Multilayer organic deposition on the rock surface with different wettabilities during solvent injection for heavy‐oil recovery
Bibliographic record
Abstract
During solvent‐based heavy‐oil and bitumen recovery processes, viscosity reduction occurs through dilution of oil by mixing process. However, asphaltene precipitation may take place, eventually resulting in organic deposition (maltenes and asphaltenes) in the reservoir causing a reduction in permeability through pore plugging and unfavourable wettability reversal. In this paper, two identical porous media (unconsolidated glass bead packs) with significant contrast in wettability were used to investigate these phenomena. The oil‐wet and water‐wet glass bead models were exposed to constant rate solvent injection (propane, n‐hexane, n‐decane, and distillate hydrocarbon). The thickness of the multilayer organic deposition was determined using focused ion beam (FIB/SEM) and scanning electron microscope (SEM). Three points from the models were analyzed to determine the level of asphaltene deposition and oil trapping (maltenes) on the surface of the glass beads and pore spaces. The results showed that the asphaltene migrated through the glass bead pack model. As a consequence, asphaltene deposition was observed at the middle and bottom points of the vertically situated glass bead packs with an injection point at the top, in addition to accumulated oil trapping at the production (bottom) end. Moreover, elemental mapping from the organic deposition visualization was conducted using the energy dispersive X‐ray (EDX) analysis. Heteroatom elements were found in the sample along with carbon and iron elements.
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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) | 0.000 | 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".