19. Effects of Heavy-Oil Cold Production on VP / VS Ratio
Bibliographic record
Abstract
Introduction Heavy-oil reservoirs are an abundant resource, particularly in Canada, Venezuela, and Alaska. By some estimates, heavy oils represent as much as 6.3 trillion barrels of oil in place. This matches available quantities of conventional oil. More than 50% of Canada's oil production is now from heavy oil (Batzle et al., 2006). Much of the heavy-oil recovery in Western Canada involves steam injection, called “hot production.” An alternative to thermal heavy-oil production in the field is known as “cold production,” which is a primary nonthermal process in which reservoir temperature is not affected. The cold production process has been economically successful in several unconsolidated heavy-oil fields in Alberta and Saskatchewan, Canada (Sawatzky et al., 2002). During the cold production process, sand and oil are produced simultaneously by progressive cavity pumps, generating high-porosity channels termed “wormholes.” The development of wormholes causes reservoir pressure to fall below the bubble point, resulting in dissolved gas coming out of solution to form foamy oil. Foamy oil and wormholes are believed to be two key factors in the enhancement of oil recovery (Metwally et al., 1995; Maini, 2004). The development of wormholes and the formation of foamy oil will disturb fluid properties in the reservoir during heavy-oil cold production. Batzle et al. (2006) showed that the bulk modulus of heavy oil drops to near zero very quickly from approximately 2.6 GPa after pressure is lower than the bubble point line at approximately 2 MPa. This disturbance will probably be detectable for seismic survey.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".