Application of High Temperature Coatings for Near-Surface Corrosion Mitigation on SAGD Wells
Bibliographic record
Abstract
Abstract Near-surface casing corrosion has been observed to occur on operating steam-assisted gravity drainage (SAGD) wells in the Surmont field. The corrosion issue could pose risks to containment assurance and have negative implications on well economics throughout the life cycle. This paper discusses how the corrosion was first identified and then successfully mitigated using a high temperature coating solution on the production casing. The methodology used for coating testing, evaluation, and selection is also discussed. A two-tiered testing program was developed to qualify suitable coatings for the Surmont application. Eleven coating systems from various suppliers were first tested in the lab in a screening exercise to identify the best-in-class coatings for further evaluation. The top six performing coatings were then tested in the field environment to complete the evaluation. Coating performance was evaluated using a series of standard test methods. Out of the eleven candidate coatings, only two coatings qualified for the high temperature application in Surmont. These coatings are expected to provide long-term corrosion protection of the production casing at an optimized cost. One of these two coatings was successfully applied on an operating well and has provided reliable corrosion protection after four years of service. The methodology discussed in this paper for coating evaluation and application has been successfully implemented in the Surmont field. The findings from this work can be used to mitigate near-surface corrosion which will result in improved containment assurance and well economics.
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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) | 0.000 | 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 teacher head, 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".