Machine Learning-Based Interfacial Tension Equations for (H<sub>2</sub> + CO<sub>2</sub>)-Water/Brine Systems over a Wide Range of Temperature and Pressure
Bibliographic record
Abstract
Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H 2 -brine interfacial tension (IFT) is a crucial parameter in structural trapping in underground geological locations and gas–water two-phase flow in subsurface porous media. On the other hand, cushion gas, such as CO 2, is often co-injected with H 2 to retain reservoir pressure. Therefore, it is imperative to accurately predict the (H 2 + CO 2 )-water/brine IFT under UHS conditions. While there have been a number of experimental measurements on H 2 -water/brine and (H 2 + CO 2 )-water/brine IFT, an accurate and efficient (H 2 + CO 2 )-water/brine IFT model under UHS conditions is still lacking. In this work, we use molecular dynamics (MD) simulations to generate an extensive (H 2 + CO 2 )-water/brine IFT databank (840 data points) over a wide range of temperature (from 298 to 373 K), pressure (from 50 to 400 bar), gas composition, and brine salinity (up to 3.15 mol/kg) for typical UHS conditions, which is used to develop an accurate and efficient machine learning (ML)-based IFT equation. Our ML-based IFT equation is validated by comparing to available experimental data and other IFT equations for various systems (H 2 -brine/water, CO 2 -brine/water, and (H 2 + CO 2 )-brine/water), rendering generally good performance (with R 2 = 0.902 against 601 experimental data points). The developed ML-based IFT equation can be readily applied and implemented in reservoir simulations and other UHS applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".