Integration of outcrop, subsurface, and microseismic interpretation for rock-mass characterization: An example from the Duvernay Formation, Western Canada
Bibliographic record
Abstract
Identifying and characterizing geomechanical domains is important for understanding how a reservoir will respond to hydraulic fracturing, including interaction with natural fractures to create new permeable pathways. We have used a rock-mass characterization approach, which describes the mechanical reservoir package by combining parameters of the intact rock, such as brittleness, with inferred geometry and density of natural fractures. Insights from outcrop observations are important to complement the interpretation of fracture geometry and density derived from subsurface data, to give a more complete understanding of natural fracture networks. This integrated approach is applied to a data set from the Duvernay play in Western Canada. A synthetic model of the subsurface reservoir is constructed using data from well logs, cores, and outcrop analogs. Numerical simulation of the response of the artificial rock mass to hydraulic fracturing is performed using a distinct element code. Independent validation of the model is obtained by achieving an agreement between the simulated microseismic response and the observed distribution of microseismicity during hydraulic fracturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".