Micromechanical characterisation of overburden shales in the Horn River Basin through nanoindentation
Bibliographic record
Abstract
Abstract The paper presents a micromechanical characterisation of Fort Simpson shale, which overlies unconventional gas-producing lithologies in the Horn River Basin, NW Canada. The Fort Simpson formation is clay-rich and microseismic data recorded during hydraulic fracturing events in the underlying reservoir has shown the formation acts as a barrier to fracture development, with a notably anisotropic seismic response. Samples were prepared from core fragments and the composition and texture of the shale was characterised using X-ray diffraction, mercury injection porosimetry and scanning electron microscopy (SEM). Nanoindentation testing was used to obtain the mechanical response of the shale microstructure, at grain-scale. The indentation was conducted on a grid pattern and samples were oriented both parallel and perpendicular to the bedding plane to assess the inherent mechanical anisotropy. Chemical analysis of the grids was also undertaken through SEM/EDS (energy dispersive X-ray spectroscopy) and the coupled chemo-mechanical data was used to characterise the material phases of the shale through a statistical clustering procedure. The results show that Fort Simpson shale broadly consists of a soft clay phase, with strongly anisotropic elastic stiffness, and stiffer but effectively isotropic grains of quartz and feldspar. A simple upscaling scheme was also applied to link the grain-scale elastic stiffness to the field-scale microseismic data.
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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 | 0.000 |
| 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".