Weight Loss Corrosion with H2S: Using past Operations for Designing Future Facilities
Bibliographic record
Abstract
Abstract Managing sour corrosion in oil and gas fields has successfully been accomplished for years with carbon steel. This solution still remains the most cost effective option for most sour projects because of the high cost of corrosion resistant alloys (CRAs) able to resist severe sour conditions. The use of CRAs may nevertheless be preferable when high flow rates/ high velocities are expected or for offshore conditions where continuous inhibition is not practical because of its operational constraints. CRA is also of interest for wet gas processes downstream of the gas-liquid separation. Sulfur deposition is one of the major corrosion contributors in gas wells likely to produce such sulfur, particularly when combined with chloride ions. However, managing sulfur with carbon steel is quite well understood and adequate mitigation methods are available. Oxygen ingress also contributes to aggressive corrosion conditions.. Particular care must be given for preventing such ingress, either from drilling fluids, completion fluids or from low pressure process equipment. Weight loss corrosion is usually lower in sour conditions than in purely sweet ones although some conditions may lead to severe localized attacks, which parameters are not yet fully identified. Whether corrosion is high or low is very dependent on flow velocities and water-cut, low flow velocities being particularly favorable to corrosion. No clear mechanism is yet available that may explain how these factors influence this corrosion, considering other identified factors such as the water salinity, H2S / CO2 ratio, pH, solids (iron sulfides, elemental sulfur) and temperature. There is a need for further detailed mechanistic studies about these inter-related factors. Until key phenomena and mechanisms are better understood, extensive field experience remains the best way to provide an accurate prediction and sound design basis. This paper is aimed at sharing such experience and at providing relevant design basis from it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".