The Effects of Minerals on Heavy Oil and Bitumen Chemistry When Recovered Using Steam-Assisted Methods
Bibliographic record
Abstract
Abstract The production of gaseous sulfur-containing species during the steam-assisted recovery of heavy oil and bitumen have important consequences for both economics and safety. Factors such as the effects of mineral matrices require laboratory data to produce accurate models. To study mineral effects on gas production we studied a well-characterized oil-containing core and the isolated crude oil from that core. The samples were run at 250–300°C in the continued presence of liquid water for 24 hours. The reaction products of all experiments include gases, oil flotate, oil sinkate, water-soluble products, and water-insoluble residues. All reaction products were studied with a variety of analytical techniques, including FTIR spectroscopy, chromatographic fractionation (SARA analysis), GC-MS, pyrolysis GCMS and GC-FPD/TCD. These techniques were applied to whole oil, maltenes and asphaltene fractions. Physical properties including viscosity and density were also measured. Our data provide insights into the physical and chemical consequences of steam assisted recovery of heavy oils and bituments from sedimentary rock reservoirs and reveal that geological and geochemical context is an essential consideration.
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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) | 0.000 | 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) | 0.002 | 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".