International Journal of Greenhouse Gas Control最新文献

{"title":"Mitigating caprock failure and leakage risks through controlled CO2 injection and coupled flow-geomechanics-fracturing simulation","authors":"Fangning Zheng,&nbsp;Birendra Jha ,&nbsp;Behnam Jafarpour","doi":"10.1016/j.ijggc.2025.104387","DOIUrl":"10.1016/j.ijggc.2025.104387","url":null,"abstract":"<div><div>During CO₂ injection into geologic storage formations, pressure increase induces stress changes in and around the reservoir, which may cause various geomechanical hazards such as caprock failure, ground surface uplifting, and induced seismicity. Caprock failure may create a leakage pathway for CO₂ to leak from the storage reservoir to shallower aquifers and even to the atmosphere. Identifying injection strategies that ensure the safety of operation in the presence of such risks is a challenging problem. In this paper, we present a novel framework for minimizing the CO₂ leakage potential due to caprock fracturing. We develop the framework by intgerating a coupled multiphase flow-geomechanics-fracturing simulation of geologic CO₂ storage process with an advanced numerical optimization algorithm that effectively utilizes the multiphysics mechanisms inherent in the model. The proposed optimization framework incorporates geological uncertainty to account for the lack of complete knowledge about the storage formation flow properties. The optimization algorithm determines the optimal well injection rate trajectories over uncertain descriptions of a heterogeneous storage reservoir to manage the pressure increase and minimize the risk of fracturing and CO₂ leakage. To minimize the caprock fracturing potential, the optimization algorithm maximizes the stress differences between the minimum effective stress and the fracture opening stress. The paper demonstrates the importance of applying optimization algorithms to systematically minimize the leakage risk. This is accomplished through a numerical optimization algorithm that uses a state-of-the-art coupled-physics modeling to search for and identify injection strategies that do not jeopardize caprock integrity. The presented approach is generic and can be adapted to minimize other environmental risks associated with geologic CO₂ storage.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104387"},"PeriodicalIF":4.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prospects of massively scalable nuclear-powered direct air capture as a climate solution","authors":"Maria Louise Bekkelund ,&nbsp;Schalk Cloete ,&nbsp;Jacob Joseph Lamb","doi":"10.1016/j.ijggc.2025.104390","DOIUrl":"10.1016/j.ijggc.2025.104390","url":null,"abstract":"<div><div>Direct air capture (DAC) has recently emerged as a promising and scalable solution for removing accumulated CO₂ in the atmosphere to mitigate global warming. Because DAC needs to capture CO₂ from highly diluted levels in the air, it has considerably higher energy demands than conventional CO₂ capture from concentrated industrial sources, making cheap input energy essential. The present study proposes that DAC facilities be powered by dedicated large-scale nuclear power plants designed to deliver the optimal heat/electricity ratio to a DAC unit serving as the condenser in the Rankine power cycle. Such plants are unbound by proximity requirements to CO₂ point sources or electricity demand centres and can be built where nuclear reactors are cheapest to construct, strongly improving the business case. A bottom-up techno-economic assessment and uncertainty quantification study for the year 2050 showed median and 90 % confidence intervals of the levelized cost of removed CO₂ to be 101.6 (71.3–153.9) $/ton when conventional nuclear reactor technology is used. An advanced configuration employing emerging high-temperature nuclear reactor technology to generate excess electricity for co-production of green methanol was slightly more expensive with a larger uncertainty range: 108.0 (68.8–176.1) $/ton. Despite the uncertainty in the assessment, the calculated CO₂ removal costs are attractive compared to projected CO₂ prices in climate change mitigation scenarios targeting substantial emissions reductions by mid-century. However, several non-economic challenges were identified and further work on these topics is recommended to clarify the long-term potential of nuclear-DAC technology as a leading climate change solution.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104390"},"PeriodicalIF":4.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty quantification for the CO2 storage efficiency in heterogeneous saline aquifers: The impact of variability in the lithology distribution","authors":"Andrey Afanasyev ,&nbsp;Irina Sypchenko ,&nbsp;Anna Andreeva ,&nbsp;Sergey Grechko ,&nbsp;Maria Dymochkina ,&nbsp;Vladimir Pavlov ,&nbsp;Kirill Maksakov ,&nbsp;Yulia Tsvetkova ,&nbsp;Elena Vedeneeva ,&nbsp;Anna Chernova ,&nbsp;Artem Penigin","doi":"10.1016/j.ijggc.2025.104384","DOIUrl":"10.1016/j.ijggc.2025.104384","url":null,"abstract":"<div><div>Geological storage of anthropogenic CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> can require a preliminary evaluation and ranking of subsurface reservoirs characterized by limited geophysical data. On the basis of regional concepts and trends, only some geostatistical variables can be estimated for the potential