Causal fracture prediction: Curvature, stress, and geomechanics
Bibliographic record
Abstract
We propose scaling volume curvature measurements with material property estimates to produce a superior prediction of natural fractures. Curvature is one of many, indirect, fracture-inferring attributes. It does not detect fractures, but is causally related to them through the assumption that increasing curvature relates to increasing strain. There are many other variables that are causally related to fractures. We propose that it would be advantageous to create combinations of these causal variables with curvature. Some of the most well known and important causes of variations in natural fracture density are material properties relating to brittleness. Material properties are critical geologically at all scales, from large-scale regional studies to prospect-level inquiries because the properties may vary significantly within individual formations and between formations. These vertical and lateral changes in material properties may be important and should be considered in fracture estimation, along with curvature. There is a lack of clarity regarding exactly which material property is best from the perspective of physics and rock mechanics; however, we have chosen a combination of parameters that we argue is a starting point. Fortunately, material properties are routinely estimated with amplitude variation with offset (AVO) techniques, and there is little practical reason not to use them together with curvature to produce a more complete attribute inferring fracture density. The combination of these variables is a step in the direction of creating quantitative causal fracture prediction estimates.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| 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".