Physics-informed and chemistry-aware framework to predict hydrogen-brine interfacial tension for underground hydrogen storage reservoir conditions; Geoenergy Science and Engineering; Vol. 266
| Parent link: | Geoenergy Science and Engineering.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 266.— 2026.— Article number 214609, 23 p. |
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| Sumari: | Title screen The distribution of hydrogen (H2) and cushion gas mixtures in underground hydrogen (gas) storage reservoirs (UHS) is partly controlled by capillary forces, which are determined by the gas-brine interfacial tension (IFT) in the pore space. Reliably and transparently predicting IFT from gas compositional and reservoir environmental conditions is useful for determining storage capabilities and gas-containment risks in UHS reservoirs. A 2649-data-record dataset is compiled for published laboratory tests of gas-brine mixtures at subsurface salinity, temperature, and pressure conditions relevant to UHS for predicting IFT. The gas compositions are divided into three classes: H2 only (Class I), binary gas combinations (H2+CO2, H2+CH4, or H2+N2; Class II), and the quaternary gas combination (H2+CO2+CH4+N2; Class III). A series of empirical IFT-prediction equations are developed based on the physics-guided, chemistry-aware-feature-influence (CAFI) method and the non-parametric, customized formulaic optimization (CFO) methods. Thirteen equations are tested on the data records of the three specific gas classes and compared with published IFT empirical equations. A further three equations develop general predictors applied to the entire dataset, predicting IFT for any of the gas compositions involved. These developed equations substantially outperform published IFT empirical relationships. The class-focused equations outperform the generic equations, although both provide credible IFT prediction performance. The CFO equations outperform the CAFI equations. The best performing equation for Class III (2240 records) achieves IFT prediction performance of predictions (R2 = 0.9602; RMSE = 2.27 mN/M). The best performing general gas-brine IFT prediction equation (2649 records) achieves reliable IFT prediction performance (R2 = 0.7878; RMSE = 5.25 mN/M). These results justify the use of a physics-guided and chemistry-aware IFT prediction approach using the CAFI and CFO methods Текстовый файл AM_Agreement |
| Idioma: | anglès |
| Publicat: |
2026
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| Matèries: | |
| Accés en línia: | https://doi.org/10.1016/j.geoen.2026.214609 |
| Format: | Electrònic Capítol de llibre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=687177 |
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| 200 | 1 | |a Physics-informed and chemistry-aware framework to predict hydrogen-brine interfacial tension for underground hydrogen storage reservoir conditions |f Shadfar Davoodi, David A. Wood | |
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| 300 | |a Title screen | ||
| 320 | |a References: 51 tit | ||
| 330 | |a The distribution of hydrogen (H2) and cushion gas mixtures in underground hydrogen (gas) storage reservoirs (UHS) is partly controlled by capillary forces, which are determined by the gas-brine interfacial tension (IFT) in the pore space. Reliably and transparently predicting IFT from gas compositional and reservoir environmental conditions is useful for determining storage capabilities and gas-containment risks in UHS reservoirs. A 2649-data-record dataset is compiled for published laboratory tests of gas-brine mixtures at subsurface salinity, temperature, and pressure conditions relevant to UHS for predicting IFT. The gas compositions are divided into three classes: H2 only (Class I), binary gas combinations (H2+CO2, H2+CH4, or H2+N2; Class II), and the quaternary gas combination (H2+CO2+CH4+N2; Class III). A series of empirical IFT-prediction equations are developed based on the physics-guided, chemistry-aware-feature-influence (CAFI) method and the non-parametric, customized formulaic optimization (CFO) methods. Thirteen equations are tested on the data records of the three specific gas classes and compared with published IFT empirical equations. A further three equations develop general predictors applied to the entire dataset, predicting IFT for any of the gas compositions involved. These developed equations substantially outperform published IFT empirical relationships. The class-focused equations outperform the generic equations, although both provide credible IFT prediction performance. The CFO equations outperform the CAFI equations. The best performing equation for Class III (2240 records) achieves IFT prediction performance of predictions (R2 = 0.9602; RMSE = 2.27 mN/M). The best performing general gas-brine IFT prediction equation (2649 records) achieves reliable IFT prediction performance (R2 = 0.7878; RMSE = 5.25 mN/M). These results justify the use of a physics-guided and chemistry-aware IFT prediction approach using the CAFI and CFO methods | ||
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| 461 | 1 | |t Geoenergy Science and Engineering |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 266 |v Article number 214609, 23 p. |d 2026 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Multi-component gas mixtures | |
| 610 | 1 | |a Gas-brine interfacial tension | |
| 610 | 1 | |a Physics-guided empirical relationships | |
| 610 | 1 | |a Thermodynamic consistency | |
| 610 | 1 | |a Correlation-guided feature selection | |
| 700 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |9 22200 | |
| 701 | 1 | |a Wood |b D. A. |g David | |
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