Bibliographic record
Abstract
ABSTRACT A Microbial Enhanced Oil Recovery (MEOR) Process was successfully applied in a mature waterflooded reservoir in Saskatchewan, Canada. A nutrient solution, which was designed specifically for this reservoir to stimulate certain indigenous microbes to grow, multiply and help to release oil was tested and piloted. A significant decrease in water cut and increase in oil production has been realized through the selective stimulation of bacteria using nutrient injection. The field is a mature waterflood averaging over 95% water cut. To combat the increasing water cut issue an In-Situ Microbial Response Analysis (ISMRA) was performed on a typical high water cut producer in the area. The test well was treated with a nutrient solution and then shut-in for a number of days to allow specific indigenous microbes to grow and multiply. Upon return to production the well produced at an average of 200% more oil with a 10% decrease in water cut for a year. Pretreatment rates averaged 1.2 m3/day oil and post-ISMRA treatment daily production peaked at 4.1 m3/day oil. As a result of the successful test, a pilot project was initiated and the nutrients were applied in three batch treatments on an injector with three offset production wells. Three weeks after the first batch treatment, a water cut decrease was seen at one of the offset producers. This well's oil production gradually increased from 1.4 to over 8 m3/day. Oil production in another producer doubled from 1.5 to over 3.0 m3/day. Subsequent treatments were tried on marginally economic wells and on a reactivated idle producer. The average decrease in water cut on these wells was over 10%. On the idle well oil production increased from 0.5 m3/day pre-treatment to an average of 3.0 m3/day post treatment. Throughout the world there remains a huge target for EOR processes to target. This successful MEOR application will have tremendous impact on ultimate recovery in many of these reservoirs not only through an increase in production, but a decrease in operating costs through associated reduction in lifting costs with less water production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".