Asphaltene Precipitation and Its Effects on the Vapour Extraction (VAPEX) Heavy Oil Recovery Process
Bibliographic record
Abstract
Abstract Asphaltene precipitation is one of the most important physical phenomena during the solvent vapour extraction (VAPEX) heavy oil recovery process. After the asphaltene precipitation occurs, the produced heavy oil is in-situ deasphalted and thus has a lower viscosity and better quality. On the other hand, precipitated asphaltenes may plug some small pores of the reservoir formation and thus reduce its permeability. In this paper, a series of the VAPEX tests is conducted by using a rectangular visual sand-packed high-pressure physical model to study the detailed effects of solvent type, operating pressure, and sand-pack permeability on the asphaltene precipitation and subsequent deposition, which strongly affect heavy oil production and quality. It is found that when the operating pressure is close to the vapour pressure of pure propane or the dew-point pressure of a butane mixture, the occurrence and extent of asphaltene precipitation and deposition strongly depend on the sand-pack permeability. At a considerably high permeability of several hundred Darcies, asphaltene deposition occurs near the injector and solvent-diluted heavy oil drains quickly, which lead to a significant heavy oil viscosity reduction and a high oil production rate, respectively. When the sand-pack permeability is low and close to a typical heavy oil reservoir permeability, however, the residence time of the solvent-diluted heavy oil inside the physical model is long due to its low drainage velocity. A sufficiently high solvent concentration in the heavy oil causes severe asphaltene precipitation in this case. A large number of the precipitated asphaltenes are blocked and deposited at the pore throats so that the porous medium is plugged to some extent.
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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".