Thixotropic Rheological Behavior of Maya Crude Oil
Bibliographic record
Abstract
Heavy oil and bitumen exhibit non-Newtonian rheological behaviors at lower temperatures. Thixotropy is one such behavior. Thixotropy affects the efficiency and length scale of mixing during blending operations and flow behaviors in pipes and pipelines following flow disruption, where it affects the pressure required to reinitiate flow. In the present work, thixotropic behaviors of Maya crude oil are explored systematically using a stress-controlled rheometer. Maya crude oil is shown to be a shear-thinning fluid below 313 K. The thixotropic behaviors are identified and explored using transient stress techniques (hysteresis loops, stepwise change in the shear rate, and startup experiments). The magnitude of the thixotropy effect is larger at lower temperatures. Relationships are identified between rest times and other thixotropic parameters, such as hysteresis loop area and stress decay, in startup experiments. Stress growth, which occurs as a result of a step-down in the shear rate, is shown to correlate with the temperature. The results also provide a benchmark data set for validation of rheological models for heavy oil that are immerging in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".