Gas Science and Engineering最新文献

{"title":"Real-time prediction and inverse design of multiphase CO2 trapping in deep saline aquifers using machine learning enhanced by SHAP analysis","authors":"Shixuan Cheng ,&nbsp;Kai Zhang ,&nbsp;Yihao Li ,&nbsp;Zhangxin Chen","doi":"10.1016/j.jgsce.2025.205782","DOIUrl":"10.1016/j.jgsce.2025.205782","url":null,"abstract":"<div><div>Accurate forecasting of multiphase CO₂ behavior is critical for safe large-scale deployment of carbon capture and storage projects. A total of 9835 high fidelity CMG GEM simulations, covering geological and operational uncertainties in deep saline aquifers, were generated and subsequently used to benchmark thirteen c algorithms for the concurrent prediction of CO₂ trapping mechanisms. Attention based tabular networks TabTransformerLite and SAINTLite together with the decision ensemble NODELite outperformed linear, support vector, tree ensemble, and fully connected neural network baselines. TabTransformerLite secured the best performance with nRMSE of 0.033, R² of 0.92, and minimal fold to fold variability. Shapley value analysis placed depth and permeability as primary controls on dissolved, residual, and mineral precipitated CO₂ compared to the injection rate and porosity. An inverse design workflow used the trained model to identify operating windows that lift mineral trapping above the ninetieth percentile, indicating optimal depth near 2.25 km, permeability above 1.6 Darcy, and injection rates between 7.5 and 14.7 × 10³ m³/day. The resulting framework connects model predictions to geographic information systems for site screening and to real time digital twin optimization, providing a scalable millisecond speed alternative to physics-based simulation for CO₂ storage planning and monitoring.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"145 ","pages":"Article 205782"},"PeriodicalIF":5.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of leakage characteristics and leakage point diameter prediction in supercritical CO2 pipeline","authors":"Zheyuan Liu ,&nbsp;Jiming He ,&nbsp;Kang Li ,&nbsp;Shi Shen ,&nbsp;Huiyong Liang ,&nbsp;Xin Lv ,&nbsp;Zaixing Liu","doi":"10.1016/j.jgsce.2025.205780","DOIUrl":"10.1016/j.jgsce.2025.205780","url":null,"abstract":"<div><div>This study systematically investigates supercritical CO₂ pipeline leakage dynamics using an integrated experimental platform. Real-time multi-sensor monitoring and high-speed photography reveal continuous phase transitions during leakage, with pressure and temperature exhibiting three distinct stages: stable phase, rapid decline phase, and slow stabilization phase. The Joule-Thomson effect dominates temperature variations, with cooling intensity scaling proportionally to flow velocity. The leakage orifice size significantly influences leakage dynamics: orifices lead to exponentially increasing pressure drops. A relationship between orifice diameter (D) and pressure drop (△P) is established. Jet development is categorized into three stages—slow growth, rapid expansion, and stabilization. Leakage orifice significantly affects jet outlet velocity; at an orifice of 1 mm, velocity changes from 6 m/s to 20 m/s in a short time, and larger orifices exacerbate this change. Variations in leakage orifice diameter exert a more pronounced influence on the jet expansion angle (<em>θ</em>_<em>c</em>). When the orifice diameter increases from 0.5 mm to 3 mm, <em>θ</em>_<em>c</em> rises from 16.365° to 40.965°. In contrast, the core contraction angle (<em>θ</em>_<em>d</em>) demonstrates higher sensitivity to pressure fluctuations: as pressure increases from 8.7 MPa to 9.31 MPa, <em>θ</em>_<em>d</em> increases from 4.844° to 8.615°. These findings provide critical data support for safety design, risk assessment, and emergency response strategies of supercritical CO₂ pipelines.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"145 ","pages":"Article 205780"},"PeriodicalIF":5.