Electrochemical study and corrosion modeling of chromium alloy steels exposed to sulfide containing environment
Bibliographic record
Abstract
Corrosion, the destructive result of a chemical reaction between a metal or metal alloy and its environment in sour systems (H₂S dominant) has progressively become a greater concern to the oil and gas industry as a result of production from increasingly sour environments. In this study, the effects of the principal H₂S corrosion product, iron sulfide, on the corrosion resistance of alloy steel were initially investigated, followed by the study of the corrosion behavior of alloy steels in the presence of elemental sulfur, which is often present in sour systems. A new experimental method was applied to synthesize the iron sulfide layer on the steel surface with no H₂S in the environment. Attempts were also made to develop an accurate computational model to predict the corrosion rate of alloy steel in various environmental conditions. A series of experiments was performed to study chloride concentration, temperature, immersion time and pH effects on the corrosion behavior of alloy steel in the simulated sour environment. Various analyzing methods, such as scanning electron microscopy and X-ray diffraction, were applied to investigate the results which suggest that each factor can significantly affect the electrochemical behavior of alloy steel, especially in the presence of H₂S corrosion products. The corrosion of alloy steel in the presence of elemental sulfur was also studied using the cyclic polarization technique. In general, it was shown that the presence of deposited layers of elemental sulfur on the surface of 13% Cr steel will increase the corrosion rate by decreasing the scaling tendency of corrosion products on the surface, especially at higher temperature. The experimental data were analyzed and used to develop an analytical model to show the effects of corrosion products, chloride concentration, pH and temperature on the likelihood of corrosion of 13% chromium steel.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".