Corrosion Failure and Control of Carbon Steel and Anti-Corrosion Performance Evaluation of Candidate Materials in Thermal Applications
Bibliographic record
Abstract
Abstract This study presents an investigation of the on-site corrosion of carbon steel pipes with stainless steel mesh screens in a steam flood well in the Athabasca oil sand reservoirs to determine the failure patterns and mechanisms. To mitigate the corrosion of carbon steel, several candidate materials were selected, and their corrosion resistance was investigated. In this work, the corrosion behavior and film characteristics of carbon steel pipes were studied by surface analysis techniques such as scanning electron microscopy, energy dispersive spetrocsopy and X-ray diffraction. Corrosion resistant alloys (proRSf and proRSc), anti-corrosion coating (proRA05a) and pre-treated steel (proRAQa) were considered as alternative materials to carbon steel (proRAa and proRAb) and their corrosion protection performance in brine solution was evaluated by electrochemical methods such as potentiodynamic sweep and electrochemical impedance spectroscopy. Results show that severe erosion-corrosion occurred on inner wall of the pipes and caused significant wall-thinning of pipes along with localized corrosion damages, which is the dominant reason for base pipe failure. In spite of the slight corrosion on outer wall of the base pipe, severe localized corrosion appeared at the interface between the carbon steel pipe and stainless steel mesh screens due to the galvanic corrosion effect of dissimilar metals. The corrosion rates of the corrosion-resistant materials were two or three orders of magnitude lower than that of carbon steel. The corrosion resistance ranking order is proRSc > proRSf > proRA05a > proRAQa > proRAa > proRAb. This study improves the material selection procedure in thermal operations by investigating several alternatives to carbon steel. It also provides a testing procedure to assess the corrosion resistance of the material in thermal applications.
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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 | 0.002 | 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".