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclic CO2/N2 injection enhances CO2 storage and CH4 recovery in anthracite by optimizing pore structure and competitive adsorption","authors":"Yunzhong Jia ,&nbsp;Jiaqi Yi ,&nbsp;Xinyu Wang ,&nbsp;Zhaolong Ge ,&nbsp;Wenyu Fu ,&nbsp;Yiyu Lu ,&nbsp;Caiyun Xiao","doi":"10.1016/j.jgsce.2025.205779","DOIUrl":"10.1016/j.jgsce.2025.205779","url":null,"abstract":"<div><div>Synergistically optimizing coalbed methane recovery rate and CO₂ sequestration efficiency remains a core bottleneck in CO₂/N₂-ECBM engineering. The competitive adsorption mechanisms underlying the differential impacts of varying CO₂/N₂ ratios on recovery and sequestration are key factors influencing ECBM project performance. Therefore, this study focuses on simulating competitive adsorption mechanisms driven by CO₂/N₂ ratio variations in deep coal reservoir environments to elucidate their critical influence on ECBM efficiency. This study proposes synergistic injection of high-concentration CO₂ from industrial flue gas with associated N₂ into deep coal seams, leveraging N₂-assisted CO₂ delivery through connected pores to improve total storage capacity, thereby realizing cost-effective CO₂ geological storage coupled with coalbed methane stimulation. Through breakthrough adsorption experiments, low-temperature liquid nitrogen adsorption, and nuclear magnetic resonance (NMR) techniques, we investigate the impacts of varying CO₂/N₂ ratios and injection modes (constant pressure/cyclic) on the pore structure and adsorption characteristics of anthracite. Results demonstrate that cyclic injection mode enhances CO₂ adsorption capacity by 28.74 %–40.01 % compared to constant-pressure injection, with a strong positive correlation between micropore volume and adsorption capacity. Although increased N₂ proportion induces pore compression effects, cyclic pressure fluctuations significantly improve macropore porosity (5.67 % increase) and connectivity, facilitating gas diffusion. A three-stage competitive adsorption model is proposed: CO₂ preferentially occupies high-affinity sites, N₂ optimizes transport pathways through pore network expansion, ultimately forming a storage pattern dominated by micropore adsorption and macropore diffusion under dynamic equilibrium. Our results indicate that a 4:1 CO₂/N₂ ratio combined with cyclic injection mode and enhanced meso-macropore porosity synergistically improves both storage efficiency and methane recovery, providing theoretical guidance for gas proportioning and process optimization in CO₂/N₂-ECBM projects.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"145 ","pages":"Article 205779"},"PeriodicalIF":5.5,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid physics-machine learning model to predict density of mixtures of CO2 with impurities","authors":"Mohamad Hussein Makke,&nbsp;Kassem Ghorayeb","doi":"10.1016/j.jgsce.2025.205768","DOIUrl":"10.1016/j.jgsce.2025.205768","url":null,"abstract":"<div><div>Carbon Capture, Utilization, and Storage (CCUS) plays a pivotal role in reducing greenhouse gas emissions, essential for limiting global warming to below 1.5 °C by 2100 and achieving carbon neutrality by 2050. Modeling carbon dioxide (CO₂) density is crucial for optimizing CO₂ transportation and storage systems. However, captured CO₂ streams from power sources often contain impurities such as Oxygen, Nitrogen, Carbon Monoxide, Argon, Sulfur Dioxide, Hydrogen, Methane, Water, and Hydrogen Sulfide. These impurities significantly impact transmission properties and challenge the predictive capabilities of equations of state (EoSs) thermodynamic models.</div><div>This study investigates the effects of impurities on CO₂ stream density using a comprehensive dataset of 134,204 density data points. Fourteen EoSs, including cubic, virial, physical, and multi-parameter equations, were evaluated to determine optimal modeling conditions. Moreover, machine learning models trained with experimental and synthetic data from equation of state (EoS) models were employed towards a high predictive capability model. This synthetic data was generated, within CCUS pipeline operating conditions, using the best-performing EoSs, primarily multiparameter equations with an Absolute Average Relative Deviation &lt;3 %. Random Forest and Artificial Neural Networks provided robust density predictions, even in complex thermodynamic regions with a Coefficient of Determination &gt;0.96.</div><div>This hybrid approach offers a novel pathway for improving density predictions of CO₂-rich systems, supporting more efficient and reliable transportation models. To the best of our knowledge, no previous study considered such a comprehensive dataset and EoSs for predicting the density of CO₂ rich mixtures using this hybrid approach.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"144 ","pages":"Article 205768"},"PeriodicalIF":5.