Long Term Evaluation of Microbial Induced Corrosion Contribution to Underdeposit Sludge Corrosivity in a Heavy Crude Oil Pipeline
Bibliographic record
Abstract
Abstract Internal corrosion has been observed in crude oil pipelines (<0.5% sediment and water(S&W)) at locations that facilitate the deposition and accumulation of entrained solids. The resulting sludges that form are composed of varying combination of hydrocarbons, sand, clay, corrosion by-products, biomass and water. The sludges are known to support robust microbial communities and these are suspected of contributing to the overall corrosivity of the sludge. This paper reports on the results of work done to evaluate the overall contribution of microbial induced corrosion (MIC) on the corrosion rates of steel coupons covered with a pipeline sludge extracted from a pigging operation. The sludge was applied to the coupons in both the as-received and sterilized condition and the coupons were extracted at 30 day intervals for 180 days. The results show that despite its high water content the corrosivity of the sludge was low (<20 μm/yr) and that weight loss after the initial 30 days was negligible. The activities of heterotrophic aerobic bacteria (HAB), acid producing bacteria (APB), and sulfate reducing bacteria (SRB) in the as-received sludge decreased during the experiment by 2, 1.5 and 1 order of magnitude, respectively. Overall there was no detectable contribution of MIC to the corrosion rates of the coupons during the experiment.
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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 | 0.000 | 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".