Corrosion Rates of Stainless Steel and Low Alloy Steels in Harsh and Corrosive Environments for Subsea Application
Bibliographic record
Abstract
Abstract Corrosion assessments based on available data or modelling are necessary for end-user material selection, yet there is limited data to specific well conditions. For subsea landing strings, various alloys are used, and due to the chemistry and corrosivity variation from one field to another, a study was conducted to investigate the corrosion performance of four specific alloys for the application. Experiments were performed in a high-pressure, high-temperature (HPHT) autoclave and corrosion rate was measured using weight loss (WL) method and rotating cage autoclave with WL coupons. After each experiment and WL measurements, the coupons were studied for pitting corrosion using profilometry scans. Four alloys, UNS(1) K21590 (F22), UNS G41400 (4140), UNS G86300 (8630) and UNS S17400 (17-4PH), were evaluated in HPHT autoclaves for corrosion under three different environment conditions varying in temperature (60°C and 150°C), H2S and CO2 partial pressures, and shear stress in HPHT autoclaves. The comparison showed that the corrosion rate for all the studied alloys increased as partial pressures of the acid gases increased and temperature decreased. Between the three low alloy steels studied in this work, UNS G41400 performed worst, especially at a lower temperature. As expected, alloy UNS S17400 showed the best performance and the lowest corrosion rates. Profilometry studies showed no evidence of localized corrosion and pitting in all four alloys.
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 | 0.001 | 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".