Numerical Evaluation of CO2-Based Enhanced Oil Recovery Approach Applied in a Heterogeneous Tight Oil Reservoir: Gas Channeling Alleviation and Parameter Optimization
Bibliographic record
Abstract
Abstract In this study, a numerical simulation approach was employed to conduct CO2 continuous gas injection (CCGI) and CO2–water alternating gas (WAG) processes in a heterogeneous tight oil reservoir. First, operation parameters of the CCGI technique, including injection pressure, injection rate, production-injection pressure difference, and well pattern, were optimized. The CO2 movement in low and high-permeability zones, light component extraction, and gas channeling were investigated. Then, both schemes were assessed under identical base conditions to investigate the impact of WAG on gas channeling and mitigate its negative influence. Finally, the CO2-WAG process is optimized by identifying the optimal WAG ratio, production pressure, and well distribution, followed by a comparative evaluation of all cases. The results indicate that CCGI achieves the best production performance with an injection pressure of 30 MPa, an injection rate of 50,000 m3/day, a production pressure of 6 MPa, and a well pattern of regular four spot. The CO2-WAG process significantly alleviates channeling, resulting in a 3.84% oil recovery factor (ORF) increment, and the production performance gets optimized under a WAG ratio of 1:2 and production bottom hole pressure of 2 MPa. The integrated optimization of CO2-WAG-regular seven spot coupled with infill well accomplished the highest ORF of 49.69% among the researched scenarios. This work supplies a deeper knowledge of gas channeling and parameter optimization in the CO2-enhanced oil recovery (EOR) process in the tight reservoirs and can be a guideline to carry out a prospective pilot test in the targeted reservoir in the future.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".