Bibliographic record
Abstract
Abstract Since the accidental discovery in the late 1800s of the benefits of injecting water into oil reservoirs to improve recovery, water has been the injection fluid preferred by the oil industry for use in recovery processes. Traditionally, waterflooding is considered an effective secondary recovery method for light and medium oil reservoirs. However, as production rates in these conventional reservoirs continue their decline, waterflooding is being considered, and used, more and more for the exploitation of more challenging heavy oil resources. Despite the unfavorable mobility ratio between injected water and more viscous heavy oils, many waterflood projects have been undertaken in heavy oil reservoirs around the world. However, the literature on heavy oil waterflooding is sparse. Those papers that have been published present a wide range of recovery factors for projects, and offer conflicting information on the theory and mechanisms involved in heavy oil waterfloods. The good news is that there is a long history in western Canada with waterfloods in heavy oil reservoirs, spanning more than 50 years. A vast amount of data and anecdotal information has been generated from these waterfloods. This could provide a key to raising the limits, in terms of reservoir conditions, under which waterflooding would be viable. In this paper we will discuss lessons learned after 50 years of waterflooding in heavy oil reservoirs, identify gaps in the application of the process, and speculate about what the future may hold as this technology evolves.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".