Experimental Analysis of Optimal Thermodynamic Conditions for Heavy-Oil/Bitumen Recovery Considering Effective Solvent Retrieval
Bibliographic record
Abstract
Summary Light-hydrocarbon solvent injection is an effective process to improve heavy-oil/bitumen recovery from oil sands. In this process, oil production is achieved by gravity drive, which is enhanced through the dilution of oil by injected solvent. However, solvent retrieval is one of the major economic concerns in defining the viability of this technique. In this research, a sandpack experimental study was conducted, and the solvent retrieval was determined on the basis of thermodynamic conditions and fluid characterization. Two heavy-oil samples (8.6°API and 10.28°API) from different fields in Alberta, Canada, and four light-hydrocarbon solvents (propane, n-hexane, n-decane, and distillate hydrocarbon) were used in this experimental scheme. Results showed that solvent retrieval increases when light-hydrocarbon solvents (propane and distillate hydrocarbon) are used compared with solvent with high molecular weight (n-hexane and n-decane). Temperature and pressure highly influenced the solvent retrieval. The percentage of solvent retrieval increased when the hydrocarbon solvent was closer to the vapor phase (dewpoint). However, oil recovery showed significant reduction when propane and n-hexane were injected because of high asphaltene deposition on the sandpack. The maximum solvent retrieval was calculated to be nearly 98% at 120°C and 698.47 kPa when propane-and-distillate hydrocarbon was used as solvent. Formation damage, on the other hand, may increase when propane is used as solvent because of the high asphaltene deposition.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".