Comparison of microbial communities involved in souring and corrosion in offshore and onshore oil production facilities in Nigeria
Bibliographic record
Abstract
Samples were obtained from the Obigbo field, located onshore in the Niger delta, Nigeria, from which oil is produced by injection of low-sulfate groundwater, as well as from the offshore Bonga field from which oil is produced by injection of high-sulfate (2,200 ppm) seawater, amended with 45 ppm of calcium nitrate to limit reservoir souring. Despite low concentrations of sulfate (0-7 ppm) and nitrate (0 ppm), sulfate-reducing bacteria (SRB) and heterotrophic nitrate-reducing bacteria (NRB) were present in samples from the Obigbo field. Biologically active deposits (BADs), scraped from corrosion-failed sections of a water- and of an oil-transporting pipeline (both Obigbo), had high counts of SRB and high sulfate and ferrous iron concentrations. Analysis of microbial community composition by pyrosequencing indicated anaerobic, methanogenic hydrocarbon degradation to be a dominant process in all samples from the Obigbo field, including the BADs. Samples from the Bonga field also had significant activity of SRB, as well as of heterotrophic and of sulfide-oxidizing NRB. Microbial community analysis indicated high proportions of potentially thermophilic NRB and near-absence of microbes active in methanogenic hydrocarbon degradation. Anaerobic incubation of Bonga samples with steel coupons gave moderate general corrosion rates of 0.045-0.049 mm/year, whereas near-zero general corrosion rates (0.001-0.002 mm/year) were observed with Obigbo water samples. Hence, methanogens may contribute to corrosion at Obigbo, but the low general corrosion rates cannot explain the reasons for pipeline failures in the Niger delta. A focus of future work should be on understanding the role of BADs in enhancing under-deposit pitting corrosion.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".