Implications of Limited Thermophilicity of Nitrite Reduction for Control of Sulfide Production in Oil Reservoirs
Bibliographic record
Abstract
UNLABELLED: Nitrate reduction to nitrite in oil fields appears to be more thermophilic than the subsequent reduction of nitrite. Concentrated microbial consortia from oil fields reduced both nitrate and nitrite at 40 and 45°C but only nitrate at and above 50°C. The abundance of the nirS gene correlated with mesophilic nitrite reduction activity. Thauera and Pseudomonas were the dominant mesophilic nitrate-reducing bacteria (mNRB), whereas Petrobacter and Geobacillus were the dominant thermophilic NRB (tNRB) in these consortia. The mNRB Thauera sp. strain TK001, isolated in this study, reduced nitrate and nitrite at 40 and 45°C but not at 50°C, whereas the tNRB Petrobacter sp. strain TK002 and Geobacillus sp. strain TK003 reduced nitrate to nitrite but did not reduce nitrite further from 50 to 70°C. Testing of 12 deposited pure cultures of tNRB with 4 electron donors indicated reduction of nitrate in 40 of 48 and reduction of nitrite in only 9 of 48 incubations. Nitrate is injected into high-temperature oil fields to prevent sulfide formation (souring) by sulfate-reducing bacteria (SRB), which are strongly inhibited by nitrite. Injection of cold seawater to produce oil creates mesothermic zones. Our results suggest that preventing the temperature of these zones from dropping below 50°C will limit the reduction of nitrite, allowing more effective souring control. IMPORTANCE: Nitrite can accumulate at temperatures of 50 to 70°C, because nitrate reduction extends to higher temperatures than the subsequent reduction of nitrite. This is important for understanding the fundamentals of thermophilicity and for the control of souring in oil fields catalyzed by SRB, which are strongly inhibited by nitrite.
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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) | 0.000 | 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 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".