Bibliographic record
Abstract
Abstract A statistical study of 166 western Canadian waterfloods recovering heavy and medium gravity oils revealed new findings about best operating practices for heavy oil waterflooding. In classical light oil waterflooding, operators are advised to start waterflooding early and maintain the voidage replacement ratio (VRR) at 1. The study, however, produced surprising results for 2 parameters − among the 120 reservoir and operating parameters investigated − that ran counter to the recommended practices of classical light waterflooding. Delaying the start of waterflooding until a certain fraction of the original oil in place was recovered was found to be beneficial. Secondly, varying the VRR was shown to correlate with increased ultimate recovery — periods of underinjection are needed, although a cumulative VRR of 1 should be maintained. Ultimate recovery was correlated with the primary recovery factor at the start of the waterflood. No trends appeared when the full set of 166 waterfloods was inspected. However, when the dataset is analyzed by ranges of API, a "sweet spot" of improved ultimate recovery was observed in a very narrow window of oil recovery factor prior to the start of waterflooding. Graphs of each category showed this "sweet spot" window where improved recovery occurred. These categories were API ranges; as well as ranges of permeability*height/viscosity (kh/μ); and pattern development. Also increases in ultimate recovery were observable when we examined graphs of ultimate recovery versus the fraction of injection volume that was underinjected — but again, only when the data was analyzed by the ranges. A certain period of injection when the VRR was less than 0.95 resulted in increased ultimate recoveries. However, it is important that this period of VRR < 0.95 be offset with periods of increased VRR so that the cumulative VRR cycles around 1.0. Again, each range manifested a narrow "sweet spot" for where this increase in ultimate recovery occurred.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".