Investigation of CO<sub>2</sub>/N<sub>2</sub> injection in tight oil reservoirs with confinement effect
Bibliographic record
Abstract
Abstract This paper investigates the CO2/N2 injection process in tight oil reservoirs considering the confinement effect. To study the microscopic physical mechanisms, the confinement effect is characterized by properties shift and capillarity and introduced into the flash calculation to obtain the phase equilibrium of mixture fluids (tight oil/CO2/N2) in tight porous media. The results indicate that the injected nitrogen gas could effectively maintain the reservoir pressure, while it also weakens the effects of the CO2 injection recovery mechanisms, notably diffusivity and viscosity reduction. In addition, a dual‐pore tight oil reservoir model is set up to investigate the CO2/N2 injection with ultra‐low permeability and hydraulic fracturing. The basic CO2 injection parameters are optimized by the orthogonal method. Based on CO2 injection process, three injection schemes of CO2/N2 injection, which are mixed‐gas injection, CO2‐alternating‐N2 (CAN) injection, and N2‐alternating‐CO2 (NAC) injection, were investigated and a comparative analysis was made for the pressure distribution, CO2 mole fraction distribution, and cumulative oil production. Based on this analysis, the CAN injection process proved to be the best injection scheme. A parametric analysis further suggested that the nitrogen gas injection rate was the most important factor. Besides, the effect of gravity drainage, reservoir permeability, nature fractures, and permeability heterogeneity on the oil production of CAN injection process were also investigated in detail. The results show that tight oil reservoir with better vertical connectivity, poor fracture growth, and higher heterogeneity is more favorable for the CO2/N2 injection process.
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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 | 0.000 |
| 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 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".