Characterization of Microbiologically Influenced Corrosion Potential in Nitrate Injected Produced Waters
Bibliographic record
Abstract
Abstract Microorganisms are notorious for being involved in serious metal infrastructure damage, popularly known as microbiologically influenced corrosion (MIC). Long term corrosion incubations (~2 years) with carbon steel (CS) beads were established using produced water collected from a Canadian oilfield where nitrate was routinely used for souring mitigation. Experiments were set up under methanogenic, sulfate-reducing, and nitrate-reducing conditions to stimulate electrical MIC (EMIC) with iron present as the sole electron donor. Microbial community analysis, chemical measurements, metal weight loss, and surface analyses were conducted to assess EMIC under these different conditions. After 2 years, incubations in the nitrate-reducing environment did not show surface damage to the CS beads nor substantial weight loss (2-3%). However, incubations in the sulfate-reducing environment showed 5-8% weight loss with severe pitting on the CS beads and community sequencing revealed the predominance of known acid producers (Mesotoga, Acetobacterium), methanogens (Methanosaeta), and sulfate-reducers (Desulfovibrio, Desulfobulbus). Incubations in the methanogenic environment also showed comparatively less weight loss (2%) though surface analysis revealed an abundance of pinhole-like pits; microbial communities were dominated by putative syntrophs (Petrimonas, Pseudomonas, Desulfovibrio) and known methanogens (Methanosaeta). In addition to the establishment of promising new EMIC enrichment cultures, this study demonstrated that the localized effect of MIC cannot be accurately assessed solely using weight loss corrosion assays, but additionally requires microscopic and/or surface studies along with an understanding of the microbial community composition.
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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 | 0.001 | 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".