INVESTIGATION OF ENVIRONMENTALLY FRIENDLY SOLVENTS FOR THE RECOVERY OF HEAVY OIL AND BITUMEN
Bibliographic record
Abstract
Solvent injection recovery processes were introduced as a more energy-efficient and environmentally friendly alternative to Steam injection processes. However, BTX chemicals commonly used for crude oil recovery due to their strong solvency and low asphaltene precipitation are acutely toxic and harmful to the environment. These chemicals are easily soluble in water causing groundwater contamination. In this study, I test the effectiveness of three solvents; Visred, Limonene and Pinene and compare their results to conventional toxic solvents. Visred, although toxic, is chosen as a solvent as it can reduce the amount of solvent injected into the wellbore. Limonene and Pinene are environmentally friendly non-toxic edible solvents. These are also readily available and cheaper than conventional solvents. Three crude samples have been tested in this study: Canadian Bitumen, Californian heavy oil (Cali 1) and Californian extra heavy oil (Cali2). A total of 15 core flooding experiments including both steam and steam-solvent flooding processes were conducted and the best recovery method for each crude sample was determined based on produced oil quality, displacement efficiency, oil recovered and economic parameters. This work proves the effectiveness of these solvents in yielding comparable if not more oil recovery than conventional solvents and can be instrumental to heavy oil and bitumen resources across the globe.
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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) | 0.000 | 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) | 0.049 | 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 teacher head, 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".