Corrosion of Brackish Water Systems Used for In-Situ Thermal Operations
Bibliographic record
Abstract
Abstract Water plants constructed to process brackish and fresh water sources for in-situ thermal oil sands production have noted failures associated with corrosion. The approach to resolve observed problems may depend on the local or upstream operating conditions, and may involve improved monitoring capabilities, additions of chemicals, and/or material selections. The unpredictable occurrences of serious corrosion issues related to the complex water chemistry make it difficult to choose the appropriate preventative and mitigation measures. This is further complicated by the effects of temperature, pressure, and flow turbulence on the equilibrium concentrations of the different species. Considering that the water chemistries are continually changing, it is beneficial to establish operating windows for the different chemical components and determine the effect of operating parameters such as turbulence and temperature. A better understanding of the singular and interactive effects of dissolved ions and gases in the waters is a necessary precursor for an effective integrity management program. The current paper details selective findings related to corrosion of brackish water systems used for in-situ thermal operations. The effect of pH, bicarbonate and oxygen were studied using model brackish water and the results show that the corrosion rates were significantly impacted by the pH and oxygen levels, while the reduction of the bicarbonate (alkalinity) content of the brackish waters did not sufficiently reduce the overall corrosion kinetics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".