Theoretical and experimental investigation of effect of salinity and asphaltene on IFT of brine and live oil samples
Bibliographic record
Abstract
Abstract Several factors influence the IFT of oil and formation water. These factors are rooted in the complex composition of oil, presence of different salts in water, water salinity, temperature, and pressure of reservoir. In the first part of this paper, effect of salinity on IFT between brine and an Iranian live oil sample has been studied experimentally. It is observed that IFT increases almost linearly with brine concentration. Also, linear increasing behavior of IFT with respect to pressure is obviously seen. Then, using thermodynamic properties such as surface excess concentration, chemical potential, chemical activity, and activity coefficient, results were analyzed and observed effect of salinity and pressure were justified thermodynamically. In the second part, the effect of asphaltene on IFT reduction has been studied. In previous works, the investigators extracted resin and asphaltene and then examined their effects on IFT in the absence of other fractions of oil phase. We believe that all fractions play a role in this phenomenon so, in this paper, the effect of natural surfactants of oil phase on IFT has been investigated in presence of all fractions of oil. Hence, SARA test was performed on all samples. Then, IFT between oil samples and brine were measured using captive drop instrument at 25 °C and 3000 psia. Results showed that neither asphaltene content nor asphaltene/resin ratio is a good indicator for effect of asphaltene on IFT, whereas colloidal instability index could be a useful tool to predict asphaltene effect on IFT.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".