An Optimal Injection Scheme for Maximizing Oil Recovery in CO2 Miscible Flooding
Bibliographic record
Abstract
Abstract As an efficient enhanced oil recovery (EOR) technique, carbon dioxide (CO2) miscible flooding can greatly reduce the viscosity of oil and improve its mobility, and has great potential to achieve higher oil recovery. However, the disadvantage of CO2 flooding, when compared with waterflooding, is the relatively larger viscosity ratio between CO2 and oil. Under such unfavorable conditions, frontal instabilities, or viscous fingering, can easily develop. This may affect the performance of CO2 miscible flooding and result in less sweep efficiency and oil recovery. In the present study, nonlinear numerical simulations were conducted to model the CO2 miscible flooding in subsurface porous media. Both concentration-dependent diffusion and a varying dispersion that is closely related with flow rates were incorporated into the mathematical model. The development of frontal instabilities with time was simulated with highly accurate numerical methods. More importantly, to reduce the unstable displacement and improve sweep efficiency, a time-dependent injection rate involving periodic alternation of injection and extraction was employed. Different from the widely used constant injection rate, this time-dependent displacement rate led to different flow dynamics and sweep efficiency, although the amount of CO2 injected was the same. In particular, the effect of a cycle period on the propagation of CO2 was carefully examined. It was found that a longer period led to earlier breakthrough of CO2 and less sweep efficiency. However, a shorter period with faster alternation of injection and extraction had a stabilizing effect. In particular, a later breakthrough was achieved and higher sweep efficiency at breakthrough was obtained compared with that of a constant injection rate. This indicates that pulsed displacement through fast switching of injection and extraction has the potential to maximize oil recovery in CO2 miscible flooding. Introduction Miscible flooding is proven as an economical EOR process and can be used for a variety of different reservoirs. In CO2 miscible flooding, one of the most promising EOR techniques, the CO2 can become miscible with the oil when the pressure is higher than the minimum miscibility pressure (MMP).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".