Numerical Evaluation of CO2-Based Enhanced Oil Recovery Approach Applied in a Heterogeneous Tight Oil Reservoir: Gas Channeling Alleviation and Parameter Optimization
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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.
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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 | 0.001 | 0.001 |
| 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 it