Bibliographic record
Abstract
Abstract In this research lab and field corrosion coupon testing was completed to determine corrosion rates on commonly used Steam Assisted Gravity Drainage (SAGD) metals. This was done to evaluate general corrosion rates, and how they vary with well depth, and operating environment. Based on this analysis and evaluating scale composition a dominating corrosion mechanism was also determined. Lab testing utilized high temperature high pressure autoclaves to allow for field various corrosion rate and characterization measurements to be taken under controlled conditions. Measurements taken include linear polarization resistance, weight loss, and zero resistance annode to determine corrosion rates. Cyclic potentiodynamic polarization and potentiostatic polarization measurements were also obtained to further evaluate a materials effectiveness in preventing pitting corrosion. Materials tested in the lab were 1018 carbon steel, Deloro-40 and Stellite-6 hard facing finishes, TN-55TH, galvanized (GLV) J-55, and K-55. Field coupon testing consisted of installing corrosion coupons at various elevations in the annulus of a producting SAGD well. Coupon materials tested included L-80, J-55, and a GLV-J55+J-55 creviced couple. Analysis consisted of weight loss corrosion rate determination, visual inspections, and x-ray diffraction analysis to determine scale compositions. Field coupons showed corrosion rates decreasing from 0.0178mm/y at the bottom of the well to 0.0145mm/y at higher well elevations. This corresponded to a decrease in iron sulfide (FeS) scale content from the well bottom upwards. Based on the scales composition, operating conditions, and fluids present, it is likely that the scales were formed through the well known solid-state reaction between aqueous H2S and the metal. High average corrosion rates of 0.263mm/y were measured in the lab, compared to a low 0.0183mm/y in field studies. This difference is due to the inhibiting effects of oil in the field which inhibits corrosion rates and the longer field test duration.
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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 | 0.000 | 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".