Discrete Fracture Network (DFN) Analysis to Quantify the Reliability of Borehole-Derived Volumetric Fracture Intensity
Bibliographic record
Abstract
Volumetric fracture intensity (P32) is a parameter that plays a major role in the mechanical and hydraulic behaviour of rock masses. While methods such as Ground Penetrating Radar (GPR) are available to map the 3D geometrical characteristics of the fractures, the direct measurement of P32 at a resolution compatible with geotechnical applications is not yet possible. As a result, P32 can be estimated from the borehole and surface data using either simulation or analytical solutions. In this paper, we use Discrete Fracture Network (DFN) models to address the problem of estimating P32 using information from boreholes (1D data). When calculating P32 based on Terzaghi Weighting, it is common practice to use drill run lengths and limit the minimum angle between the borehole and the intersected fractures. The analysis presented in this paper indicated that limiting the minimum angle of intersection would result in an underestimation of the calculated P32. Additionally, the size of the interval has a significant impact on the variability of the calculated P32. We propose a methodology to calculate P32 using variable lengths, depending on the angle between the fractures and the borehole. This methodology allows the capture of the spatial variation in intensity and simultaneously avoids artificially increasing or decreasing the intensity sampled along borehole intervals. Additionally, this work has addressed the impact of boundary effects in DFN models and proposes a methodology to mitigate them.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".