Modeling anisotropic static elastic properties of soft mudrocks with different clay fractions
Bibliographic record
Abstract
ABSTRACT We have quantified the effects of clay fraction and fabric on the static elastic properties of soft mudrocks with emphasis on microlevel mechanisms. Soft mudrocks are treated as a mixture of nonclay minerals and clay-water composites. We have devised a simplified approach to estimate the fabric orientation distribution of soft mudrocks based on measured parameters such as clay fraction and porosity. A single parameter (fabric angle) that characterizes the fabric orientation distribution of soft mudrocks is related to the void ratio of clay-water composites. The static transversely isotropic (TI) elastic properties of soft mudrocks are modeled using an anisotropic differential effective medium approach. The effect of variation in fabric orientation distribution on the TI elastic parameters of clay-water composites is studied by applying the Voigt approximation. With an increase of clay fraction, soft mudrocks have decreasing trends in the deformation moduli because some nonclay minerals are replaced by clay-water composites. However, the deformation moduli of clay-water composites could increase when there is more anisotropy in the fabric due to an increase in the clay fraction. Thus, the correlations between anisotropic elastic moduli and volume fraction of clay-water composites will display some fluctuations. Such nonlinear relationships are validated against published experimental data on Colorado shale samples from the Western Canadian Sedimentary Basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 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".