H2S + CO2 Corrosion: Additional Learnings from Field Experience
Bibliographic record
Abstract
Abstract The field experience in H2S + CO2 corrosion which was first reported in 20061 has been significantly increased, some of which has been made available in the literature. Several new cases are included in this paper. This experience has been compiled and extensively analyzed during the last few years, which has allowed some recurrent corrosion effects to be found, and some lessons learned on how to address or mitigate such effects. Six distinct recurrent findings are listed in this paper. These findings have been analyzed in a very simple approach, which can be summarized as follows: Under significantly sour conditions and despite a permanent contact with water, the H2S + CO2 carbon steel corrosion rate typically remains low, as long as the following conditions are met: There are sufficient anions and cations at the steel surface to ensure quick FeS precipitation at the steel-water interface,Precipitation kinetics are high enough to ensure the precipitation reaction to be immediate at the interface, hence producing a dense protective corrosion product layer,No detrimental factor is present that would alter this protective layer, neither locally nor on extended parts of the surface.On the other hand, H2S + CO2 corrosion of carbon steel is possible, as long as water is present, if any of these 3 conditions is not fulfilled. Though this summary does not provide a detailed mechanistic description of H2S + CO2 corrosion, it provides a very simple way to approach this corrosion threat, while also showing essential tendencies and suggested barriers that future mechanistic description should be able to explain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.188 | 0.003 |
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; both teacher heads agree on what is shown here.
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".