Control of Souring through a Novel Class of Bacteria That Oxidize Sulfide as Well as Oil Organics with Nitrate
Bibliographic record
Abstract
Abstract Hydrogen sulfide production by sulfate-reducing bacteria (SRB) in oil fields (souring) can be eliminated through the activity of nitrate-reducing bacteria (NRB). Two distinct classes of NRB have been described. The heterotrophic NRB (hNRB) reduce nitrate using similar oil organics as used by SRB for the reduction of sulfate. These inhibit SRB by competitive exclusion. The nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB) directly oxidize sulfide with nitrate. Many of these use only CO2 as the carbon source and do not compete with SRB for oil organics. Both hNRB and NR-SOB produce nitrite as an intermediate in nitrate reduction, which strongly inhibits SRB. The NR-SOB Thiomicrospira sp. strain CVO has previously been shown to be an effective agent for sulfide removal in situ and in laboratory studies. A continuous up-flow packed-bed bioreactor was inoculated with a microbial consortium obtained from the same oil field. Although SRB-generated sulfide was removed by nitrate addition, community analysis indicated that strain CVO did not become a major component under these conditions. Also, strain CVO could not be established in the bioreactor by bioaugmentation. Two related microorganisms Sulfurospirillum sp. strains NO2B and KW became major community members during nitrate treatment. These were found to have both hNRB and NR-SOB activity and be capable of producing large amounts of nitrite, make them ideal agents for souring control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".