Advanced Analytical Model for Interpreting Oscillatory Pumping Tests With Wellbore Skin and Rate‐Dependent Skin Effects
Bibliographic record
Abstract
Abstract Oscillatory pumping tests presents a potential advancement in aquifer testing as they minimize hydraulic perturbations and result in no net water abstraction or injection. However, aquifer property estimates can be less accurate if they are compromised by well head losses during the pumping test. Currently, oscillatory pumping test models ignore these losses. The objective of this study is to develop and evaluate an analytical solution that can interpret oscillatory pumping test data affected by rate‐dependent skin losses. Accordingly, the sensitivity of the drawdown response to small variations in aquifer and well parameters was analyzed. In addition, a previous field aquifer test was analyzed to evaluate the feasibility of the solution for aquifer parameter estimation. The results indicate that neglecting the rate‐dependent skin effect can lead to substantial errors in the determination of aquifer parameters such as transmissivity and storativity. Additionally, the proposed solution effectively reproduces the sharp groundwater level peaks observed in field data, whereas the solution that does not consider the rate‐dependent skin effect greatly underestimates the peak values.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".