storage reservoirs near a CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emitter. Consequently, the storage efficiency may be subject to a substantial uncertainty caused by the variability in the heterogeneous distribution of geological parameters, which details are not exactly known without extensive geophysical surveys. We evaluate that uncertainty by the numerical modeling of the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage efficiency in open heterogeneous aquifers. Using geostatistical modeling, we generate a large dataset of synthetic heterogeneous reservoir models characterized by various lithofacies ratios and distributions. We then simulate the injection of CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> through a standalone vertical well to propagate the uncertainty forward to the storage efficiency. We show that the efficiency is characterized by a significant variance. Through an extensive parametric study of the statistical characteristics, we propose several recommendations for the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage site selection in the regional surveys. We show that the storage is more efficient in aquifers built of thin and extended reservoir interlayers at a specific net-to-gross ratio. The heterogeneity should also be isotropic in the lateral directions. We propose useful scaling relationships for the efficiency against the variogram ranges and the net-to-gross ratio. Finally, we show that the uncertainty in the efficiency cannot significantly be reduced using the well logging data.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104384"},"PeriodicalIF":4.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning assisted monitoring framework for geological carbon sequestration","authors":"Giovanni Zingaro ,&nbsp;Saeed Hatefi Ardakani ,&nbsp;Robert Gracie ,&nbsp;Yuri Leonenko","doi":"10.1016/j.ijggc.2025.104372","DOIUrl":"10.1016/j.ijggc.2025.104372","url":null,"abstract":"<div><div>Geological Carbon Sequestration (GCS) is expected to play a vital role in mitigating the harmful effects of climate change. However, the environmental risks associated with <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume migration, caprock fractures, induced seismicity, and fatal reactivation motivate the development of a high-resolution monitoring framework capable of predicting reservoir pressure build-up and <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume location in real-time. This study presents a Deep Learning (DL)-based monitoring framework that predicts reservoir pressure build-up and <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume location from sparse geophysical measurements during GCS injection operations. The proposed surrogate model consists of an Encoder–Decoder Artificial Neural Network (ANN) that inverts sparse surface uplift and injection pressure measurements to predict the pressure build-up field. A Long Short-Term Memory (LSTM)-based surrogate model is developed to capture the spatio-temporal evolution of the <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> saturation field, using the predicted pressure build-up history and <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> injection schedules. The synthetic dataset used is parameterized by variations in permeability, porosity, and dynamic <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> injection schedules. An ensembled eXplainable Artificial Intelligence (XAI) feature attribution approach is used to identify the most effective surface uplift sensor locations. The performance of the multi-network monitoring surrogate model is studied in the form of a case study involving the injection of <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> into a single wellbore. The accuracy of the proposed surrogate model demonstrates its potential for real-time monitoring of GCS processes.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104372"},"PeriodicalIF":4.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capillary pinning in sedimentary rocks for CO2 storage: Mechanisms, terminology and State-of-the-Art","authors":"Qin Zhang ,&nbsp;Sebastian Geiger ,&nbsp;Joep E.A. Storms ,&nbsp;Denis V. Voskov ,&nbsp;Matthew D. Jackson ,&nbsp;Gary J. Hampson ,&nbsp;Carl Jacquemyn ,&nbsp;Allard W. Martinius","doi":"10.1016/j.ijggc.2025.104385","DOIUrl":"10.1016/j.ijggc.2025.104385","url":null,"abstract":"<div><div>Capillary pinning refers to the immobilization of CO₂ at capillary barriers when the uprising CO₂ pressure is lower than the capillary entry pressure of the overlaying pore throats. Also known as local capillary trapping, it has been proposed as a fifth geologic CO₂ storage mechanism, alongside structural, solubility, residual, and mineral trapping. Despite extensive research, the fragmented terminology surrounding capillary pinning has led to confusion, making it challenging to synthesize findings effectively. Often conflated with mechanisms such as residual and hysteresis trapping, capillary pinning is commonly underestimated or completely overlooked in reservoir-scale models. Furthermore, difficulties in characterizing and upscaling small-scale geologic heterogeneities that influence capillary pinning contribute to significant uncertainties, with estimates of CO₂ trapped via this mechanism ranging from 3 % to 100 % of total CO₂ trapped via capillary actions. This review explores the fundamental mechanisms, experimental findings, and modeling approaches for assessing CO₂ capillary pinning in carbon capture and storage (CCS). It seeks to bridge the gap between the reservoir engineering community, with its extensive expertise in hydrocarbon recovery but that needs adjustments for CCS applications, and the subsurface storage community, which stands to benefit from this knowledge but often lacks access to relevant literature. Additionally, the study identifies key research opportunities to advance the understanding of capillary pinning in sedimentary rocks, ultimately enhancing the efficacy and reliability of CCS operations.