Corrosivity of Produced and Make-up Water in Oil Sands Thermal Water Treatment Systems
Bibliographic record
Abstract
Abstract Water treatment systems used to recycle produced water and/or make-up water for the production of steam used in the SAGD and CSS processes have noted failures associated with erosion-corrosion, under deposit corrosion, and fouling/scaling. Oil sands operators employ corrosion monitoring tools, chemical treatment, and/or material selection to resolve integrity related issues. However, the unpredictable occurrences of serious corrosion issues related to the complex and constantly changing water chemistries make it difficult to choose the appropriate preventative and mitigation strategies. This is further complicated by the effects of operating conditions; such as temperature, pressure, and flow geometry. This paper presents the corrosivity of model/simulated produced and make-up (brackish) water to UNS G10180 carbon steel. Rotating cylinder electrode methodology was used to determine the general corrosion rates using linear polarization resistance technique. The corrosion rate in the model brackish water system was low for the investigated environmental conditions. However, as the pH of the systems was reduced from 8 to 6, the corrosion rate became high (0.8 mm/yr). Similarly, dissolved oxygen (DO) ingress was found to increase the general corrosion rate. However, only DO ≥ 8,000 ppb led to localized (pitting) corrosion (0.6 mm/yr). Contrary to model brackish water corrosivity, oxygen ingress as low as 120 ppb triggered localized corrosion in the simulated produced water system.
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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".