Enhanced Vapor Extraction through Foamy-oil flow
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Bibliographic record
Abstract
Abstract Hydrocarbon solvent-based enhanced oil recovery techniques, such as vapor extraction (VAPEX) and cyclic solvent injection (CSI), have showed great potential to recover heavy oil reserves. However, VAPEX suffers from low production rates because of the slow mass transfer and inefficient gravity drainage. CSI benefits from solution gas drive and foamy-oil flow; however, solvent gas release would cause the viscosity of the diluted oil to re-increase to slow down the oil flow. In addition, the oil rate of CSI might be uneconomical due to the long no-production injection/soaking period. This paper proposes a new process, named foamy-oil assisted VAPEX, to enhance the heavy oil recovery. This process applies the same production mode as a traditional VAPEX except the model pressure is reduced cyclically to induce foamy-oil flow. Laboratory experiments are conducted to analyze the new process and compare it with VAPEX under the same well pattern and similar physical conditions. Sandpack permeability is about 5 Darcy. Propane is used to recover a heavy oil sample with a viscosity of 5,875 cP. For VAPEX, model pressure is kept at ~800 kPa. For foamy-oil assisted VAPEX, pressure control in each cycle consists of two periods: a constant pressure period and a pressure reduction period. Results show that a strong foamy-oil flow can be induced through a pressure drawdown, which forms a foamy-oil zone. The foamy-oil flow pushes the solvent-diluted heavy oil inward the solvent chamber, which not only increases the oil production, but also helps solvent to contact the fresh heavy oil and accelerate the mass-transfer process. In the foamy-oil assisted VAPEX, oil that could not be produced in a traditional VAPEX by the gradititional force could be recovered in the foamy-oil assisted VAPEX process by a foamy-oil flow. Compared with a traditional VAPEX, foamy-oil assisted VAPEX can increase the oil production rate by 68.5 % and the final recover factor by 20.4%.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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