4. Correlating the Chemical and Physical Properties of a Set of Heavy Oils from around the World
Bibliographic record
Abstract
Introduction Heavy oil has recently become an important resource as conventional oil reservoirs have limited production and oil prices rise. More than 6 trillion barrels of oil in place have been attributed to the world's heaviest hydrocarbons (Curtis et al., 2002). Therefore, heavy-oil reserves account for more than 3 times the amount of combined world reserves of conventional oil and gas. Of particular interest are the large heavy-oil deposits of Canada and Venezuela, which together may account for approximately 55%–65% of the known less than 20° American Petroleum Institute (API) gravity oil deposits in the world (Curtis et al., 2002). Heavy oils cover a large range of API gravities, from 22° for the lightest heavy oils to less than 10° for extra-heavy oils. This wide range of values means that heavy oils vary greatly in their physical properties. Thus, extensive research is required before the properties of heavy oil can be properly understood. Several prevailing issues are seen repeatedly in various fields around the world, including how to make measurements on unconsolidated sandstone cores, production of sand with oil and its effect on formation, exsolution gas drive of heavy oil, understanding the control of viscosity and other physical properties of heavy oils, and monitoring of steam recovery processes. Simply, the high viscosity of heavy oils limits its extraction by traditional methods.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".