limitations, we developed a CA-mimetic metal-amino acid (Phe-Zn(II)) bionanozyme featuring amyloid-like supramolecular cross-β-sheet architecture that provides high structural stability and recyclability. Gas chromatography (GC) analysis of a continuous flow bubble reactor charged with Phe-Zn(II) bionanozyme exhibits a CO2 conversion efficiency of approximately 18% in an aqueous medium (pH 7.0, 25 °C, ambient pressure), while maintaining remarkable structural integrity as confirmed by postcatalysis PXRD analysis. The amyloid-like supramolecular cross-β-sheet architecture, stabilized by π-π stacking and intermolecular hydrogen bonding, generates a confined catalytic microenvironment that enhances Zn(II) Lewis's acidity and promotes efficient CO2 hydration, which is crucial compared to previous reports. Next, density functional theory (DFT) calculations reveal a three-step catalytic pathway involving hydroxide ion generation, nucleophilic attack, and carbonic acid formation, with a rate-determining barrier of 12.3 kcal/mol, making the reaction feasible at room temperature. We also investigated the impact of different amino acids coordinated with Zn, finding that Phe-Zn(II) shows higher catalytic activity. This is due to the stronger electron-withdrawing effect of the phenyl group, which enhances the Lewis acidity of Zn2+, activates the Zn2+-OH2 bond, and lowers the rate-determining barrier. Taken together, the combination of experimental catalysis, structural robustness, and mechanistic validation highlights Phe-Zn(II) as a promising, cost-effective, and minimalistic catalyst yet efficient carbonic anhydrase mimic for CO2 conversion, paving the way for scalable and sustainable carbon sequestration strategies critical for mitigating climate change.

Sediment grain size and mineralogy change in sediment routing systems from source to sink. A better understanding of sediment routing allows improved predictions to be made of the bulk grain-size and mineralogy of sandstone fairways. We present a new appraisal of sediment routing in the Triassic Helsby Sandstone Formation (Sherwood Sandstone Group) and lowermost Mercia Mudstone Group of the British Isles, which constitute a key play for geological sequestration of CO2. These strata were deposited and supplied by a major, north-flowing river system, which is traced from its source region in north France to beyond the Irish Sea. We construct a new, integrated litho- and chronostratigraphic model to correlate key units across the British Isles. We then present sediment isopachs and volumes for this chronostratigraphic interval, and resolve palaeogeographic discrepancies using published sedimentological datasets, supplemented by a new synthesis of bulk sandstone mineralogy. Finally, we present a unified, updated sediment routing map for the Helsby Sandstone Formation. Substantial north-south differences in bulk sandstone mineralogy indicate that that sediment input from tributaries modified the composition of the Helsby Sandstone Formation along the course of the sediment routing system.

The controlled release of CO2 from structure I (sI) hydrates is essential for advancing carbon capture and storage (CCS) technologies. This study utilizes molecular dynamics (MD) simulations to investigate the pressure-dependent (1-80 bar) dissociation of CO2 hydrates in two aqueous deep eutectic solvents (DESs) medium: tetrabutylammonium bromide/ethylene glycol (TBAB/EG, DES1) and methyl triphenyl phosphonium bromide/ethylene glycol (MTPB/EG, DES2), each in a 1:4 M ratio. The results demonstrate that both pressure and DES composition critically influence hydrate stability. DES1 promotes greater CO2 release by reducing the CO2 density from 640 kg/m3 within the sI hydrate to 206 kg/m3 at the sI hydrate-aqueous DES1 interface at 80 bar. DES1 shows more hydrogen bonding between CO2 and aqueous water, while enhancing CO2 mobility, outperforming DES2. The radial distribution function (RDF) analysis shows that CO2 interacts more strongly with aqueous water in the presence of DES1, with a coordination number (CN) of approximately 27.02 at 1 bar, compared to DES2, which shows a maximum CN of about 26.37 at 1 bar. Higher CO2 mobility in the aqueous DES1 phase from CO2 hydrate is further supported by mean square displacement (MSD) compared to DES2. These findings establish a molecular-level framework for understanding how DES composition modulates hydrate dissociation, offering valuable insights for the rational design of DES-based media for targeted CO2 sequestration.

