Measurements and Modeling of Interfacial Tension for CO<sub>2</sub>/CH<sub>4</sub>/Brine Systems under Reservoir Conditions
Bibliographic record
Abstract
Supercritical CO 2 injection is a promising way to hydraulically fracture tight/shale gas formations as well as enhance gas recovery from these formations. Understanding of phase behavior and interfacial tension (IFT) of CO 2 /CH 4 /brine (NaCl) systems is important, because they affect the performance of such a process in tight/shale gas formations. In this study, we employ the axisymmetric drop shape analysis (ADSA) method to measure the IFT between CO 2 /CH 4 mixtures and brine over the temperature range from 77.0 to 257.0 °F and the pressure range from 15 to 5027 psia. Test results show that the presence of CO 2 decreases the IFT of CH 4 /H 2 O or CH 4 /brine (NaCl) systems, while the degree of reduction depends on the molar fraction of CO 2 in the gas mixture. Salinity tends to cause an increase in IFT of CO 2 /CH 4 /brine (NaCl) systems; a higher salinity leads to an increased IFT for a given system. On the basis of the Parachor model (Weinaug and Katz J. Ind. Eng. Chem. 1943, 35, 239) and Firoozabadi’s model (Firoozabadi and Ramey J. Can. Pet. Technol. 1988, 27, 41), we propose an improved IFT model to represent the measured IFT data for CO 2 /CH 4 /brine systems. The new IFT model preserves the principle of zero IFT at a critical point. Comparison of the new IFT model with four commonly used IFT correlations presented in the literature shows the superiority of the new model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".