Linking Sulfur Cycling and MIC in Offshore Water Transporting Pipelines
Bibliographic record
Abstract
Abstract Microbial activities in oil and gas operations cause souring, the production of sulfide by sulfate-reducing bacteria (SRB), and microbiologically-influenced corrosion (MIC). MIC may be especially severe in systems were several different types of fluids are mixed together, as this may provide a variety of nutrients for microbial growth. We have studied samples from an offshore production site and an onshore terminal for separation, crude oil storage, effluent treatment and disposal. We have investigated the samples using chemical analyses, culture-based microbial counts and molecular DNA-based techniques (pyrosequencing) to obtain whole microbial community composition. We found that (i) sulfate reduction by SRB (Desulfovibrio, Desulfobacterium, Desulfobacter) leads to the formation of sulfide, that (ii) sulfide is reoxidized to form elemental sulfur both abiotically and through the metabolism of sulfide-oxidizing bacteria (Sulfurimonas, Arcobacter) and that (iii) sulfur is converted back to sulfide by sulfur-reducing bacteria (S0RB, Desulfuromonas, Desulfuromusa), completing the sulfur cycle. Samples from these systems have significant sulfate, sulfide and sulfur (S8) concentrations and reactions (i) to (iii) can be demonstrated to occur. We find that the presence of elemental sulfur, which is increased by reactions (i) and (ii) and decreased by reaction (iii) gives rise to considerably increased corrosion risk towards steel infrastructure.
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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) | 0.001 | 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".