Bibliographic record
Abstract
Abstract The objective of this work was to study the effect of connate water in the progression of the Vapor Extraction (VAPEX) process for heavy oil recovery. A large fraction of the physical model tests of the VAPEX process reported in the literature have been conducted without any connate water in the system. The absence of connate water was rationalized by suggesting that it has little or no influence on relative permeability of oil and since the vaporized solvent does not dissolve in water, there is no effect of water on the mass transfer process. We have evaluated the effect of connate water on VAPEX performance using physical model experiments carried out in a visual model, with different connate water saturations. Butane was used as the solvent and fine Ottawa sand was used for packing the model to obtain permeability and capillary pressure values comparable to the field conditions. In addition to the visual observations of the size and shape of the vapor chamber, the rates of oil and gas production were monitored during the experiments. The results show that connate water has a measurable effect on the process, both in terms of the shape of the vapour chamber and the drainage rate of the diluted oil. The presence of connate water causes faster spreading of the vapor chamber in the lateral direction and tends to increase the thickness of the mixing zone. This increase in the mixing zone thickness appears to result from capillarity driven fingering phenomenon. The mixing zone had a distinct uneven appearance that was similar to patterns generated by frontal instabilities in miscible displacements. The effect of connate water on drainage rate was an increase in the initial rate but a reduction in the rate subsequently. The presence of mobile water speeds up the communication between the two wells and leads to even faster spreading of the vapor chamber. Finally, the de-asphalting and oil upgrading was more significant in the presence of connate water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".