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural gas hydrate exploitation: A comprehensive review of structural properties, technical progress and environmental challenges","authors":"Michelle Tiong ,&nbsp;Wen-zheng Peng ,&nbsp;Qi Liu ,&nbsp;Shengkun Wu ,&nbsp;Hang Ye ,&nbsp;Shuang-xing Liu ,&nbsp;Ming Xue ,&nbsp;Chenggang Xian","doi":"10.1016/j.jgsce.2025.205769","DOIUrl":"10.1016/j.jgsce.2025.205769","url":null,"abstract":"<div><div>Natural gas hydrates (NGH), represent a promising and environmentally friendly energy resource due to their high energy density and global abundance in permafrost and deep marine sediments. However, their commercial exploitation remains constrained by significant technical, geological, and environmental challenges. Although various extraction technologies, such as depressurization, thermal stimulation, CO₂ replacement, and chemical additives have demonstrated potential in laboratory settings, there is limited synthesis of how technical advancements align with environmental risk management strategies. This review provides a comprehensive assessment of NGH extraction technologies, beginning with an overview of hydrate structures and classifications. The effectiveness and limitations of major extraction methods were critically evaluated, with further discussion on the role of hybrid techniques and reservoir transformation strategies in enhancing recovery efficiency. Particular attention was given to the environmental risks and scalability barriers associated with offshore development. In addition, the review highlights the growing importance of numerical modeling for simulating coupled thermal, hydraulic, and kinetic processes, emphasizing the need for multiscale approaches calibrated with field data to improve predictive accuracy. Future research should focus on optimizing hybrid recovery methods, enhancing model fidelity, and facilitating real-world implementation. These efforts are essential for enabling safe, efficient, and climate-aligned <span>NGH</span> exploitation in support of clean energy development and the global energy transition.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"144 ","pages":"Article 205769"},"PeriodicalIF":5.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental and molecular simulation study of in-situ self-growing nano-liquid crystal for smart water management in natural gas reservoirs","authors":"Rui Liu ,&nbsp;Jipeng Shi ,&nbsp;Qin Peng ,&nbsp;Zezhou Chen ,&nbsp;Mingqi Sun ,&nbsp;Kang Liu ,&nbsp;Wanfen Pu","doi":"10.1016/j.jgsce.2025.205767","DOIUrl":"10.1016/j.jgsce.2025.205767","url":null,"abstract":"<div><div>Natural gas reservoir development faces significant challenges from water invasion due to reservoir heterogeneity. Thus, effectively controlling water invasion while simultaneously facilitating gas production presents a significant historical mission and a vital research hotspot. A key research gap exists in developing water control agents for natural gas reservoirs that combine smart selective plugging and efficient profile control, with limited understanding of GO-based nanocomposites mechanisms and functionalization. Thus, this study pioneers a novel method for designing graphene oxide (GO)-based nano-liquid crystal sheets (GOLC) to address this issue. The design was inspired by the in-situ scale growth due to the association with inorganic salt ions in the formation water and hydrocarbon dissociation of polyethylene glycol (PEG) hybrid GO nanocomposite. Experimental and molecular simulation characterizations were employed to elucidate and investigate the intelligent water control and gas evacuation mechanism of GOLC. GOLC exhibits excellent dispersion in polar solvents. The in-situ growth of GOLC, driven by electrostatic interactions between PEG and metal cations (Ca²⁺ &gt; Mg²⁺ &gt; Na⁺), enlarges GOLC sheets from 50 nm to 3500 nm, achieving an 84.81 % plugging rate in porous media. Interestingly, these self-growing GOLC structures dissociate upon contact with methane (CH₄), showcasing effective water blocking and gas drainage characteristics, which results in a 12.9 % increase in recovery rate. This research will provide an innovative perspective for the synthesis and application of newly generated nanomaterials following a de novo design to intelligently control water in natural gas reservoirs.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"144 ","pages":"Article 205767"},"PeriodicalIF":5.