Experimental and Numerical Study of Asphaltene Deposition and Precipitation in the Vapor Extraction Process
Bibliographic record
Abstract
Abstract Vapor Extraction (VAPEX) is one of the most promising solvent-based methods to tackle the issues associated with SAGD. Asphaltene precipitation due to solvent injection and its in-situ upgrading plays an essential role in VAPEX. As the VAPEX chamber expands laterally and vertically, the speed of the front movement and the angle of the chamber boundary varies. This research investigates the rate of asphaltene precipitation and deposition in VAPEX using different solvents. In this research, three VAPEX experiments are carried out in a physical model using bitumen and three solvents (propane, butane, and pentane), where VAPEX chamber movement, expansion rate, and the amount of precipitated asphaltenes are monitored. In addition, an Eulerian-Lagrangian model, including the Eulerian approach for the continuous phase (solvent-rich area of the chamber) and the Lagrangian approach for the extracted asphaltene solid particles by bitumen dilution, is generated for numerical modelling. The movement and the deposition of the asphaltene particles at the front are calculated and then validated with the physical model experiments. A significant amount of precipitated asphaltenes was observed when pentane was used, as it creates a pattern on the wall of the physical model, especially near the wellbore area. However, no specific pattern was observed for other experiments. In terms of chamber expansion and movement, the chamber expanded laterally and then vertically in the pentane and propane systems, while the butane system revealed vertical movement at the beginning, followed by lateral movement. In all cases, the amount of precipitated asphaltenes was always higher near the wellbore compared with areas far from the production well. A numerical model has been implemented to capture the transport phenomena, simulate the asphaltene deposition mechanisms, and reveal the variations in the behavior of different solvents. This study can assist the oil sands industry in optimizing the VAPEX process to have an effective in-situ upgrading and the highest production rate with better oil API gravity, as the literature suffers from a lack of understanding of the mass transfer physics involved in VAPEX. Also, this study sheds light on the physics behind the asphaltene deposition and precipitation process in VAPEX, as it is impossible to be understood without molecular dynamic simulation besides laboratory experiments.
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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) | 0.001 | 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 | 0.001 | 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".