Competitive, Microbially-Mediated Reduction of Nitrate with Sulfide and Aromatic Oil Components in a Low-Temperature, Western Canadian Oil Reservoir
Bibliographic record
Abstract
Fields from which oil is produced by injection of sulfate-bearing water often exhibit an increase in sulfide concentration with time (souring). Nitrate added to the injection water lowers the sulfide concentration by the action of sulfide-oxidizing, nitrate-reducing bacteria (SO-NRB). However, the injected nitrate can also be reduced with oil organics by heterotrophic NRB (hNRB). Aqueous volatile fatty acids (VFAs; a mixture of acetate, propionate, and butyrate) are considered important electron donors in this regard. Injection and produced waters from a western Canadian oil field with a low in situ reservoir temperature (30 degrees C) had only 0.1-0.2 mM VFAs. Amendment of these waters with nitrate gave therefore only partial reduction. More nitrate was reduced when 2% (v/v) oil was added, with light oil giving more reduction than heavy oil. GC-MS analysis of in vitro degraded oils and electron balance considerations indicated that toluene served as the primary electron donor for nitrate reduction. The differences in the extent of nitrate reduction were thus related to the toluene content of the light and heavy oil (30 and 5 mM, respectively). Reduction of nitrate with sulfide by SO-NRB always preceded that with oil organics by hNRB, even though microbially catalyzed kinetics with either electron donor were similar. Inhibition of hNRB by sulfide is responsible for this phenomenon. Injected nitrate will thus initially be reduced with sulfide through the action of SO-NRB. However, once sulfide has been eliminated from the near-injection wellbore region, oil organics will be targeted by the action of hNRB. Hence, despite the kinetic advantage of SO-NRB, the nitrate dose required to eliminate sulfide from a reservoir depends on the concentration of hNRB-degradable oil organics, with toluene being the most important in the field under study. Because the toluene concentration is lower in heavy oilthan in light oil, nitrate injection into a heavy-oil-producing field of low temperature is more likely to succeed in containing souring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".