Bibliographic record
Abstract
Summary For decades the effect of physical dispersion (in-situ mixing) in porous media has been of interest in reservoir engineering and groundwater hydrology. Dispersion can affect the development of multi-contact miscibility and bank breakdown in enriched gas drives and miscible solvent floods of any mobility ratio. The magnitude or extent of dispersion is quantified by the rock property physical dispersivity (α) which is the order of 0.01 ft for consolidated rocks and several times smaller for sand-packs, from many laboratory measurements. Numerical studies of the effect of dispersion on enriched gas drives and field tracer tests often use input values of dispersivity 100 to 1000 or more times larger than ~0.01 ft. These large input dispersivity values stem from large apparent dispersivities (αa) determined by matching the one-dimensional convection-diffusion (1D CD) equation to production well effluent tracer concentration profiles observed in field tracer tests. The large apparent dispersivities reflect conformance or other behavior not governed by the 1D CD equation and should not be used to justify large physical dispersivity as input to numerical studies. This paper shows that large apparent dispersivities observed in field tests can result with physical dispersivity no larger ~0.01 ft lab-measured value. Heterogeneity alone (no physical dispersivity or molecular diffusion) causes no in-situ mixing and cannot explain observed large apparent echo dispersivities. Large apparent echo dispersivities for two reported field tracer tests are shown to result from the effect of drift alone with no dispersion. The widely reported scale-dependence of apparent dispersivity is a simple and necessary consequence of mis-applying the 1D CD equation, with its single parameter of Peclet number L/α, to conformance it does not describe. Apparent dispersivity is scale-dependent but physical dispersivity is a rock property independent of scale and time.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".