The Underoil Droplet Corrosion Behavior of X100 Pipeline Steel
Bibliographic record
Abstract
Canadian natural bitumen production will reach a peak of 112 million barrels per day in 2078 [1]. Pipelines are primarily used to transport this bitumen. Bitumen must be diluted in order to be pumped through a pipeline effectively because it is otherwise too viscous to flow [2]. Underoil droplet corrosion of diluted bitumen (dilbit) pipelines may lead to the loss of pipe wall thickness, which deteriorates the structural reliability and increases the risk of leaking of pipelines. The current models for atmospheric droplet corrosion, where no limitation to oxygen access is imposed, cannot be applied to underoil droplet corrosion in dilbit pipeline conditions. In this study, a NaCl droplet on X100 pipeline steel covered by paraffin oil is used to simulate the corrosive environment encountered in diluted bitumen pipelines. A new distinctive corrosion mechanism is proposed to explain the corrosion of X100 pipeline steel beneath an underoil salty droplet. This mechanism accounts for the depleted oxygen levels in the droplet environment. In the initial stage (1 h), the distribution of corrosion pits was heterogeneous with one area under the droplet presenting a higher pit density. As the corrosion proceeded (24 h), the localized corrosion was governed by pH development due to initial pit density. Regions of initially higher pit density switched to general corrosion, while regions with lower pit densities continued pitting. The effects of droplet volume, chloride ion concentration, and temperature on the underoil droplet corrosion behavior of X100 pipeline steel were also studied by scanning electron microscopy, Raman spectroscopy, and profilometry. At 23 °C, lepidocrocite and hematite were observed in the region where pitting was initially prevalent. However, as this region switched to uniform corrosion, goethite, lepidocrocite, and hematite were formed. The same distribution of crystal structures was identified for the droplets with varying volume and chloride ion concentration. When the temperature was increased to 60 °C, hematite was detected in both regions. Decreased droplet volumes, increased chloride concentration, and raised temperatures increase corrosion penetration when uniform corrosion is prevalent. This study provides important findings to better understand the mechanisms behind droplet corrosion underoil and informs future predictions of the corrosion rate of pipeline steels. References [1] S. H. Mohr and G. M. Evans. Long term prediction of unconventional oil production. Energy Policy 2010; 38: 265−276. [2] National Research Council of the National Academy of Sciences, Effects of diluted bitumen on crude oil transmission pipelines, in: Transportation Research Board Special Report 311, Washington DC, 2013.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".