Understanding and Mitigating Under-Deposit Corrosion in Large Diameter Crude Oil Pipelines: A Progress Report
Bibliographic record
Abstract
This paper is an exploration of factors affecting internal corrosion of transmission pipeline systems (<0.5% S&W), as well as a progress report on research aimed at improving chemical mitigation of this threat in heavy oil product streams. Typical pipeline corrodents and corrodent transport mechanisms are explored. Transmission quality hydrocarbon products are shown to carry micro-emulsified water, various solid particles, solid particles with micro-attached water, and bacteria. While micro-emulsified water can be considered benign owing its ability to be transported harmlessly without accumulation; water-wetted solid particles have sufficient density to reach the pipe floor. Patterns of internal corrosion on a transmission pipeline are used to demonstrate the significance of solids accumulation leading to under-deposit corrosion. Analysis of pipeline sludge reveals significant populations of different bacterial species indicating the existence of a robust biomass capable of creating or sustaining a corrosive environment. Corrosivity testing of pipeline sludges was performed using two static autoclave coupon methods. One test method demonstrated that the addition of chemical inhibitor directly to the pipeline sludge could reduce corrosion rates as effectively as batch treatment of a clean coupon. A rotating mechanical contactor was designed and built to facilitate the blending of corrosion inhibitor with pipeline sludge under ‘like-pipe’ flow conditions, but results of sludge corrosivity testing using this device are not yet available.
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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 | 0.001 |
| 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".