MODELING OF MULTISTEP DRAINAGE PROCESS USING THE EXTENDED INTERACTIVE TUBE-BUNDLE MODEL
Bibliographic record
Abstract
The relative permeabilities at low saturation and high capillary pressure are important for any drainage process such as CO2 storage and steam-assisted gravity drainage. The relative permeabilities can be estimated by analyzing the wetting phase production histories at each step in a multistep drainage process. Different from the conventional porous plate method, the multistep drainage process applies a plastic membrane to significantly reduce experiment time while prevent the nonwetting phase from being discharged from the porous medium The conventional tubebundle model has some difficulties in modeling this process because in a drainagetype process the sealing effect of the membrane significantly changes the multiphase flow pattern. In this paper, an extended interactive tubebundle model (ITBM) was developed to model this process. First, in order to qualitatively model this process, a new threetube interacting capillary model was developed and the reverse flood and bidirectional flood were properly modeled. This model also explains in concept why, in this process, the phase with lower mobility determines the wetting phase production history. After that, the threetube interacting capillary model was extended to a complex ITBM, consisting of hundreds of tubes. The saturation profiles along the porous medium, the wetting phase production curves, and the multistep drainage process were all successfully modeled. The application of the ITBM indicates that it can better represent the pore structure of a porous medium and potentially be applied to history match a multistep drainage 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".