Effect of a new nano‐viscosity reducing agent on the rheological properties and wax deposition characteristics of waxy crude oil
Bibliographic record
Abstract
Abstract Wax inhibitors are widely applied during the process of waxy crude oil production, storage, and transportation. However, their effects on the crude oil rheological properties and wax deposition characteristics are still not well understood. Herein, a new nano‐viscosity reducing agent was chosen to investigate its influences on the physical properties and the wax deposition process of Liaohe crude oil in a cold finger experimental device. Firstly, the rheological property variations of crude oil with different dosages of reagent added were tested. Furthermore, the changes of wax deposit morphology, wax deposition rate, and wax precipitation characteristics were studied, and the influencing mechanism was elucidated. It was found that the new nano‐viscosity reducing agent can significantly alter the crude oil rheology properties and wax deposition process. The gel point, wax appearance temperature (WAT), and viscosity all decreased after addition of the reagent. With the dosage of the new reagent increasing from 0 to 120 mg/kg, the wax deposition rate decreased greatly, but the wax content and WAT of the deposits increased obviously. Further, the wax content and WAT are larger in the bottom layer. The wax particles in the bottom layer present smaller size and larger size for the blank crude oil and additive crude oil, respectively, which are caused by the variation of physical properties and molecular diffusion process. This research provides valuable information for the action mechanism of the new nano‐viscosity reducer on Liaohe crude oil, which can guide the optimization of the oil transmission process.
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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".