A comprehensive review of underground hydrogen storage: Insight into geological sites (mechanisms), economics, barriers, and future outlook

Journal of Energy Storage, Год журнала: 2024, Номер 90, С. 111844 - 111844

Опубликована: Май 9, 2024

Язык: Английский

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Difference between of coal and shale pore structural characters based on gas adsorption experiment and multifractal analysis

Fuel, Год журнала: 2024, Номер 371, С. 132044 - 132044

Опубликована: Май 30, 2024

Язык: Английский

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Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 20, 2024

Abstract This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify most suitable locations storing in caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, MLR—creating rock deposit suitability maps The performance these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Absolute (MAE), Percentage (MAPE), Root Square (RMSE), Correlation Coefficient (R 2 ), compared against actual dataset. CatBoost model demonstrated exceptional performance, achieving R 0.88, MSE 0.0816, MAE 0.1994, RMSE 0.2833, MAPE 0.0163. novel methodology, leveraging advanced machine learning techniques, offers unique perspective assessing potential is valuable asset stakeholders, government bodies, geological services, renewable energy facilities, chemical/petrochemical industry, aiding them identifying optimal

Язык: Английский

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Data-Driven Prediction of Storage Column Height for H2-Brine Systems: Accelerating Underground Hydrogen Storage

International Petroleum Technology Conference, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Abstract A practical solution to energy transition and the increasing demand for is underground hydrogen storage (UHS). The contribution of (H2) as a clean source has proven be an effective substitute future use meet net-zero target reduce anthropogenic greenhouse gas emissions. One most important factors affecting H2 displacement capacity under geological circumstances column height. objective this study underscore importance large-scale reliable machine learning algorithms evaluate predict height varied thermophysical salinity conditions. In study, dataset 540 datapoints evaluation prediction generated, which involves three main parameters: density difference (Δρ), interfacial tension (IFT) contact angle (θ). correlation angles against various reservoir depths used evaluated. Thermophysical conditions include pressures (0.1-20 MPa), temperatures (25-70°C), salinities including deionized water, seawater brines 1 3 molar concentrations salts (NaCl, KCl, MgCl2, CaCl2, Na2SO4) from our experimental data. (h) predicted using (ML) models, viz., random forest (RF), decision tree (DT) gradient boosting (GB). Statistical data analysis performed generate distribution coefficient calculated while feature determined identify relationship each input parameter with output Pearson, Spearman, Kendall models. RF GB, demonstrated in have shown promising results providing accurate predictions maintaining generalizability. Various error assessment metrics MSE, RMSE, MAPE R2 are utilized evaluation. Prediction resulted values 0.995 training 0.999 testing model. Whereas GB model also superior performance 0.997 during phase phase. However, DT 0.994 phases respectively. While MSE value 0 obtained indicated overfitting. findings suggest that data-driven ML models can powerful tool accurately predicting effectively determine capacity, reducing time cost associated determination traditional methods. addition, advanced explored overcome challenges pertinent

Язык: Английский

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Improving wettability estimation in carbonate formation using machine learning algorithms: Implications for underground hydrogen storage applications

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 111, С. 781 - 797

Опубликована: Фев. 27, 2025

Язык: Английский

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Advanced Smart Models for Predicting Interfacial Tension in Brine-Hydrogen/Cushion Gas Systems: Implication for Hydrogen Geo-Storage

Energy & Fuels, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

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Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications

Journal of Petroleum Exploration and Production Technology, Год журнала: 2025, Номер 15(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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Application of Ensemble Learning Paradigms in Predicting Interfacial Tension of H2/Cushion Gas Systems and the Implications on Subsurface H2 Storage

International Petroleum Technology Conference, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Abstract The role of hydrogen geo-storage and production in addressing global warming energy demand concurrently cannot be understated. Diverse factors such as interfacial tension (IFT) wettability influence safe effective production. IFT controls the maximum H2 storage column height, capacity, capillary entry pressure. Current laboratory experimental techniques for determination H2/cushion gas systems are resource-intensive. Nonetheless, extensive data supports machine learning (ML) deployment to determine time-efficiently cost-effectively. Hence, this work evaluated predictive capabilities supervised ML paradigms including random forest, extra trees regression, gradient boosting regression (GBR), light machine, wherein novelty study lies. An comprehensive dataset comprising 2564 instances was gathered from literature, encompassing independent variables: pressure 0.10–45 MPa), temperature (20–176 °C), brine salinity (0–20 mol/kg), hydrogen, methane, carbon dioxide, nitrogen mole fractions (0-100 mol.%). pre-processed split into 70% model training 30% testing. Statistical metrics visual representations were utilized quantitative qualitative assessments models. Leverage approach subsequently applied classify different categories verify statistical validity database reliability constructed paradigms. impact variables on prediction using Spearman correlation, permutation importance, Shapley Additive Explanations (SHAP). Nitrogen CO2 demonstrated least greatest gas/brine based correlation analysis, SHAP. Generally, developed successfully captured underlying relationships between IFT, recording an overall R2 > 0.97, MAE < 1.30 mN/m, RMSE 2 AARD 2.3% GBR superior performance, yielding highest lowest MAE, RMSE, 0.987, 0.507 0.901 0.906%, respectively. also provided more accurate results pure H2/water than empirical molecular dynamics-based correlations by other scholars. Only 0.43–2.11% outside range, underscoring beneficial tools toolbox domain experts, which could fast-track workflows minimize uncertainties surrounding conventional aqueous systems. This progress is promising mitigating loss optimizing strategies

Язык: Английский

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Data-driven modelling to predict interfacial tension of hydrogen–brine system: Implications for underground hydrogen storage

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104608 - 104608

Опубликована: Март 1, 2025

Язык: Английский

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Novel intelligent models for prediction of hydrogen diffusion coefficient in brine using experimental and molecular dynamics simulation data: Implications for underground hydrogen storage in geological formations

Journal of Energy Storage, Год журнала: 2025, Номер 118, С. 116297 - 116297

Опубликована: Март 21, 2025

Язык: Английский

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