Model and application of CO2-EOR injection and production parameters for high pour-point oil reservoirs: A case study of SUBEI A reservoir
Bibliographic record
Abstract
Addressing the poor waterflooding performance (characterized by high injection pressure and high water cut) in the high-viscosity oil reservoir of Block A, Eastern China, this study systematically investigated the impact mechanisms of CO 2 flooding injection-production parameters via PVT experiments, numerical simulations, and multi-factor optimization. Results demonstrate that CO 2 exerts significant viscosity-reducing and swelling effects on high-viscosity crude oil, with its oil recovery efficiency being substantially higher than that of waterflooding. Ranking of influencing factors using the Spearman correlation coefficient reveals that CH 4 can notably reduce the gas-oil ratio during CO 2 flooding; moreover, a well pattern with gas injection in the upper section and oil production in the lower section enhances the recovery rate to 23.28 %. Additionally, a recovery rate prediction model R f = 18.24 + 0.092 R + 0.068 S + 0.004 P 2 − 0.185 P − 0.063 V with a fitting degree of 97.6 % was established. This research provides a scientific basis for optimizing CO 2 flooding injection-production parameters in high pour-point oil reservoirs and offers valuable guidance for the development of analogous reservoirs. • Optimization of injection and production parameters for high-pour-point oil. • The spearman sorting algorithm was used to rank the influencing factors hierarchically. • The injection and production mathematical model of high pour point oil was proposed. • The model has a high prediction accuracy rate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".