The evaluation of CO2-based vapour extraction (VAPEX) process for heavy-oil recovery
Bibliographic record
Abstract
Vapor extraction (VAPEX) has been proposed as an alternative for heavy-oil recovery in reservoirs where thermal methods face technical and economic problems. In VAPEX, a pair of horizontal injector-producer wells is employed. The gaseous hydrocarbon solvent (normally propane or a mixture of methane–propane or propane–butane) is injected from the top well and the diluted oil drains downward by gravity to the bottom producer. Recently, the idea of incorporation of CO 2 into the gaseous hydrocarbon mixture has emerged. Incorporation of CO 2 is believed to make the process more economical and environmentally and technically attractive. CO 2 is cheaper than the hydrocarbon gases and has higher solubility into the heavy oil than most of the hydrocarbon gases. It also adds value to the environmental side of the process as CO 2 can be sequestered while improving the VAPEX performance at the same time. Moreover, the addition of CO 2 to the injected gas increases the dew point of the solvent mixture, and solvent mixtures with higher dew point can be used in heavy-oil reservoirs with higher pressure in which the mixture of hydrocarbon gases may partly condense, which decreases the VAPEX efficacy. Thus, the advantage of incorporating CO 2 into the injected solvent is threefold. The objective of this work, therefore, is to simulate the performance of the VAPEX process when different solvent mixtures, including hydrocarbon gases and CO 2 , are incorporated with the aim of improving its performance. The design and the major results of the simulation for the CO 2 -based VAPEX process are 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".