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Underground hydrogen storage (UHS) in geological formations is expected to play a critical role as a net-zero energy strategy in the coming decades as the global energy mix shifts toward cleaner, renewable resources. A thorough understanding of the interactions between hydrogen and fluids in subsurface formations is essential for industrial-scale storage. Specifically, the interfacial tension (IFT) of the hydrogen–brine system is a fundamental property that controls hydrogen storage capacity, flow behavior, and saturation distribution in subsurface porous formations such as depleted hydrocarbon reservoirs and deep saline aquifers. While laboratory measurements of IFT are necessary for assessing sealing capacities, they are often costly, time-consuming, and present safety concerns regarding flammability and high-pressure, high-temperature testing. Consequently, developing intelligent prediction models is an effective alternative for optimizing hydrogen geo-storage procedures.
This research work introduced the first predictive mathematical model developed using Gene Expression Programming (GEP) to accurately estimate hydrogen–brine IFT. The model was developed using a dataset of 159 experimental data points collected from various literature sources, covering a wide range of geological conditions. The input variables for the model included pressure (0.10 to 45.20 MPa), temperature (293.15 to 448.35 K), salinity (0 to 4.95 mol/kg), and the density difference between the gas and fluid phases (890.40 to 1166.60 kg/m³). To ensure the reliability of the GEP algorithm, which utilizes tree structures and evolutionary computation inspired by natural selection, the database was randomly divided into a training set (80%) and a testing set (20%). The GEP configuration utilized 100 chromosomes and twelve genes, with a head size of 10 and 10 constants per gene.
Statistical and graphical analyses demonstrated that the proposed GEP model is highly accurate in predicting IFT under various geological conditions. The model achieved best-reported performance metrics of R² = 0.981, RMSE = 1.33, AARE = 1.75%, and MAE = 1.11. Graphical comparisons showed that the predicted values align satisfactorily with the 45° cross-line, and the relative error distribution indicated high density near the zero-error line.
The developed correlation provides a reliable and cost-effective tool for the assessment of hydrogen storage capacity and multiphase flow in reservoir conditions. By considering four critical parameters simultaneously for the first time, this model reduces the risks associated with storing hydrogen in porous formations and facilitates the appropriate design of UHS operations.
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| Country | Kazakhstan |
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