Experimental study on the effect of pressure decline rate on foamy oil flow characteristics in a heavy oil– <scp> CO <sub>2</sub> ‐C <sub>3</sub> H <sub>8</sub> </scp> system
Bibliographic record
Abstract
Abstract CO 2 ‐based cyclic solvent injection (CSI) processes have gained great interest and have been piloted to enhance heavy oil recovery sustainably. Although the performance of pure CO 2 injection is encouraging, it is still not commercially feasible. In order to improve the performance of the CSI process, CO 2 (72%) and C 3 H 8 (28%) were chosen to be mixed with a heavy oil sample, and then four pressure depletion tests were conducted to investigate the effects of decline rates on the enhanced oil recovery performance and foamy oil flow behaviour. The production behaviour can be divided into three phases: volume expansion production phase (7% contribution), foamy oil flow production phase (73% contribution), and solution‐gas drive phase (20% contribution). The experimental results showed that depletion rates positively correlated with oil recovery factors in the last two phases. Additionally, the foamy oil flow phase was further divided into two zones: Zone‐1 (bubble nucleation and growth dominant zone) and Zone‐2 (bubble coalescence and disengagement dominant zone). The results showed that higher pressure depletion rates lead to larger pressure differences which in turn resulted in an increasing bubble nucleation rate in Zone‐1. While coalesced bubbles in Zone‐2 lead to a stronger discontinuous gas flow and the relative permeability of the oil phase decreased sharply. Compared with relatively lower heavy oil recovery factors (19.4%) in pure heavy oil–CO 2 system, the heavy oil–CO 2 ‐C 3 H 8 system took advantage of a higher oil recovery factor (23.44%). Furthermore, the relatively lower average gas recovery factor and cumulative gas–oil ratio (cGOR) indicated that CO 2 can be sequested in heavy oil reservoirs more efficiently.
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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) | 0.000 | 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 | 0.001 |
| 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".