Comparative investigation of water and gas flooding in tight oil reservoirs: a pore-scale perspective
Bibliographic record
Abstract
ABSTRACT: Tight oil reservoirs present significant challenges for efficient hydrocarbon recovery due to their low permeability and strong capillary effects. This study investigates the pore-scale difference of gas and water flooding by employing both capillary bundle and Lattice Boltzmann modeling. The results reveal that the relative efficiency of gas and water flooding is strongly dependent on pore scale, heterogeneity, and injection pressure. In homogeneous porous media, gas flooding outperforms water flooding at low to moderate pressures due to higher mobility and lower capillary resistance. At moderate to high injection pressures, water flooding gradually improves as capillary barriers weaken, leading to a transition in the optimal displacement strategy. The threshold pressure for optimal flooding significantly decreases with increasing pore size. In heterogeneous porous media, preferential flow paths intensify gas channeling, which weakens performance of gas flooding by causing inefficient displacement and early breakthrough. This further lowers the threshold pressure for water flooding, reinforcing its superiority in highly heterogeneous systems. LBM simulations further demonstrate that pore connectivity plays a crucial role in determining recovery efficiency, a factor not captured by traditional capillary bundle models. These findings highlight the importance of considering pore-scale heterogeneity and injection pressure constraints in enhanced oil recovery (EOR) applications.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".