Methane hydrate possesses both high energy density and low carbon emission potential. Replacing CH4 with CO2 (CH4-CO2 replacement) is considered as a sustainable approach to simultaneously recover energy and achieve carbon sequestration. However, the influence mechanism of associated gases such as H2S on the replacement process under clay confinement and marine conditions remains poorly understood. This study employs molecular dynamics (MD) simulations to systematically investigate gas behavior evolution, hydrate structural responses, and microscale regulatory effects on the sequestration mechanism during CH4-CO2 replacement in the presence of H2S. Results show that CO2 hydrate nucleation mainly occurs on the surface of pre-existing CH4 hydrate. Due to pore structure and the adsorption layer on clay surfaces, hydrate tends to grow preferentially at the nanopore center. The presence of H2S does not significantly disturb the adsorption layer but enhances the early aggregation of gas bubbles. H2S also shows a higher occupancy capability for 512 and 51262 cages and can form topologically continuous hydrate frameworks by edge-sharing with cages occupied by other gases, promoting the formation of a closed hydrate shell. This structure improves CO2 sequestration efficiency and CH4 hydrate stability but hinders mass transfer and direct replacement pathways. These findings reveal the critical regulatory role of associated gases in marine hydrate reservoirs and provide theoretical support for methane hydrate exploitation and CO2 sequestration strategy design.

To elucidate the microscopic mechanisms of competitive adsorption between CO2 and CH4 under deep coal seam geological conditions, and to clarify the intrinsic advantages of CO2 adsorption sequestration and CH4 displacement from a molecular-scale perspective, this study conducts a systematic investigation using molecular simulation methods. A molecular structure model of coal was constructed and optimized based on elemental analysis and petrographic data of a representative deep coal sample. Combined grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations were employed to systematically analyze the adsorption capacity, density distribution, adsorption heat, adsorption potential energy, and adsorption selectivity of equimolar CO2/CH4 mixtures under geologically constrained conditions of high temperature (308-348 K), high pressure (0-20 MPa), and experimentally measured moisture contents (0-3.58%). The results demonstrate that CO2 consistently exhibits a significantly stronger competitive adsorption advantage over CH4 within the coal matrix, with higher adsorption capacity, adsorption heat, and adsorption potential energy and an adsorption selectivity coefficient always greater than 1. Increasing the temperature generally weakens the adsorption capacity of both gases, with CO2 showing greater sensitivity to temperature variations. At intermediate temperature (328 K) and moderate-to-high pressure, the CO2 adsorption density exhibits a nonmonotonic trend, reaching a peak, whereas the CH4 adsorption density decreases monotonically with increasing temperature. Increasing overall moisture content suppresses adsorption behavior; however, at low moisture content (approximately 1.22%), CO2 adsorption capacity and density are enhanced under high-pressure conditions, creating a favorable microscopic environment for CO2 retention. With further increases in the moisture content, the saturated adsorption capacity and density of both gases decrease markedly, with a more pronounced decline observed for CO2. This study reveals, at the molecular scale, the regulatory mechanisms of temperature, pressure, and moisture content on CO2-CH4 competitive adsorption behavior and clarifies the critical role of coal matrix adsorption in geological CO2 sequestration. The findings provide a theoretical basis for evaluating the potential of CO2-enhanced coalbed methane recovery (CO2-ECBM) and adsorption-based sequestration in deep coal seams.

Underground CO 2 storage, a core component of Carbon Capture and Storage (CCS) technologies, plays a pivotal role in mitigating greenhouse gas emissions and addressing global climate change. The importance of CO 2 storage capacity lies in its direct correlation with the potential for long-term, secure sequestration of CO 2 , thereby allowing for the continued use of fossil fuels while reducing greenhouse gas concentrations in the atmosphere. In this study, three machine learning methods (Multilayer Perceptron (MLP), Least Squares Support Vector Machine (LSSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS)) along with their hybrid combinations (hybrid MLP-LSSVM, hybrid MLP-ANFIS, and hybrid LSSVM-ANFIS) were employed to predict the storage capacity in various underground CO 2 storage sites. These sites include salt caverns, saline aquifers, depleted oil and gas reservoirs, coal seams, and basalt formations. Eight technical parameters influencing the storage capacity of these sites were utilized, comprising a total of 4545 data points. The results indicate that the three hybrid methods employed were highly effective in predicting CO 2 storage capacity, achieving a determination coefficient (R 2 ) value of 0.9999. Additionally, a sensitivity analysis performed using feature importance method revealed that the depth parameter had the greatest impact on the results, while the permeability parameter had the least influence. This study presents a comprehensive machine learning framework utilizing various types of underground CO 2 storage sites, incorporating a new set of technical parameters and innovative machine learning methods to predict the storage capacity of these sites. This approach significantly enhances both the comprehensiveness and accuracy of the findings. The findings of this study are significant for technical and economic evaluations of underground CO 2 storage sites, aiding in macro-decision-making for future projects in this sector while helping to minimize costs and risks. • Prediction of CO 2 storage capacity was done using a new set of 8 technical parameters. • A wide range of various geological CO 2 storage sites were investigated. • Storage capacity prediction was done using innovative hybrid machine learning methods. • The novel hybrid models achieved the highest accuracy. • The most significant feature for making predictions was the depth of CO 2 storage sites.

Electric field-induced morphological transition between water film and water bridge in shale nanopores: A novel switching strategy for enhancing oil recovery and CO2 sequestration