Prediction of hydrogen−brine interfacial tension at subsurface conditions: Implications for hydrogen geo-storage

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 58, С. 485 - 494

Опубликована: Янв. 25, 2024

Underground hydrogen storage (UHS) offers a promising approach for the of significant volumes gas (H2) within deep geological formations, which can later be utilized energy generation when necessary. Interfacial tension (IFT) between H2 and formation brine plays vital role in influencing distribution at pore scale and, ultimately, capacity. In this research, we developed four intelligent models: Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP). These models were designed to predict IFT utilizing pressure, temperature, molality. Additionally, fine-tuned three explicit correlations previously our research. To assess influence each parameter on IFT, conducted comprehensive analysis raw data exclude doubtful samples. This was followed by rigorous model development, including hyperparameter tuning, finally, an examination using testing data. The results clearly demonstrate superiority RF model, achieving high accuracy reliability with coefficients determination (R2), root mean square error (RMSE), average absolute relative deviation (AARD) values 0.96, 1.50, 1.84 %, respectively. exemplary performance attributed its inherent characteristics. ensemble excels capturing complex relationships, thereby enhancing predictive solidifying over other study. Furthermore, feature importance revealed that temperature has most influence, molality pressure. Moreover, assessed these through external not used initial training stages. Our study highlights exceptional power emphasizing practical selecting enhanced reliability. proposed method shows potential industrial applications, especially optimizing underground storage.

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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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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Long-term stability forecasting for energy storage salt caverns using deep learning-based model

Energy, Год журнала: 2025, Номер unknown, С. 134854 - 134854

Опубликована: Фев. 1, 2025

Язык: Английский

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Machine Learning-Based Interfacial Tension Equations for (H₂ + CO₂)-Water/Brine Systems over a Wide Range of Temperature and Pressure

Langmuir, Год журнала: 2024, Номер 40(10), С. 5369 - 5377

Опубликована: Фев. 28, 2024

Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H2-brine interfacial tension (IFT) is crucial parameter structural trapping geological locations and gas–water two-phase flow subsurface porous media. On the other hand, cushion gas, such as CO2, often co-injected with H2 to retain reservoir pressure. Therefore, it imperative accurately predict (H2 + CO2)-water/brine IFT under UHS conditions. While there have been number of experimental measurements on H2-water/brine IFT, an accurate efficient model conditions still lacking. In this work, we use molecular dynamics (MD) simulations generate extensive databank (840 data points) over wide range temperature (from 298 373 K), pressure 50 400 bar), gas composition, brine salinity (up 3.15 mol/kg) for typical conditions, which used develop machine learning (ML)-based equation. Our ML-based equation validated by comparing available equations various systems (H2-brine/water, CO2-brine/water, CO2)-brine/water), rendering generally good performance (with R2 = 0.902 against 601 points). The developed can be readily applied implemented applications.

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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Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration

Results in Engineering, Год журнала: 2025, Номер 25, С. 104047 - 104047

Опубликована: Янв. 25, 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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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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A two-step failure identification approach using a stochastic optimization-based ensemble learning model for beams without pristine data

Engineering Structures, Год журнала: 2025, Номер 334, С. 120253 - 120253

Опубликована: Апрель 11, 2025

Язык: Английский

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