A multiscale approach to determine hydraulic conductivity in thick claystone aquitards using field, laboratory, and numerical modeling methods
Bibliographic record
Abstract
Abstract Characterizing the hydraulic conductivity (K) of aquitards is difficult due to technical and logistical difficulties associated with field‐based methods as well as the cost and challenge of collecting representative and competent core samples for laboratory analysis. The objective of this study was to produce a multiscale comparison of vertical and horizontal hydraulic conductivity (Kv and Kh, respectively) of a regionally extensive Cretaceous clay‐rich aquitard in southern Saskatchewan. Ten vibrating wire pressure transducers were lowered into place at depths between 25 and 325 m, then the annular was space was filled with a cement‐bentonite grout. The in situ Kh was estimated at the location of each transducer by simulating the early‐time pore pressure measurements following setting of the grout using a 2‐D axisymmetric, finite element, numerical model. Core samples were collected during drilling for conventional laboratory testing for Kv to compare with the transducer‐determined in situ Kh. Results highlight the importance of scale and consideration of the presence of possible secondary features (e.g., fractures) in the aquitard. The proximity of the transducers to an active potash mine (∼1 km) where depressurization of an underlying aquifer resulted in drawdown through the aquitard provided a unique opportunity to model the current hydraulic head profile using both the Kh and Kv estimates. Results indicate that the transducer‐determined Kh estimates would allow for the development of the current hydraulic head distribution, and that simulating the pore pressure recovery can be used to estimate moderately low in situ Kh (<10−11 m s−1).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".