Corrosion under insulation in-situ testing in marine environment
Bibliographic record
Abstract
Oil and gas pipelines in marine or coastal environments experience significant corrosion when moisture and chloride penetrate surface protections. There is lack of in-situ work studying corrosion under insulation (CUI) and corrosion under coating, especially in marine atmosphere. As well, monitoring and inspection of assets under corrosion attack is a concern, as it can spread insidiously in out of sight areas. Real time monitoring of pipelines and other assets is best to get information quickly, especially in difficult to reach areas. Electrochemical potential noise (EPN) is one proposed method to investigate CUI using simple equipment and should be further investigated in determining quantitative corrosion results. This thesis investigates corrosion and pitting from pipelines which were coated, uncoated, insulated, and not, in a marine harsh environment field experiment. The surface morphology, and mechanisms of corrosion were also studied. Thirty-six A333 low temperature carbon steel pipelines were placed at Argentia, NL, Canada, an extremely corrosive environment (C5) near shoreline. High humidity, winds, and sea-spray are present throughout most of the year. Corrosion rate was assessed using mass loss and optical inspections were used for pit depth. X-ray diffraction and scanning electron microscope were used to determine corrosion products and surface morphology. Corrosion near the ends of the pipe were most severe, perhaps due to crevice corrosion, and ingress of moisture and chloride. Insulated uncoated pipes showed deepest pits, therefore when pipes are insulated, anti-corrosion protective coating should be applied. Corrosion and pitting rates were lowest in insulated and coated pipes. Real time monitoring using EPN was explored, varying electrode size, temperature, and electrolytes. EPN was recorded using Keithly digital multimeter controlled by LabVIEW software. Potential was investigated using time and frequency domain methods to determine its usefulness in monitoring corrosion. In this research the best way to relate potential to mass loss and corrosion type occurred using time-frequency domain power spectral density from raw potential noise generated from electrodes. This in-situ testing enhanced the understanding of corrosion mechanisms and pitting in the environment. Important coating and insulation time to failure was recorded, which provides insights for oil and gas operations regarding inspection, maintenance, and design life. EPN tests proved a simple method and equipment can be used for in-situ corrosion detection, which can help ensure safety and asset integrity.
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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".