Bibliographic record
Abstract
Summary 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 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 have been realized through the selective stimulation of bacteria using nutrient injection. The field is a mature waterflood averaging more than 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 was shut in for a number of days to allow 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/d of oil (8 BOPD) and post-ISMRA treatment daily production peaked at 4.1 m3/d of oil (26 BOPD). The ISMRA provides a direct support of laboratory studies and frequently increases oil production. As a result of the successful ISMRA, 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 more than 8 m3/d (9 to 50 B/D). Oil production in another producer doubled from 1.5 to more than 3.0 m3/d (9 to 19 B/D). Subsequent treatments were tried on marginally economic wells and on a reactivated idle producer. The average decrease in water cut in these wells was more than 10%. On the idle well, oil production increased from 0.5 m3/d (3 B/D) pretreatment to an average of 3.0 m3/d (19 B/D) post-treatment. Throughout the world, there remains a huge target for enhanced-oil-recovery (EOR) processes to target (Bryant 1991). This successful MEOR application will have a 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".