Evaluation of CO2 Based Vapex Process for the Recovery of Bitumen from Tar Sand Reservoirs
Bibliographic record
Abstract
Abstract The vapour extraction (Vapex) process has recently gained considerable attention for its promising application for the recovery of heavy oil and bitumen reservoirs that are deemed unsuitable for thermal methods. In conventional Vapex process, a mixture of vaporized solvent (propane and/or butane) and a commercially available non-condensable gas (methane, natural gas) is injected into the reservoir to reduce the oil viscosity. Therefore, the cost of these injected gases per barrel of produced oil is critical for the economic viability of the process. With predicted increase in the gas prices, the process has lost some of its initial attraction. For reducing the solvent cost, the injection of CO2 as a non-condensable gas appears to be an attractive alternative. The advantage of this low cost alternative is that the solubility of CO2 in heavy oils is significantly higher than that of methane. Consequently, the mixture of propane and CO2 can provide greater reduction in oil viscosity compared to the equivalent mixture of propane and methane. In addition, this CO2 injection can play a significant role in sequestering CO2 emissions. However, the CO2-propane-heavy oil system shows complex phase behaviour, forming multiple liquid phases. Such multiple liquid phases can reduce the effectiveness of gravity drainage process by introducing complex relative permeability effects. This paper presents an experimental study aimed at developing a new CO2 based Vapex process for in situ recovery of bitumen from tar sand reservoirs. The experiments have been performed in a partially scaled physical model at different operating conditions. The main conclusion from this study is that the CO2 based Vapex process is more cost effective and environmentally friendly than the conventional Vapex process.
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".