Microbial Methane Production Associated with Carbon Steel Corrosion in a Nigerian Oil Field
Bibliographic record
Abstract
Microbially influenced corrosion (MIC) in oil field pipeline systems can be attributed to many different types of hydrogenotrophic microorganisms including sulfate reducers, methanogens and acetogens. Samples from a low temperature oil reservoir in Nigeria were analyzed using DNA pyrotag sequencing. The microbial community compositions of these samples revealed an abundance of anaerobic methanogenic archaea. Activity of methanogens was demonstrated by incubating samples anaerobically in a basal salts medium, in the presence of carbon steel and carbon dioxide. Methane formation was measured in all enrichments and correlated with metal weight loss. Methanogens were prominently represented in pipeline solids samples, scraped from the inside of a pipeline, comprising over 85% of all pyrosequencing reads. Methane production was only witnessed when carbon steel beads were added to these pipeline solids samples, indicating that no methane was formed as a result of degradation of the oil organics present in these samples. These results were compared to those obtained for samples taken from a low temperature oil field in Canada, which had been incubated with oil, either in the presence or in the absence of carbon steel. Again, methanogens present in these samples catalyzed methane production only when carbon steel was present. Moreover, acetate production was also found in these enrichments only in the presence of carbon steel. From these studies it appears that carbon steel, not oil organics, was the predominant electron donor for acetate production and methane formation in these low temperature oil fields, indicating that the methanogens and acetogens found may contribute significantly to MIC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".