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104385"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the feasibility of retrofitting legacy wells for CO2 geological sequestration","authors":"Mohamed L. Malki ,&nbsp;Joseph Heimerl ,&nbsp;Zhiwei Ma,&nbsp;Bailian Chen,&nbsp;Jolante Van Wijk,&nbsp;Mohamed Mehana","doi":"10.1016/j.ijggc.2025.104389","DOIUrl":"10.1016/j.ijggc.2025.104389","url":null,"abstract":"<div><div>In the United States (U.S.), the Environmental Protection Agency (EPA) oversees geologic sequestration (GS) wells, designating them as <em>“Class VI wells”</em>. However, the few operational Class VI wells and those in the permitting process are insufficient for the scale of sequestration needed. An effective solution to boost storage capacity is retrofitting legacy oil and gas wells into sequestration wells. Our analysis applies the <strong>R</strong>eusing <strong>EX</strong>isting wells for CO₂ Storage Operations (REX-CO₂) qualitative screening tool to evaluate the feasibility of retrofitting legacy Class II wells into Class VI wells for geological CO₂ sequestration. We examine regulatory requirements, well integrity factors, and publicly available data from a pilot study on 15 wells in New Mexico. In addition to the REX-CO₂ assessment, we propose primary and secondary barriers based on individual well documentation to address key integrity challenges. We find that wellbore retrofits for long-term carbon dioxide sequestration are only suitable for wells that possess the correct combination of well factors. For these wells, retrofitting could offer significant time and cost savings over drilling new wells. The study highlights the importance of improving publicly available wellbore data and regulatory coordination to enhance future well retrofit evaluations.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104389"},"PeriodicalIF":4.6,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intermittent injection of supercritical CO2 in oil-brine-saturated rocks: Experimental data and numerical modelling","authors":"Nikolai Andrianov,&nbsp;Samira Mohammadkhani,&nbsp;Behzad Rostami,&nbsp;Wael Fadi Al-Masri","doi":"10.1016/j.ijggc.2025.104382","DOIUrl":"10.1016/j.ijggc.2025.104382","url":null,"abstract":"<div><div>We present both experimental and numerical modelling results for an unsteady-state core flooding experiment, designed to mitigate risks associated with CO₂ injection in a depleted oil field offshore Denmark. The change in the samples’ permeability before and after the experiment is shown to be minimal, suggesting that a potential reduction in injectivity is unlikely. The experimental data include differential pressure measurements across the sample and brine saturation measurements in the sample as a function of time. We demonstrate that the experimental data can be history-matched with a range of parameters defining the relative permeability and capillary pressure functions for the three-phase oil-brine-CO₂ system. Two numerical models are developed for solving the forward problem: a computationally expensive, comprehensive equation-of-state based model and a faster incompressible immiscible proxy model. The inverse problem solutions are estimated using the proxy model, and the best-obtained saturation functions are utilized to solve the forward problem with the comprehensive model. The obtained indicative uncertainty ranges can be useful in the absence of more complex and costly experimental data, such as in-situ X-ray saturation measurements.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104382"},"PeriodicalIF":4.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning accelerated inverse modeling and forecasting for large-scale geologic CO2 sequestration","authors":"Bailian Chen ,&nbsp;Bicheng Yan ,&nbsp;Billal Aslam ,&nbsp;Qinjun Kang ,&nbsp;Dylan Harp ,&nbsp;Rajesh Pawar","doi":"10.1016/j.ijggc.2025.104383","DOIUrl":"10.1016/j.ijggc.2025.104383","url":null,"abstract":"<div><div>Traditional physics-simulation based approaches for inverse modeling and forecasting in geologic CO₂ sequestration (GCS) are very time consuming. For example, a single inverse modeling may take a few weeks for a large-scale CO₂ storage model without leveraging any high-performance computing. To speed up this process, we developed a novel approach that employs machine learning methods to integrate monitoring data into subsurface forecasts more rapidly than current physics-based inverse modeling workflows allow. These updated forecasts with the updated models from the inverse modeling process will be used to provide site operators with decision support by generating