Joint Estimation of Hydraulic and Poroelastic Parameters from a Pumping Test
Bibliographic record
Abstract
The coupling of hydraulic and poroelastic processes is critical in predicting processes involving the deformation of the geologic medium in response to fluid extraction or injection. Numerical models that consider the coupling of hydraulic and poroelastic processes require the knowledge of relevant parameters for both aquifer and aquitard units. In this study, we jointly estimated hydraulic and poroelastic parameters from pumping test data exhibiting "reverse water level fluctuations," known as the Noordbergum effect, in aquitards adjacent to a pumped aquifer. The joint estimation was performed by coupling BIOT2, a finite element, two-dimensional, axisymmetric, groundwater model that considers poroelastic effects with the parameter estimation code PEST. We first tested our approach using a synthetic data set with known parameters. Results of the synthetic case showed that for a simple layered system, it was possible to reproduce accurately both the hydraulic and poroelastic properties for each layer. We next applied the approach to pumping test data collected at the North Campus Research Site (NCRS) on the University of Waterloo (UW) campus. Based on the detailed knowledge of stratigraphy, a five-layer system was modeled. Parameter estimation was performed by: (1) matching drawdown data individually from each observation port and (2) matching drawdown data from all ports at a single well simultaneously. The estimated hydraulic parameters were compared to those obtained by other means at the site yielding good agreement. However, the estimated shear modulus was higher than the static shear modulus, but was within the range of dynamic shear modulus reported in the literature, potentially suggesting a loading rate effect.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".