What does a slug test measure: An investigation of instrument response and the effects of heterogeneity
Bibliographic record
Abstract
We consider parameters determined by the inversion of slug‐test head recovery data with the homogeneous‐parameter model of Cooper et al. [1967] to be weighted spatial averages of transmissivity and storage defined at a smaller scale. We quantify the spatial averaging using a power‐average spatial filter expression. We determine the form of the filter function and the power exponent using numerically simulated slug‐test data. The filter function that describes how smaller‐scale transmissivity is weighted by slug tests displays an approximate 1/r2 behavior, with r the radial distance from the well. The radius of the cylinderical volume that is averaged by the slug test is inversely proportional to the square root of the storage coefficient (larger averaging volume with smaller storage). The power exponent grows from −0.19 to 0.345 as the ratio of the characteristic scale of the heterogeneity to the characteristic scale of the averaging volume grows, although a power exponent of zero, corresponding to geometric averaging, provides good results for most simulations. Our results show that while slug tests are useful to estimate transmissivity, they have dubious value for estimating storage coefficients. We find that the transmissivity estimate is unbiased and does not appear to be strongly influenced by storage properties. The storage coefficient estimate is, however, strongly influenced by the transmissivity and is biased. We investigate the interaction between storage coefficient and transmissivity by examining an analytical slug‐test model that contains two annular zones, each with distinct transmissivity and storage coefficient.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".