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.
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 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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".