Oil Field Souring Control by Nitrate-Reducing <i>Sulfurospirillum</i> spp. That Outcompete Sulfate-Reducing Bacteria for Organic Electron Donors
Bibliographic record
Abstract
Nitrate injection into oil reservoirs can prevent and remediate souring, the production of hydrogen sulfide by sulfate-reducing bacteria (SRB). Nitrate stimulates nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB) and heterotrophic nitrate-reducing bacteria (hNRB) that compete with SRB for degradable oil organics. Up-flow, packed-bed bioreactors inoculated with water produced from an oil field and injected with lactate, sulfate, and nitrate served as sources for isolating several NRB, including Sulfurospirillum and Thauera spp. The former coupled reduction of nitrate to nitrite and ammonia with oxidation of either lactate (hNRB activity) or sulfide (NR-SOB activity). Souring control in a bioreactor receiving 12.5 mM lactate and 6, 2, 0.75, or 0.013 mM sulfate always required injection of 10 mM nitrate, irrespective of the sulfate concentration. Community analysis revealed that at all but the lowest sulfate concentration (0.013 mM), significant SRB were present. At 0.013 mM sulfate, direct hNRB-mediated oxidation of lactate by nitrate appeared to be the dominant mechanism. The absence of significant SRB indicated that sulfur cycling does not occur at such low sulfate concentrations. The metabolically versatile Sulfurospirillum spp. were dominant when nitrate was present in the bioreactor. Analysis of cocultures of Desulfovibrio sp. strain Lac3, Lac6, or Lac15 and Sulfurospirillum sp. strain KW indicated its hNRB activity and ability to produce inhibitory concentrations of nitrite to be key factors for it to successfully outcompete oil field SRB.
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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 | 0.000 | 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 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".