Multi-scale analysis of the influence of sedimentary fabric and composition on the geomechanical properties of organic-rich mudstones: a case study from the Duvernay Formation, Alberta, Canada
Bibliographic record
Abstract
In this research, the influence of sedimentary fabric and composition on the geomechanical properties of organic-rich mudstones has been investigated at the seismic, wireline log and core scale with resolution ranging from 50m- to the cm-scale. Our analysis reveals that a relationship is observable between composition, fabric, and elastic properties of the rock units at each scale of observation. Generally, carbonate-rich facies show the highest values of Young’s modulus (YM) and Poisson’s ratio (PR); clay-rich facies show the lowest YM and intermediate PR; biogenic silica-rich (“organic-rich”) facies show intermediate YM and the lowest PR. At the seismic scale, for which the resolution does not allow for assessment of intra-Duvernay mechanical heterogeneity characterization, significant difference in computed elastic moduli is observed between the organic-rich Duvernay Formation and its bounding strata which show different mineralogy. Analysis at the m- and cm-scale of compositional and geomechanical properties of the Duvernay Formation from outcrop samples reveals significant heterogeneity within the Duvernay Formation. Our research suggests that geological and geomechanical heterogeneity within the Duvernay Formation is facies-dependent. In fact, organic-rich mudstones show nearly twice as much variability in composition and mechanical hardness than carbonate-rich facies. This, in conjunction with our analysis of vertical and lateral geological heterogeneity within the Duvernay Formation from subsurface data, suggests that a cautionary approach should be adopted when using averaged or upscaled data for subsurface modeling which risk oversimplifying reservoir complexity. In this case, heterogeneity of rock properties should be included in reservoir models as a measure of uncertainty on the input data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".