Formation mechanisms of the capillary valve effect in porous media and its response characteristics under heterogeneous displacement
Bibliographic record
Abstract
Capillary valve effect (CVE) is a key microscale mechanism that is responsible for entrapping residual gas during water flooding in low-permeability gas reservoirs, and ultimately leads to low recovery of gas. To address this issue, this study developed a capillary tube model using the phase-field method within a finite element method framework, incorporating dynamic curvature, capillary pressure, and threshold capillary pressure to investigate the CVE formation and interface evolution during drainage and imbibition. The effect primarily originated from abrupt curvature transitions at the throat-pore transition region, inducing spikes in the local capillary pressure at the three-phase contact line (TPCL) and resulting in pinning and meniscus retraction. In drainage, a sharp increase in local capillary pressure marked the onset of CVE, causing the meniscus to be pinned at the TPCL. Under strong non-wetting conditions and high pore-throat aspect ratios, the threshold capillary pressure increased by approximately 70%–85%, thereby intensifying the effect. In imbibition, the inversion of the interface indicated the onset of CVE. Under weak wetting conditions and large aspect ratios, the threshold capillary pressure increased by approximately 30% and up to threefold, respectively, indicating a pronounced enhancement of CVE. In both types of displacement, variations in capillary number influenced the threshold pressure by less than 10%. These findings provide guidance for optimizing water flooding in low-permeability gas reservoirs. In areas with strong CVE, adjusting pressure differentials or using cyclic injection can help overcome pinning and reduce gas trapping, improving recovery.
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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 | 0.000 |
| 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".