Experimental Investigation of CO2 Utilization as an Injection Solvent in Vapour Extraction (VAPEX) Process
Bibliographic record
Abstract
In this research, an extensive experimental investigation is carried out to evaluate the utilization of CO2 as an injection solvent for VAPEX process. To accomplish this goal, two large, visual rectangular, sand-packed VAPEX models with 24.5 cm and 47.5 cm heights were employed to run the experiments using Plover Lake heavy oil (5650 cP) with a low permeability (6∼9 D) sand pack. Propane, CO2 and propane/CO2 mixture were considered as respective solvents for the experiments, and a total of 6 tests were carried out. The heavy oil production rate and the produced gas-oil ratio were measured periodically. Moreover, separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models. To observe the drainage height effect in more details, a comprehensive image analysis was performed during the solvent chamber evolution. As a result, it was determined that drainage height has a significant impact on production rate and heavy oil recovery. The results prove the complexity of the effect of drainage height and the up- scaling issues with the VAPEX process. Furthermore, in terms of solvents, propane showed the highest recovery factor of 75% of original oil in place due to its favourable low vapour pressure and high solubility. Ultimately, the promising recovery performance after introducing CO2 as a carrier gas was observed, the recovery factor of 55% of original oil in place was achieved when the mixture of propane/CO2 was used as the solvent. After conducting asphaltene measurement tests, it was observed that more asphaltene precipitation occurred close to the injection points and at the oil/solvent interface. Furthermore, the image analysis revealed that the highest sweep efficiency was observed to be 0.86 after injecting propane, followed by propane/CO2 mixture, and pure CO2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".