Case Study of the Mannville B ASP Flood
Bibliographic record
Abstract
Abstract In May 2006, the Warner Mannville B ASP flood was the first field wide ASP flood implemented in Canada. The objective was to successfully implement a commercially viable ASP flood. In this project produced water is treated and reinjected into the reservoir. As of December 2012, an incremental 420 103m3 (2.65 million bbl) of oil has been recovered with an expected total incremental recovery of 777 103m3 (4.89 million bbl), which represents 11.1% of the OOIP. In October 2008, after 0.35 pore volume of ASP injection, the project moved into the Polymer only injection phase. Polymer injection will continue as long as is economically feasible. A comprehensive monitoring and testing program was implemented to evaluate flood response and performance. This allowed for the optimization of the flood through continuous adjustment to flow rates and led to successful infill drilling locations. Many challenges have been encountered during this project, including: silicate scale production, treating issues related to the water quality of the recycled injection water, and loss of injectivity in many injection wells. The challenges were overcome and it has been an economic success with a cumulative positive cashflow within 5 years. The results of this flood have led to the implementation of an additional four floods. The lessons learned from this project have improved numerous aspects of how future floods are designed and implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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".