Inhibition of biogenic sulfide production through indigenous bacteria and amended nitrate in western Canadian heavy oil fields
Bibliographic record
Abstract
The presence of hydrogen sulphide (H{sub 2}S) in oil reservoirs is a major concern for the petroleum industry due to the toxic and corrosive nature of H{sub 2}S. This study investigated the biogenic production of H{sub 2}S in oil reservoirs subjected to water flooding. It involved stimulating indigenous nitrate-reducing bacteria (NRB) in western Canadian oil fields through nitrate amendment; investigating the competition growth of NRB and sulphate-reducing bacteria (SRB) under nitrate treatment in a synthetic brine of western Canada oil field; studying the effects of nitrate added at various concentrations on the inhibition of H{sub 2}S production inhibition; and, examining appropriate concentrations of nitrate added into the oil fields for H{sub 2}S control. In serum bottle microcosms, SRB and NRB that were isolated from a western Canadian oil field were utilized as inocula. Synthetic brine from the same oil field was then used as the growth substrate. Nitrate was amended into the microcosms. Competitive growth of SRB and NRB in the microcosms was monitored during 35 days of incubation. The changes in concentrations of sulfate, nitrate and sulfide, were also investigated. The addition of nitrate immediately inhibited SRB activity. The results suggest that there are two mechanisms involved in the inhibition of SRB activities through nitrate addition. The first mechanism is that nitrate could stimulate NRB which has an advantage in the competition for electron donors. The second mechanism is the increase of redox potential. The time length of sulfide inhibition is dependent on nitrate concentration. It was concluded that nitrate treatment is an effective and promising way to inhibit sulphide production in western Canadian oil fields where oil reservoirs have low porosity, low temperature, heavy density, complex geological conditions, high sulphate ratio, and high viscosity. 22 refs., 2 tabs., 7 figs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".