Mineralogy and Surface Chemistry of Alberta Oil Sands: Relevance to Nonaqueous Solvent Bitumen Extraction
Bibliographic record
Abstract
The mineralogy, chemistry, surface properties, and pore structure characteristics of the four different petrologic types of Alberta oil sands were determined in order to better understand their impact on nonaqueous solvent bitumen extraction. Quartz, clay minerals, and carbonates were the main mineral constituents of the studied samples. With increasing weight percentage of clay minerals, the solvent bitumen extraction (by the Dean–Stark procedure using toluene) decreased. Fine grained illite and illite-smectite had more detrimental effect on solvent bitumen extraction than coarse grained kaolinite. The pore structure data revealed that the extractability of bitumen from the oil sands depends on accessibility, more specifically on the size of bitumen-filled pores, by the solvent. The relative abundance and size of pores in the oil sands were controlled by mineral composition and particle size. Fine size fractions were typically enriched in kaolinite and 2:1 clay minerals (illite and illite-smectite) and contained small pores, with diameters of ∼100 and ∼10 nm, respectively. Coarse size fractions were usually rich in quartz and contained large pores with diameters greater than 200 nm. The efficiency of nonaqueous bitumen extraction was significantly higher for coarse grained, quartz-rich oil sands in which the organic matter was distributed mainly at a scale of greater than 200 nm compared with fine grained, clay-rich oil sands where organic matter occurred as meso-macropore filling material within clay mineral aggregates. Overall, the finest (<0.2 μm) fraction of oil sands composed mainly of illite and illite-smectite had the most detrimental effect on solvent bitumen extraction. This was related to the contrasting properties of the finest fraction (mainly high specific surface area, high cation exchange capacity, and the presence of small-sized pores) compared with the other studied fractions of oil sands.
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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 | 0.001 | 0.001 |
| 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".