Viscosity and other rheological properties of bitumen from the Upper Devonian Grosmont reservoir, Alberta, Canada
Bibliographic record
Abstract
Abstract Viscosity is directly related to the mobility and quality of hydrocarbons in reservoirs, and its distribution is commonly heterogeneous. Therefore, viscosity has a great impact on the exploitation of heavy oil and bitumen reservoirs. Previous studies showed that viscosity measurements are problematic and are often inconsistent mainly because of the challenges of sample preparation and lack of a standard procedure for measuring viscosity in the oil and gas industry. This study aims to improve the characterization of reservoir viscosity by understanding the rheological properties of bitumen using bitumen samples from the Upper Devonian Grosmont reservoir, Alberta, Canada. This study shows that Grosmont bitumen is essentially a non-Newtonian fluid, exhibiting a distinctive shear-thinning behavior at low temperatures of less than 40°C. With increasing temperature, however, the bitumen changes from a non-Newtonian fluid to a Newtonian fluid. At low temperatures, the viscosity variations can be divided into four stages. A standard viscosity called “zero-shear viscosity” is obtained from the viscosity variations and can be used throughout the reservoir as characteristic viscosity for Grosmont bitumen. The viscosity distribution in the Grosmont reservoir is complex and appears to be stratigraphically related. Bitumen samples from formation boundaries tend to have higher viscosities, suggesting more severe biodegradation at these locations. Possible causes for the observed patterns in the Grosmont include (1) oil-water contacts migrating up and down over time; (2) oil migration and/or biodegradation controlled by aquitards that divide the reservoir into hydrostratigraphically separated units; and (3) differences in microbial activity, that is, aerobic versus anaerobic, possibly controlled by the level of oxygenation over time.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".