Scale Formationfrom Dissolved Organic Matter in SAGDSteam GeneratorsPart I: Acidic Species Characterized by FTICR-MS
Bibliographic record
Abstract
Boiler feedwater in in situ oil production from Alberta (Canada) oil sands contains a complex mixture of organic and inorganic compounds, parts of it responsible for scale formation in steam generators, which challenges operation efficiency and increases greenhouse gas emissions. A series of heating experiments using a closed reactor system were performed to study the effect of high temperature (300 °C) and pressure (9 MPa) on the quality of the organic compounds. For this goal, a detailed molecular organic matter characterization of six boiler feedwaters before and after heating from two Alberta geographical areas was carried out. Organic species from water and heated water samples were analyzed with ESI-N FTICR-MS. Solid generation and a pH drop (even to pH 1.5) were the immediate impacts of heating BFWs. Our analysis showed that the formed solids mainly consisted of organic species. The analysis of DOM showed that the Ox class group is the most abundant in all water samples, followed by the SOx class group in the Athabasca region and NOx species in the Cold Lake area, respectively. The analysis of solids with FTICR-MS showed enrichment of compounds with higher carbon numbers and DBE and S-containing species compared to the BFW DOM, while sulfur-containing constituents in BFWs were depleted after heating. Based on the experimental results, we propose the following mechanisms for the observed phenomena. (1) Cleavage of weak C–S bonds in dissolved organic matter leading to countless reactions is likely one of the key initiators of precipitation. (2) pH decrease during heating facilitates solid precipitation. (3) Part of the existing large multifunctional species breaks down and produces smaller acidic species, contributing to a further pH decrease. (4) Bonding of some compounds with multiple functional groups forms larger molecules that precipitate out and assist in lowering the pH by reducing alkaline compounds in the water.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.732 | 0.010 |
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; both teacher heads agree on what is shown here.
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".