Research on viscoelastic properties of water in waxy crude oil emulsion gels with the effect of droplet size and distribution
Bibliographic record
Abstract
Abstract The viscoelastic properties of gelled oil can reflect its structural characteristics, which are significant for the restart operation of prolonged‐shutdown pipelines. The viscoelastic properties of water‐in‐oil (W/O) emulsions are more complicated on account of the influence from the dispersed water phase, which cannot be ignored. This paper attempts to reveal the relationship of viscoelastic characteristics to temperature and water fraction for waxy crude emulsion gels from a micro perspective. As a basis of the rheological analysis, the droplet size and distribution of inner phase are studied with microscopic observation. It is found that both the total number and Sauter mean diameter of dispersed droplets increase with growth of the water fraction. Through a series of small amplitude oscillatory shear experiments, the linear viscoelastic regions and the viscoelastic parameters of emulsion gels with different water fractions are obtained and compared under varying temperatures. The results provide evidence that as the water fraction rises, the structural strength of emulsion gels becomes stronger. This can mainly be attributed to the larger deformable oil‐water interface area with adsorbed wax crystals and the stronger interactions among water droplets. Based on the experimental results, regression formulas for viscoelastic parameters, including elastic modulus, loss modulus, and loss angle, are proposed and proven reliable. This offers useful information for the restart operation of oil‐water multiphase transportation pipelines.
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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) | 0.000 | 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 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".