Down-The-Hole without a Paddle: Corrosion Mitigation of Wellhead Surface Casings Using IMM Coatings
Bibliographic record
Abstract
Abstract For high temperature thermal operations in the oil and gas industry, such as wells used in steam assisted gravity drainage (SAGD) recovery, an extremely costly challenge has been to mitigate severe corrosion - with otherwise potentially dangerous consequences - of thousands of carbon steel near-surface casings. Until recently only a few mitigation options have been trialed, and with limited success. However, in the past five years, after an idea originating from the proven performance of a novel high temperature IMM (inert multi-polymeric matrix) coating preventing corrosion under insulation (CUI), producers have successfully trialed and now adopt this unique technology as the newest corrosion mitigation technique for existing wellhead surface casings. This paper outlines the aggressive service conditions experienced by wellhead casings and the resulting failures seen to date in the Alberta oil patch. Deleterious in-service conditions include; high temperatures and significant temperature fluctuations, expansion and contraction of the steel substrate, and the wet and dry oxidizing micro-environment, the influence of concrete, and a plethora of chlorides and other contaminants elevating the corrosion rate. Previous corrosion mitigation programs and current inspection techniques are reviewed. The chemistry and performance attributes of the novel IMM coating (hereinafter referred to as “IMM coating”) technology is reviewed, and why it offered a unique solution to the hitherto massive costs of addressing wellhead surface casing corrosion. The surface preparation of steel surfaces and application of the IMM coating to ambient or hot surface casings is described in detail from the applicator's vantage point.
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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".