Successful Testing of Toe-To-Heel Waterflooding in Medicine Hat Glauconitic C Reservoir
Bibliographic record
Abstract
Abstract The Medicine Hat Glauconitic C reservoir contains medium heavy oil, and was put on production in 1985. In 1993, a waterflood pilot was initiated; in 1996, several horizontal wells were drilled as producers, and they resulted in a spectacular increase in oil production. In 2001 a conventional field-wide waterflood was implemented. The waterflood included seven Toe-To-Heel Waterflooding (TTHW) modules involving 10 injectors and 18 horizontal producers, out of a total of 52 injectors and 100 producers. Five of these modules that utilized vertical wells as injectors performed well; oil rate increased 2 to 11 times, as increases in oil production continued for several years, or stabilized. During the same period, the performance of conventional waterflood modules was characterized by reductions in decline of oil production trends. Two TTHW modules that used horizontal injectors performed similar to conventional waterflood modules. The best performance was shown by Module # 5 (involving two horizontal producers and two vertical injectors), located in the best quality reservoir. Compared to conventional waterflood pilot area nearby, the performance of Module #5 was superior, with oil production rates increasing 7-11-folds, while in the pilot area this was only 2-3 times; the water injected/oil produced ratio was 12–15 m3/m3 for Module #5, while it was 24–44 m3/m3 for the pilot area. The incremental oil recovery due to waterflooding from the TTH module # 5 was estimated at about 7%, and 5% from a nearby conventional waterflood area. A statistical analysis for the whole field revealed that TTH horizontal wells produced at oil rates 30% higher than the non-TTH configuration horizontal producers.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".