Field determination of mechanical aperture, entry pressure and relative permeability of fractures using NAPL injection
Bibliographic record
Abstract
An in situ field technique has been developed and implemented to measure non-aqueous phase liquid (NAPL) entry pressure and subsequently estimate mechanical aperture variability in an undisturbed, water-saturated rock fracture. The field experiment also provided the opportunity to measure wetting phase relative permeability at residual non-wetting phase saturation, kw,r(Snw,r). The radial aperture determination by the injection of oil (RADIO) method employs a constant-rate injection of a non-toxic NAPL into a fracture isolated by a double packer array. Injection pressures are recorded to provide a characteristic signature that is interpreted to reveal aperture enlargements and constrictions several metres from the borehole. Fracture aperture values are calculated by reverse modelling of the pressure signatures using the FRACAS fully compositional two-phase flow model. Pre-NAPL injection hydraulic testing established a baseline dataset of hydraulic apertures ranging from 26 μm to 259 μm with a mean value of 86 μm. Post-NAPL injection hydraulic testing gave kw,r(Snw,r) values ranging from 0·04 to 0·39 with a mean value of 0·15. Numerical modelling calculated fracture mechanical apertures vary from 36 μm to >1000 μm in test 1, and from 46 μm to >1000 μm in test 2. Single-value approximations of volume-averaged fracture apertures are two to three times larger than the respective hydraulic apertures, suggesting that volume balance calculations using the hydraulic aperture may overpredict NAPL penetration distances.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".