Impact of Nitrate-Mediated Microbial Control of Souring in Oil Reservoirs on the Extent of Corrosion
Bibliographic record
Abstract
The effect of microbial control of souring on the extent of corrosion was studied in a model system consisting of pure cultures of the nitrate-reducing, sulfide-oxidizing bacterium (NR-SOB) Thiomicrospira sp. strain CVO and the sulfate-reducing bacterium (SRB) Desulfovibrio sp. strain Lac6, as well as in an SRB consortium enriched from produced water from a Canadian oil reservoir. The average corrosion rate induced by the SRB consortium (1.4 g x m(-2) x day(-1)) was faster than that observed in the presence of strain Lac6 (0.2 g x m(-2) x day(-1)). Examination of the metallic coupons at the end of the tests indicated a uniform corrosion in both cases. Addition of CVO and 10 mM nitrate to a fully grown culture of Lac6 or the SRB consortium led to complete removal of sulfide from the system and a significant increase in the population of CVO, as determined by reverse sample genome probing. In the case of the SRB consortium addition of just nitrate (10 mM) had a similar effect. When grown in the absence of nitrate, the consortium was dominated by Desulfovibrio sp. strains Lac15 and Lac29, while growth in the presence of nitrate led to dominance of Desulfovibrio sp. strain Lac3. The addition of CVO and nitrate to the Lac6 culture or nitrate to the SRB consortium accelerated the average corrosion rate to 1.5 and 2.9 g x m(-2) x day(-1), respectively. Localized corrosion and the occurrence of pitting were apparent in both cases. Although the sulfide concentration (0.5-7 mM) had little effect on corrosion rates, a clear increase of the corrosion rate with increasing nitrate concentration was observed in experiments conducted with consortia enriched from produced water.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".