Exploring dynamic effects in capillary pressure in multistep outflow experiments
Bibliographic record
Abstract
Traditional steady state experiments to measure constitutive relations governing two‐phase (organic‐aqueous) flow in the subsurface often extend over periods of weeks or months. Alternatively, one‐step or multistep outflow (MSO) experiments can be combined with application of a multiphase flow simulator and an optimization algorithm to achieve a more rapid technique for parameter estimation. In this work, MSO experiments were conducted to produce a data set for the estimation of two‐phase constitutive parameters using this inverse modeling approach. Examination of experimental results reveals significant discrepancies between observed and simulated outflow data, with simulated curves tending to approach equilibrium at a faster rate than the experimental observations. Similar behavior has been documented by other investigators. Application of alternative equilibrium constitutive models in the multiphase flow optimization simulator failed to improve model fits to observed data. However, when model governing equations were modified to incorporate a dynamic capillary pressure term, there was significant improvement in the agreement between measured and simulated cumulative water outflow and outflow rates. Comparisons of simulated and measured data further suggest that the dynamic capillary pressure constitutive coefficient depends on saturation. Attribution of the observed experimental deviations to dynamic effects in capillarity is also supported by the consistency of the fit equilibrium retention function with an independently measured static retention relation.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".