Laboratory study of cyclic underground hydrogen storage in porous media with evidence of a dry near-well zone and evaporation induced salt precipitation

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 71, С. 515 - 527

Опубликована: Май 22, 2024

Large-scale storage of 'green' hydrogen is essential in our quest to transition renewable energy sources and achieve the net-zero emission targets. As such, Underground Hydrogen Storage (UHS) geological porous media, such as depleted oil gas fields or saline aquifers, provides a cost-effective solution store large volumes (H2) buffer seasonal fluctuations supply demand. Due novelty topic, many research gaps persist, but we focus on H2 transport characteristics subsurface, recoverability during cyclic operations, chemical interactions, acoustic velocity an indicator saturation. In this study, investigate UHS using triaxial core-flooding experiment address above-mentioned by replicating field operations laboratory setup at representative subsurface conditions. Unstable displacement due immiscible two-phase flow led early breakthrough 18% saturation, ultimate saturation 36% withdrawal efficiency 78% first cycle. However, charging discharging eventual high 95%. Observed interactions 3-day experiments were negligible, results also indicated strong potential for evaporation-induced salt precipitation after injection dry gas, leading decreased reservoir porosity permeability over time. Our ultrasonic measurements confirmed sensitivity P-wave which 3.5% when increased 36%. This indicates that plume leaks should be possible monitor 4D seismic surveys. help better understand flow, storage, UHS. The indicate increases matures evidence near-well zone evaporation induced reservoir.

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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Key aspects of underground hydrogen storage in depleted hydrocarbon reservoirs and saline aquifers: A fundamental review and understanding

Energy Geoscience, Год журнала: 2024, Номер 5(4), С. 100339 - 100339

Опубликована: Авг. 23, 2024

Язык: Английский

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Integrating Capacity and Efficiency for Optimal Hydrogen Storage Site Selection in Saline Aquifers

Energy & Fuels, Год журнала: 2024, Номер 38(5), С. 4733 - 4742

Опубликована: Фев. 21, 2024

Hydrogen (H2) energy is a promising transition pathway from conventional fossil fuels to sustainable clean energy. However, H2 requires large storage capacity because of its low volumetric energy–density nature. Underground sites provide ample space for storage. In this work, we proposed general workflow select saline aquifers' optimal sites, considering the and operational efficiency. We developed comprehensive data set high-fidelity numerical simulations quantify effects geologic operating parameters on performance. The simulation results are used train robust reduced-order model (ROM) estimate Due high accuracy flexibility, selected multilayer perceptron develop our ROM. mean squared errors all ROMs less than 0.0001, coefficients determination (R2) were higher 0.99. Integrating performance estimations with calculations capacity, quantitatively evaluated in aquifers using designed objective function. applied Intermountain-West (I-WEST) region, which central mountain area United States, including Arizona, Colorado, New Mexico, Montana, Utah, Wyoming. identified top three 12 potential sites. Our agnostic region could be other areas. Generally, work supports safe efficient operations I-WEST region.

Язык: Английский

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Hydrogen storage potential of salt domes in the Gulf Coast of the United States

Journal of Energy Storage, Год журнала: 2024, Номер 82, С. 110585 - 110585

Опубликована: Янв. 25, 2024

Язык: Английский

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