Fractional Flow Analysis of Foam Displacement in Tight Porous Media with Quasi-Static Pore Network Modeling and Core-Flooding Experiments
Bibliographic record
Abstract
Fractional flow analysis is an efficient tool to evaluate the gas-trapping performance of foam in porous media. The pore-scale simulation study and the core-scale experimental work have been bridged via the fractional flow analysis to distinguish the characteristics of foam displacement inside the tight porous media with varying absolute permeability, injection rate, and foam quality. In this work, the combined investigation suggests that conventional foam-enhancing strategies, pursuing higher foam quality and stronger foam regime, are inefficient and restricted in tight reservoirs that the critical Sw corresponding to the limiting capillary pressure has increased around 37~43%, which indicates severely weakened gas-trapping capacity as permeability reduces one order of magnitude. The moderate mobility adjustment and corresponding optimized fluid injectivity exerting from the “weak foam” flow presents a staged decline feature of decreasing water fractional flow, which implies the existence of the delayed gas-trapping phenomenon when water saturation reduces to 0.5~0.6. The finding has supported the engineering ideal of promoting low-tension gas (LTG) drive processes as a potential solution to assist field gas injection applications suffering from gas channeling. Also, the validation with core-flooding experimental results has revealed several defects of the current pore network model of foam displacement in tight porous media, including exaggerated gas trapping and overestimated confining water saturation. This study has innovatively demonstrated the feasibility and potential of optimizing the foam performance of gas trapping and mobility control in tight reservoirs, which provides a clue that may eventually boost the efficiency of the gas injection process in enhanced oil recovery or CO2 sequestration projects.
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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".