A Laboratory Scale Drawdown Testing for the Determination of a Model Sand Pack Aquifer Hydraulic Parameters
Bibliographic record
Abstract
Naturally occurring hydrocarbon fluids and ground water are two major geological resources essential for both energy and domestic driven civilizations at all levels of human activities. These two resources are hosted by subsurface geologic media commonly called reservoirs or aquifers. The development and supply of these resources in a systematic and guaranteed manner requires knowledge of their subsurface environments of flow. In both ground water and petroleum engineering communities, the same diffusion type equation describes subsurface fluid flow and most importantly, the analytical solutions of these equations have been greatly exploited for both formation and resource evaluation purposes. This study uses a newly installed ground water flow unit at the Chemical Engineering Laboratory of Dalhousie University (Halifax- Nova Scotia Canada) to obtain the first ever laboratory-scaledrawdown testing data for the determination of the hydraulic conductivity of a model sand pack aquifer.Conventional plots of experimental data in this study have been found to be perfectly similar to those obtained using actual field data from field-scale drawdown testing. Accordingly, absolute permeabilities, hydraulic conductivities, skin factors and other theoretically deduced parameters based on rigorous mathematical analysis of drawdown test data have been found to be physically realistic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".