Enhanced estimate of fracture network dimensions by injection of non-Newtonian fluids
Bibliographic record
Abstract
Lugeon tests, also known as water pressure tests, are widely used to estimate the transmissivity of rock-fracture networks and are considered standard testing for grouting of dam foundations. The Lugeon test consists of injecting water into an isolated borehole section intersecting several fractures and monitoring water pressure and flow rate over time to estimate the rock fractures’ transmissivity. An average fracture aperture for the whole section is then estimated from the transmissivity value, which is used for selecting the appropriate grout mix. However, the current procedure does not provide any information on the variability in aperture sizes within the investigated rock interval. This paper presents a new approach for performing Lugeon testing that allows for providing a probabilistic distribution of fracture apertures by injecting bio-degradable non-Newtonian fluids at different pressures/flow rates. The theoretical framework demonstrating the ability to estimate the dimensions of multiple fractures, in parallel and in series, from non-Newtonian fluid injection tests is presented. Then, experimental results on different combinations of simple fractures, made of parallel plates, are used to validate the derived model and evaluate its performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".