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transfer learning approach for cross-area hydraulic fracturing pressure prediction","authors":"Lei Hou ,&nbsp;Xiaobing Bian ,&nbsp;Liang Fu ,&nbsp;Jiangfeng Luo ,&nbsp;Jiale He ,&nbsp;Tingxue Jiang ,&nbsp;Fengshou Zhang","doi":"10.1016/j.jgsce.2025.205760","DOIUrl":"10.1016/j.jgsce.2025.205760","url":null,"abstract":"<div><div>The data-driven algorithms provide a powerful tool for predictions of hydraulic fracturing pressures, which is crucial for the design of pumping schedules and safety operations. For unconvetional oil and gas, the rapid declines in productions require continuous exploration of new blocks, during which cross-area pressure prediction becomes essential. However, significant regional variations in geology and limited access to geological data, make data-driven models mainly reliable in the data source region and restrict their generalization across regions. This study builds a transfer learning framework for pressure prediction across areas. In this framework, a deep learning model is first trained by historical data from the basic region, and then fine-tuned using eight fracturing stages of data (geological, wellbore, and pumping records) from the target region. The results show that using data from six fracturing stages is sufficient to achieve desirable generalization performance. This strategy transfers the experiences learned from the basic region to new regions, which may break the data-dependence barrier. Different transfer learning strategies, the core technique for knowledge transfer, are optimized to boost the predicting accuracy and transferring efficiency with minimum tuning dataset. Taking shale gas fracturing for instance, the performance of the new framework is demonstrated by the errors of pressure predictions, with root mean square error (RMSE) 2.26–8.22 MPa, r-square (R²) error 0.50–0.91, and symmetric mean absolute percentage (SMAP) error 2.09–8.55 %. The gradually-unfreezen strategy demonstrates superior performance than the full-unfreezen and full-freezing strategies, and performs better as more tuning data are incorporated. A comparison between the new framework and traditional algorithms (Support Vector Regression and Random Forest) further demonstrates the accuracy of our new method. The SHAP (SHapley Additive exPlanations) value indicates that pump rate is the most influential factor for pressure prediction, followed by perforation friction and well depth. The successful application of the transfer learning framework bridges the gap between different regions, leveraging past experience to improve the efficiency of new developments, particularly beneficial for unconventional shale developments that sustain productions by new explorations. Moreover, the transfer learning strategy may improve the inherent data-dependence weakness of data-driven algorithms, and then promote the generalization of well-trained machine-learning models.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"144 ","pages":"Article 205760"},"PeriodicalIF":5.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonic-assisted synthesis of Bio-MOF-based cobalt-glutamic acid for CO2/CH4 adsorption: Experimental and isotherm modeling","authors":"Fayza Yulia ,&nbsp;Muhammad Ridha ,&nbsp;Ahmad Singgih ,&nbsp;Arya Wirayuda ,&nbsp;Euis Djubaedah ,&nbsp;Tarno ,&nbsp;Nonni Soraya Sambudi ,&nbsp;Sri Hastuty ,&nbsp;Nasruddin","doi":"10.1016/j.jgsce.2025.205762","DOIUrl":"10.1016/j.jgsce.2025.205762","url":null,"abstract":"<div><div>The natural gas and carbon capture industries require sustainable solutions, therefore producing eco-friendly materials is important. The Bio-Metal Organic Framework (Bio-MOF) of cobalt chloride and L-glutamic acid (Co-Glu) produced ultrasonically in this research captures and stores CO₂ and CH₄ sustainably. For industrial applications, the Bio-MOF reduces energy usage, improves material characteristics, and scales for mass manufacturing. The synthesis was improved using several reactant molar ratios, and SEM, XRD, TGA, FTIR, and BET were used to analyze the material's structure and stability. Post-combustion experiments evaluated CO₂ and CH₄ gas adsorption ability from ambient pressure up to 3.0 MPa. The research found that the Bio-MOF had high uptake capacity for CO₂ and CH₄ at 27 °C, with CO₂ uptakes of 0.51 kg/kg and CH₄ uptakes of 0.12 kg/kg, demonstrating excellent performance at moderate pressures. Isotherm modelling were also undertaken with the langmuir, toth, and langmuir-freundlich (Sips) model to simulate adsorption isotherms with R² values of 0.99 and AARE values of 0.05 observed in most regressions. The Clausius-Clapeyron and Chakraborty-Saha-Koyama (CSK) equation was used to compute the enthalpy of adsorption up to 700 kJ/kg, highlighting the energy-efficient potential of Bio-MOFs in gas storage applications.