Influence of a Shaky Start upon Overdamped or Underdamped Field Permeability (Slug) Test Results
Bibliographic record
Abstract
ABSTRACT ASTM D4044/D4044M-15A, Standard Test Method (Field Procedure) for Instantaneous Change in Head (Slug) Tests for Determining Hydraulic Properties of Aquifers (Withdrawn), describes several methods to start a slug test, which all involve some quick slugging of the water column in the riser pipe. Having a standard for the slugging action seems important. However, the initial dynamic effects are mostly ignored in slug test theories. In the field, slug tests may have a shaky start instead of a rapid and clean start. The question is, does the slugging quality influence the test results? This can be studied using recent tests for which shaky starts were registered by a pressure transducer (PT) and an atmospheric pressure transducer (APT). The PT–APT pair must be calibrated and synchronized, as explained in previous articles, but this is rarely done and regrettably it is not requested in standards. This article uses the data collected by PT–APT pairs for a few shaky starts in aquifers. Theoretical and practical aspects are explained. Then, this article examines the graphs that are needed for a correct analysis and compares results obtained with either a clean start or a shaky start in the same monitoring well. The test data are fitted with a three-parameter model for overdamped (OD) tests and a four-parameter model for underdamped (UD) tests. For OD tests, the start quality slightly influences the results for hydraulic conductivity, K, and piezometric level, PL, for the test. For UD tests, the slugging action may influence markedly the K value derived with the ASTM D5785/D5785M-20, Standard Practice for (Analytical Procedure) for Determining Transmissivity of Confined Nonleaky Aquifers by Underdamped Well Response to Instantaneous Change in Head (Slug Test), method. The analysis of residuals between data and best fit is very useful in selecting the correct starting time and avoiding the influence of slug dynamic effects and outgassing effects. Several recommendations are made to improve standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".