Upscaling Study of Vapour Extraction Process through Numerical Simulation
Bibliographic record
Abstract
Abstract Vapour Extraction (VAPEX), a process to recover heavy oil by injecting vapourized solvent into a reservoir, has been extensively studied through small-scale 1-D and 2-D laboratory tests. Recently, a series of large-scale 3-D tests have been conducted by Saskatchewan Research Council (SRC). In this study, 2-D tests were conducted under the same conditions as those for the 3-D tests; then, numerical simulation models were investigated to reduce the uncertainty in upscaling the results from 2-D tests to 3-D tests. This helps to better understand the uncertainty in predicting the field-scale VAPEX performance. Plover Lake heavy oil was used in the tests, and the sandpack permeability was about 4.4 Darcy. In each test, the initial waterflooding was conducted prior to the subsequent solvent injection. Then, a numerical model was established to simulate the 2-D test. History match of the 2-D test was conducted by tuning the uncertainties such as the relative permeability, capillary pressure, solubility, and the wall effect in sand-packing. Afterwards the tuned parameters were applied to predict the 3-D test performance. Through comparison of the predicted and experimental results in the 3-D test, the capability of predicting up-scaled VAPEX processes through numerical simulation was examined, and the differences between physical and numerical modeling were identified. The results show that the waterflooding performance can be successfully predicted, whereas the uncertainty in upscaling the VAPEX process is large. In the waterflooding period, the predicted oil recovery factor was 25.78% compared with 23.4% in the 3-D test. In the VAPEX process, the difference between the predicted and measured oil recovery factors was in the range of 0.75–25.14%, depending on the different combination of uncertain parameters. This fact indicates that different scales of physical modeling are required in order to reduce the uncertainties in predicting the field-scale VAPEX performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".