The evaluation of CO2-based vapour extraction (VAPEX) process for heavy-oil recovery
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 it