real-time performance metrics of CO₂ storage (e.g., CO₂ plume and pressure area of review). First, we developed a deep learning (DL) model to predict the pressure/saturation evolution in large-scale storage reservoirs. A feature coarsening technique was applied to extract the most representative information and perform the training and prediction at the coarse scale, and to further recover the resolution at the fine scale by 2D piecewise cubic interpolation. The accuracy of the feature coarsening-based DL model is validated with a reservoir model built upon a Clastic Shelf storage site. Thereafter, the feature coarsening-based DL model was utilized as forward model in the inverse modeling process where a classical data assimilation approach, ES-MDA-GEO, was applied. The efficiency and effectiveness of the proposed DL-assisted workflow for large-scale inverse modeling and forecasting was demonstrated with the Clastic Shelf storage model.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104383"},"PeriodicalIF":4.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of alkali-activated fly Ash-CO2 mineralization reaction and micro carbon sequestration mechanism","authors":"Lei Qin ,&nbsp;Siheng Lin ,&nbsp;Haifei Lin ,&nbsp;Shugang Li ,&nbsp;Niandong Chen ,&nbsp;Chengang Sun","doi":"10.1016/j.ijggc.2025.104377","DOIUrl":"10.1016/j.ijggc.2025.104377","url":null,"abstract":"<div><div>The mineralization reaction characteristics of alkali-activated fly ash with CO₂ directly influence its carbon sequestration capacity. Investigating the micro-scale carbon sequestration mechanisms in alkali-activated fly ash mineralization reactions is essential for improving the efficiency of mineralization and sequestration of fly ash. This study focuses on lignite fly ash from a coal-fired power plant in Shijiazhuang, China. A closed mineralization reactor experimental platform and a pressure drop method were utilized to characterize carbonation performance. The experimental results indicate that the alkali activation treatment effectively enhances the mineralization and carbon sequestration performance of fly ash. Alkali-activated lignite fly ash post-mineralization reaction surfaces showed significant carbonate material presence. After mineralization reaction, the distribution of particle sizes of alkali-activated lignite fly ash increased. The C-O bonds in mineralized fly ash increase with the degree of alkali activation, indicating that alkali activation treatment enhance the carbon sequestration capability of lignite fly ash.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104377"},"PeriodicalIF":4.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Similarity of trapping efficiencies for N2, CO2, H2, and air in indiana limestone based on capillary gas displacement and spontaneous water imbibition experiments","authors":"Abdulrauf R. Adebayo ,&nbsp;Mohamed Gamal Rezk ,&nbsp;Zuhair AlYousef ,&nbsp;Rahul S. Babu ,&nbsp;Almohannad Alhashboul","doi":"10.1016/j.ijggc.2025.104386","DOIUrl":"10.1016/j.ijggc.2025.104386","url":null,"abstract":"<div><div>The trapping coefficient is a parameter that describes the relationship between the displacement efficiency and capillary trapping of a gas in a porous medium. Accurate prediction of gas residual trapping is essential in evaluating underground carbon storage projects. The determination of the gas trapping coefficient could be complicated when a high-pressure and high-temperature experiment is needed to mimic subsurface conditions and when a complex gas is involved. The objective of this study is to investigate the role of gas type and operating conditions on trapping coefficient using Indiana limestone core samples with different permeability ranges and different gases such as CO₂, N₂, H₂, and air. A variety of displacement methods was employed such as a simple benchtop porous plate drainage chamber, an electrical resistivity-based saturation monitoring core flooding equipment, and a vapor desorption chamber to drain water-saturated rock samples to different levels of water saturation. The different methods displaced brine at different levels of capillary pressures. A benchtop spontaneous imbibition chamber was then used to spontaneously imbibe brine again under a capillary-dominated process until a residual gas saturation was attained in each case. A nuclear magnetic resonance (NMR) relaxation technique was used to monitor fluid distribution in the pores of the saturated samples, gas saturations after drainage, and the trapped gas saturations. An initial-residual saturation curve based on Land’s (1968) trapping model was then used to compare the trapping coefficient of the rock samples to the different gases. The trapping coefficients of the rock samples were similar for the tested gases, although hydrogen exhibited a relatively lower residual trapping efficiency. This observation further elucidates previous results and confirms that the trapping coefficient is mainly a function of pore structure. However, this observation is not conclusive until further tests are completed on rocks with a wider range of petrophysical properties and under the same flow conditions.</div></div>","PeriodicalId":334,"journal":{"name":"International Journal of Greenhouse Gas Control","volume":"144 ","pages":"Article 104386"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}