Modeling Microscale Foam Propagation in a Heterogeneous Grain-Based Pore Network with the Pore-Filling Event Network Method
Bibliographic record
Abstract
Foam flooding is an efficient and promising technology of enhanced oil recovery that significantly improves sweep efficiency of immiscible displacement processes by providing favorable mobility control on displacing fluids. Although the advantages in flexibility and efficiency are apparent, accurate prediction and effective control of foam flooding in field applications are still difficult to achieve due to the complexity in multiphase interactions. Also, conventional field-scale or mesoscale foam models are inadequate to simulate recent experimental findings in feasibility of foam injection in tight reservoirs. Microscale modeling of foam behavior has been applied to further connect those pore-scale interactions and mesoscale multiphase properties such as foam texture and the relative permeability of foam banks. Modification on a microscale foam model based on a pore-filling event network method is proposed to simulate its propagation in grain-based pore networks with varying degrees of heterogeneity. The impacts of foam injection strategy and oil-weakening phenomena are successfully incorporated. Corresponding microfluidic experiments are performed to validate the simulation results in dynamic displacement pattern as well as interfacial configuration. The proposed modeling method of foam propagation in grain-based networks successfully captures the effects of lamellae configurations corresponding to various foaming processes. The results of the simulation suggest that the wettability of rock has an impact on the relevance between reservoir heterogeneity and the formation of immobile foam banks, which supports the core idea of the recently proposed foam injection strategy in tight oil reservoirs with severe heterogeneity, that of focusing more on the IFT adjustment ability of foam, instead of arbitrarily pursuing high-quality strong foam restricted by permeability constraints.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".