Bibliographic record
Abstract
Summary Incorporating some heat injection along with solvent injection appears to bethe most viable option for improving the oil-drainage rate of vapour-assistedpetroleum extraction (VAPEX) in extraheavy-oil formations. This study wasintended to quantify the maximum possible increase in VAPEX drainage rate thatcan be obtained by heating the formation to a target temperature. Theexperimental phase of this study involved conducting VAPEX experiments in alarge high-pressure physical model, packed with 250-darcy sand, using propaneas the solvent. The physical model was preheated to 40, 50 and 60°C, andpropane was injected at the same test temperature but different injectionpressures to observe how injection pressure affects oil-drainage rate atelevated temperatures. In the experiments at elevated temperatures, but withoutincreasing the injection pressure, higher rate of oil production was achievedin the early stages of the process. However, a stabilized rate of oilproduction did not show pronounced improvement caused by a lower solubility ofpropane in the oil at higher temperatures. Increasing injection pressure alongwith increasing the test temperatures was successful in accelerating the oilproduction. The oil used in these experiments was found to become mobile withthe increase in temperature even without solvent dissolution. As a result, thetotal rate of oil production appeared to be controlled by two mechanisms: (1)by solvent dissolution and oil mobilization at the boundaries of the vapourchamber and (2) by pure free-fall gravity drainage beyond the vapour chamberwherever gravity head was sufficient to push the mobile oil toward theproduction well. The results of this these tests define the upper limit of oilrates achievable with heated solvent injection. They can also be used to assessthe applicability of VAPEX to warm reservoirs naturally (e.g., in Venezuela)and reservoirs with mobile oil in place.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".