Corrosion Management and Cleaning of SAGD Produced Gas/H2S Scavenger Contactor
Bibliographic record
Abstract
Abstract Produced gas (PG) from the Steam Assisted Gravity Drainage (SAGD) recovery process typically consists of approximately 6% mercaptans and 7,500 ppm H2S. This sour gas is sweetened using a triazine H2S scavenger in a carbon steel (CS) contactor. The reactions between the scavenger and H2S were studied for operation optimization. A corrosion management program was established in PG lines and contactor including water/gas chemistry study and coupon monitoring. To keep the high contact efficiency of H2S and the scavenger, the contactor required thorough cleaning to remove several years of built up solids. A commercial hydrogen peroxide (H2O2) incorporating a stabilizer to reduce its decomposition in the presence of metal ions is used for mercaptan removal and oxidizing other sulfur containing compounds. The first contactor cleaning with H2O2 at 60°C (140°F) resulted in the generation of hazardous oxygen from H2O2 decomposition and the release of H2S. New procedures developed with an optimized cleaning temperature of 30°C (86°F) and a pH of 8.0 ensured the effective removal of the mercaptans, a significant decrease in H2O2 decomposition, and the oxidation of polysulfide for corrosion mitigation. This paper discusses the successful application of these procedures as well as the results of H2S scavenger reactions and the corrosion monitoring program.
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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 | 0.001 | 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".