When Metals and Microbes Meet: Preventing Microbial Corrosion in Crude Oil Transmission Pipelines
Bibliographic record
Abstract
Abstract Corrosion of carbon steel infrastructure in the oil and gas industry can occur via a variety of chemical, physical, and/or microbiological mechanisms. Although microbial corrosion is known to lead to infrastructure failure in many upstream and downstream operations, predicting when and how microorganisms attack metal surfaces remains a challenge. In crude oil transmission pipelines, a kind of aggressive corrosion known as under deposit corrosion (UDC) can occur, wherein mixtures of solids (sands, clays, inorganic minerals), water, oily hydrocarbons, and microorganisms form discreet, (bio)corrosive sludges on the metal surface. To prevent UDC, operators will use physical cleaning methods (e.g., pigging) combined with chemical treatments such as biocides, corrosion inhibitors, and/or biodispersants. As such, it necessary to evaluate the efficacy of these treatments in preventing UDC by monitoring the sludge characteristics and the microorganisms that are potentially involved in the corrosion process. The efficacies of a biocide, corrosion inhibitor, and biodispersant being used to prevent microbial corrosion in a crude oil transmission pipeline were evaluated. A combination of various microbiological analyses and corrosivity tests were performed using sludge samples collected during pigging operations. The results indicated that the combined treatment using inhibitor, biocide 1 and biodispersant was the most effective in preventing metal damage, and both growth-based and Next-Generation Sequencing approaches provided value towards understanding the effects of the chemical treatments. The efficacy of a different biocide (#2) could be discriminated using these test methods. The results of this study demonstrate the importance of considering and monitoring for microbial corrosion of crucial metal infrastructure in the oil and gas industry, and the value of combining multiple lines of evidence to evaluate the performance of different chemical treatment scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".