Effects of Waterflooding and Solvent Injection on the Solvent Vapour Extraction (VAPEX) Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Solvent vapour extraction (VAPEX) process is an economically viable, technically sound, and environmentally friendly in-situ heavy oil recovery method to exploit tremendous heavy oil and bitumen reserves. In this recovery process, significant heavy oil viscosity reduction is achieved through sufficient solvent dissolution and possible asphaltene precipitation. Over the past two decades, several researchers have carefully studied the effects of some major factors on the VAPEX process, such as the test pressure, reservoir porosity and permeability, solvent and heavy oil types, well configuration, and connate water saturation. However, it is unclear how waterflooding and solvent injection will affect a typical VAPEX process. In this paper, waterflooding and solvent injection effects are experimentally studied by using a visual rectangular sand-packed high-pressure VAPEX physical model with a low permeability. The physical model is packed and then saturated with a heavy oil sample at the connate water saturation. Pure propane and a mixture of n-butane and methane are used as respective solvents to extract two different heavy oil samples. The waterflooding effect is examined by performing a series of VAPEX tests with the initial waterflooding, prior to the subsequent solvent injection/soaking. In addition to the visual observation of the solvent chamber evolution, the heavy oil production rate, produced solvent–oil ratio, and asphaltene content of the produced heavy oil are measured during the waterflooding and solvent injection/soaking. It is found that the initial waterflooding causes an oil production reduction in the subsequent solvent injection. Also solvent breakthrough occurs earlier and a small amount of water is produced afterwards. This is because the initial waterflooding creates some low-resistance channels for the injected solvent to bypass the untouched heavy oil. As a result, the heavy oil is not diluted enough to be produced during the subsequent solvent injection/soaking. In the absence of waterflooding, however, solvent injection alone can increase the heavy oil production in comparison with the solvent-soaking process. Moreover, it is visually observed that solvent injection leads to less asphaltene deposition onto the porous media. This is quantitatively verified by a higher measured asphaltene content of the produced heavy oil at a higher solvent injection rate.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".