A Reservoir Management Case Study of a Polymer Flood Pilot in Medicine Hat Glauconitic C Pool
Bibliographic record
Abstract
Abstract The Medicine Hat Glauconitic C Pool is located partially inside the city limits of Medicine Hat, Alberta, Canada. This 15-18° API oil pool is under waterflood. Reservoir management strategies in the pool are limited by the reservoir quality, wellbore architecture and business environment. Surface land access due to the expanding Medicine Hat city limits and pre-existing wellbore orientations have constrained the waterflood pattern and infill drilling options. Current estimates indicate 21% of the oil in place will be recovered at the end of the waterflood. The polymer flood pilot was started in 2012 with a goal to achieve 6% incremental recovery with a one year initial response time. The pilot's designed injection rate was 6,300 bbl/d (1000 m3/d) in 5 injectors, each representing different reservoir conditions and injection patterns. Following injection, pre-existing water channels and high permeability streaks resulted in rapid polymer breakthrough in as little as 48 hours in some cases. To increase the efficiency of the polymer flood and reduce cycling polymerized water, a new approach to reservoir management had to be considered: – Each of the 5 patterns inside the pilot was monitored and managed individually, with a fit-for-purpose strategy in mind. – Although 1 year to response was estimated, after four months, oil rates and oil cuts increased unexpectedly. However, after reaching peak production, the expected steep decline was observed. – New reservoir management strategies were implemented in order to slow the decline and attempt to increase the incremental recovery from 6% to 10%: attempts to block water channels in the injectors, producer shut-ins and frequent injection target changes proved beneficial. The polymer pilot has exceeded expectations to date. This case study provides an outline of the changes that were made and the results that have been observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".