Applicability of Vapor Extraction Process to Problematic Viscous Oil Reservoirs
Bibliographic record
Abstract
Summary Production of heavy oil and bitumen from subterranean deposits is difficult, even under best of circumstances, due to very high oil viscosity. The recovery by currently used thermal based methods is more problematic and uneconomical for some of the reservoir scenarios, such as the reservoirs with an overlying gas cap, bottom water table, high water saturation, low porosity, low thermal conductivity, thin pay zone, vertical fractures and/or fissures, etc. Currently there is no proven recovery technique that can be economically applicable to such viscous oil reservoirs. However, there is a huge amount of hydrocarbon resource present in such reservoirs that can only be exploited with new concepts. The Vapex (vapor extraction) process has recently emerged as a superior technology for the recovery of heavy oil and bitumen reservoirs. Recent research has shown that the process is highly energy efficient, environmentally friendly, causes in-situ upgrading, and requires low capital investment compared to its competitor SAGD process. The objective of this work was to evaluate the effectiveness of this newly postulated Vapex process to some of these problematic reservoir scenarios. An extensive experimental study has been carried out in a partially scaled physical model. Some experimental results, theoretical analysis, and the applicability of this process to such problematic viscous oil reservoirs are presented and discussed.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".