Sulfide persistence in oil field waters amended with nitrate and acetate
Bibliographic record
Abstract
Nitrate amendment is normally an effective method for sulfide control in oil field-produced waters. However, this approach has occasionally failed to prevent sulfide accumulation, despite the presence of active nitrate-reducing bacterial populations. Here, we report our study of bulk chemical transformations in microcosms of oil field waters containing nitrate-reducing, sulfide-oxidizing bacteria, but lacking denitrifying heterotrophs. Amendment with combinations of nitrate, acetate, and phosphate altered the microbial sulfur and nitrogen transformations. Elemental sulfur produced by chemotrophic nitrate-reducing bacteria was re-reduced heterotrophically to sulfide. Ammonification, rather than denitrification, was the predominant pathway for nitrate reduction. The application of nitrite led to transient sulfide depletion, possibly due to higher rates of nitrite reduction. The addition of molybdate suppressed both the accumulation of sulfide and the heterotrophic reduction of nitrate. Therefore, sulfidogenesis was likely due to elemental sulfur-reducing heterotrophic bacteria, and the nitrate-reducing microbial community consisted mainly of facultatively chemotrophic microbes. This study describes one set of conditions for continued sulfidogenesis during nitrate reduction, with important implications for nitrate control of sulfide production in oil fields.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".