Numerical Homogenization of Anisotropic Static Elastic Properties of Soft Mudrocks
Bibliographic record
Abstract
Soft mudrocks have low permeability and are mostly treated as sealing geological formations. Clay-rich soft mudrocks are also classified as the transversely isotropic (TI) material due to the intrinsic preferred fabric orientation. The TI elastic properties of soft mudrocks are highly dependent on the containing mineralogical compositions and clay fractions. Thus numerical modeling is necessary to estimate the behavior of soft mudrocks. In this study, a two-dimensional homogenization model is employed to assess the elastic moduli of soft mudrocks. Clay-water composites are treated as the hosting matrix, and non-clay minerals are placed in the REV as the inclusions. The inclusions are placed randomly in different sizes. In order to consider the partial flexibility between the non-clay minerals and clay-water composites, an imperfect boundary is defined between the inclusions and matrix using the eXtended Finite Element Method (XFEM). The periodic boundary condition is imposed on the REV, and the homogenized anisotropic elastic moduli of rocks with different clay fractions are estimated. The numerical results were validated using published experimental data on the static TI elastic properties of soft Colorado shale samples. The results show the predictable decreasing trends in elastic modulus of soft mudrocks with increasing of the clay fraction.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".