Mechanical Properties and Natural Fractures in a Horn River Shale Core from Well Logs and Hardness Measurements
Bibliographic record
Abstract
Abstract This paper addresses factors that influence rock mechanical properties and the development of natural fractures of Horn River Group, a major shale gas play, using cores and well log data of well Maxhamish D-012-L/094-O-15. The majority of natural fractures in the Horn River shale are narrow fractures, sealed with carbonate mineral. In this study, the formation of observed fractures is primarily determined by the lithology type, mineral composition, bed thickness, the lithology of the surrounding bed and the rock mechanical properties. The different cement types in the fracture aperture also control the fracture reactivation by hydraulic fracturing. Brittleness is one of the most important mechanical properties controlling fractures formation, because brittle shale is more easily fractured than ductile shale and fractures in brittle shale tend to perpetuate when the fracturing pressure is released. In this study, a hardness value measured by the Equotip Bambino 2 hardness tester is found to be a good proxy for the brittleness of shale layers. The hardness measured an Equotip Bambino 2 hardness tester and the brittleness calculated from well logs show a good correlation. An orthogonal regression method is applied to deeply investigate their relationship. For different members of the Horn River Group, the correlation relationship is different, which is mainly affected by the changes of the Si and Ca mineral content. As shown in this paper, the hardness measured by an Equotip Bambino 2 is a good indicator of the log-derived brittleness, because of its high resolution and more accuracy. A hardness value is also more sensitive to the mineral composition changing than a brittleness value. Based on a statistical analysis, the hardness more accurately predicts the distribution of natural fractures, so that when a hardness value is over 550, natural fractures will be developed. These relationships can also be used to predict natural fractures and the targets of hydraulic fracturing when dipole sonic and density well logs are not available.
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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 | 0.001 |
| 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".