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"145 ","pages":"Article 205762"},"PeriodicalIF":5.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermo-mechanical stress analysis of wellbore integrity in CO2 storage wells: A case study from the Tubåen Formation, Snøhvit Field Norway","authors":"Jawad Ali Khan ,&nbsp;Nejma Djabelkhir ,&nbsp;Mazda Irani ,&nbsp;Kegang Ling ,&nbsp;Saleem Ghori ,&nbsp;Sahar Ghannadi","doi":"10.1016/j.jgsce.2025.205758","DOIUrl":"10.1016/j.jgsce.2025.205758","url":null,"abstract":"<div><div>Ensuring long-term wellbore integrity is a critical challenge in carbon capture and storage (CCS) operations, where mechanical failure at the casing–cement and cement–formation interfaces can create potential leakage pathways. This study presents a fully coupled thermal mechanical Poroelastic finite element analysis (FEA) simulation to evaluate stress redistribution and failure risks in the Tubåen Formation, Snøhvit Field. Previous studies primarily focused on bulk stress distribution while this simulation captured dynamic stress evolution across initial and post-injection phases, providing insight into interface-specific failure mechanisms. Low-temperature CO₂ injections induced localized stresses amplification leading to radial cracking, debonding, and microannulus development. Long-term well integrity is seriously compromised by these mechanisms. The study also integrated the Drucker–Prager plasticity model to identify failures. High-resolution stress distribution maps identified critical failure zones, enabling more accurate predictions of failure initiation. This approach captured transient effects considering long term CO₂ injection projects, providing an understanding about well integrity challenges specifically at the cement interfaces. In addition to improving long-term storage performance and regulatory compliance, the findings support proactive risk mitigation strategies for CCS well designs.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"145 ","pages":"Article 205758"},"PeriodicalIF":5.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review on the loss during hydrogen storage in depleted gas reservoirs","authors":"Gang Chen,&nbsp;Shilai Hu,&nbsp;Wei Zhang,&nbsp;Jingang Fu,&nbsp;Jiqiang Li","doi":"10.1016/j.jgsce.2025.205759","DOIUrl":"10.1016/j.jgsce.2025.205759","url":null,"abstract":"<div><div>Underground hydrogen storage (UHS) is essential for large-scale energy storage as it can address the intermittency of renewable energy sources and support the transition to a low-carbon energy structure. Compared with other suitable sites for UHS, depleted gas reservoirs may be the best due to their large storage capacity, numerous potential options and complete surface facilities. However, the lack of practice and theory about the loss during hydrogen storage in depleted gas reservoirs (HSDGs) hinders its development and large-scale utilization. This review aims to minimize the loss and improve withdrawal efficiency via analyzing and discussing previous works on hydrogen loss during HSDGs. To fill this gap, according to the mechanisms of hydrogen loss during HSDGs, the loss is classified into two categories in this work, real loss and fake loss. Real loss is the complete reduction in hydrogen caused by chemical reactions, gas diffusion, and geological leakage. Fake loss means that hydrogen stored in gas reservoirs can't be withdrawn under the specific conditions due to gas dissolution in formation water, residual trapping, adsorption in rock minerals, and other engineering factors, such as cushion gas, well perforation and cyclic operation. Given two categories of hydrogen loss, the pathways, mechanisms, quantity and influencing factors were evaluated and discussed critically, and specific strategies were also proposed to minimize the loss. Finally, the limitations of current valuable works were highlighted, and future works were developed. This review can help scientists and engineers maximize the withdrawal efficiency of HSDGs.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"144 ","pages":"Article 205759"},"PeriodicalIF":5.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}