Interpreting a Pumping Test Influenced by Another Well in an Anisotropic Aquifer
Bibliographic record
Abstract
In confined aquifers the influence of neighboring active wells is often neglected when interpreting a pumping test. This can, however, lead to an erroneous interpretation of the pumping test data. This article presents simple methods to evaluate the transmissivity tensor and storativity of an anisotropic confined aquifer when there is an interfering well in the neighborhood of the tested well. Two methods have been developed depending on whether the tested well or the interfering well is the first in operation. These new methods yield better estimates of the hydraulic parameters than when the influence of the interfering well is neglected. These methods have then been used on data obtained from numerical models with an interfering well and the results have been compared to an analytical method that neglects the influence of the interfering well. The methods require knowledge of the pumping rate of the interfering well and the time elapsed since the pumping started in each well. If the interfering well started pumping before the tested well, the method does not require knowledge of the aquifer piezometric level at the beginning of the test, which is often unknown in this case. As for the method without interference, at least three monitoring wells (MWs) are needed, the position of which influences the accuracy of the estimated parameters. Some recommendations concerning MWs position have been given to get more accurate results according to the sought parameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".