Sulfur Production Associated with Souring Control by Nitrate Injection: a Potential Corrosion Risk?
Bibliographic record
Abstract
Abstract Injection of nitrate into an oil field can significantly reduce the concentration of sulfide produced by endogenous sulfate-reducing bacteria (SRB). Although much is known of the effects of nitrate in relatively high temperature reservoirs (60-80 °C), flooded with seawater, its effectiveness in lower temperature reservoirs (30-40 °C) subjected to produced water reinjection (PWRI) is less well understood. The nitrate-reducing, sulfide-oxidizing bacterium (NR-SOB) Thiomicrospira sp. strain CVO, was isolated from such a reservoir. This organism converts sulfide and nitrate into sulfate and nitrite or into sulfur and nitrogen, depending on whether the initial nitrate to sulfide (N/S) ratio is high or low, respectively. The presence of iron minerals in reservoir rock (e.g. siderite FeCO3) can delay the onset of souring by immobilizing SRB-produced sulfide as FeS (FeCO3+HS−→FeS+HCO3−). Strain CVO appeared incapable of oxidizing ferrous sulfide (FeS) with nitrate, indicating that it does not mobilize precipitated sulfides. Interestingly, in ferrous iron-containing cocultures of sulfate-reducing Desulfovibrio spp. and strain CVO, FeS was transformed into greigite (Fe3S4). The mechanism for greigite formation is probably reaction of FeS with CVO-produced sulfur (3FeS + S0 → Fe3S4). Hence, on the plus side CVO-mediated conversion of FeS to greigite increases the sulfur-binding capacity of reservoir rock by 33%. On the downside, sulfur is corrosive towards iron increasing corrosion risk if it emerges in production wells. A reservoir model, taking some of these features into account and indicating the formation of sulfur as a function of space and time by the combined action of SRB and NR-SOB such as strain CVO, is presented
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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".