Miscible Flooding for Bitumen Recovery with a Novel Solvent
Bibliographic record
Abstract
Abstract Steam injection is an effective heavy oil recovery method, however, poses several environmental concerns. Solvent injection methods are introduced in an attempt to combat these environmental concerns. This paper evaluates the effectiveness of a new solvent (VisRed) in the recovery of a Canadian bitumen and compares its results with toluene. While VisRed is selected due to its high effectiveness as a viscosity reducer even at very low concentrations, toluene is selected due to its high solvent power. Five core flooding experiments were conducted; E1 (Steam flooding), E2 (VisRed flooding), E3 (Toluene flooding), E4 (Steam + Toluene flooding), and E5 (Steam + VisRed flooding). Core samples were prepared by saturating 60% of the pore space with oil samples and 40% with deionized water. The solvents were injected at a 2 ml/min rate, while steam was injected at a 18 ml/min cold water equivalent rate. Produced oil and water samples were collected every 20 min during every experiment. The oil recovery efficiencies of the core flood experiments were analyzed by the emulsion characterization in the produced fluids and the residual oil analysis on the spent rock samples. The best oil recovery of ~30 vol % was obtained for E2 (VisRed) in which VisRed was injected alone. Although similar cumulative recoveries were obtained both for E2 (VisRed) and E3 (Toluene), the amount of VisRed injected [~1 pore volumes (PV)] was half the volume required by toluene (~2 PV). The produced oil quality variations are mainly due to the formation of the water-in-oil emulsions during mainly steam processes (E1, E4, and E5). The increased amount of the polar fractions in the produced oil enhances the formation of the emulsions. These polar fractions are namely asphaltenes and resins. As the amount of the polar fractions in the produce oil increases, more water-in-oil emulsion formation is observed due to the polar-polar interaction between crude oil fractions and water. Consequently, E1 and E5 resulted in more water in oil emulsions. The cost analysis also shows the effectiveness of solvent recovery over steam-solvent recovery processes.
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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 | 0.000 | 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) | 0.001 | 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".