An Experimental Investigation of the Effect of Pressure and Salinity on IFT in Live Oil/Brine Systems
Bibliographic record
Abstract
Residual oil saturation in reservoirs is primarily influenced by viscous and capillary forces, with interfacial tension (IFT) being a critical factor in fluid distribution due to capillary pressure. Adjusting IFT is essential for enhancing oil recovery, particularly in waterflooding, which is the most common secondary recovery technique after primary production. The salinity of injected water directly affects the IFT between crude oil and brine, making it a crucial factor in optimizing recovery. However, limited studies have examined IFT using live oil samples under actual reservoir conditions. In this study, a high-pressure, high-temperature (HPHT) drop shape analyzer was used to measure the IFT between live oil and brine under reservoir conditions. Five live oil samples and two sodium chloride (NaCl) brine concentrations (30,000 and 100,000 ppm) were tested at a reservoir temperature of 180 °F. Measurements were conducted above the bubble points of the oils, replicating undersaturated reservoir conditions. The results revealed that the impact of pressure on IFT was more complex than that of salinity. IFT generally decreased with increasing pressure but showed mixed behavior across different samples. Conversely, IFT consistently increased with higher salinity. These findings enhance the understanding of IFT behavior under reservoir conditions, supporting improved reservoir simulations and oil recovery strategies.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".