Compaction and clay content control mudrock porosity
Bibliographic record
Abstract
Mudrocks, ubiquitous yet poorly understood sedimentary rocks with significant variations in composition and physical properties, form seals for geological carbon dioxide and energy (e.g., hydrogen and methane) storage, repositories for radioactive waste disposal, and reservoirs for natural gas. Understanding the controls on mudrock pore structure is essential for evaluating their porosity. The identification and quantification of controls depend on the nano-to micron scale pore network, which are the subject of this study. Small-angle (SANS) and very small-angle neutron scattering (VSANS) experiments were conducted on 13 diverse mudrock sets, characterised by differences in mineralogy, stratigraphy, maturity, and depositional environment. We performed multivariate statistics to systematically characterise the pore structure in 71 samples cross a 5 μm–2 nm pore size range. Our results indicate a multivariate approach more effectively captures the complex controls on porosity rather than single parameters. Compaction and clay content emerge as key primary and secondary controls on mudrock porosity, respectively, upon which we introduce a new porosity classification. Our complementary experimental-statistical assessment involving SANS-derived multiscale porosity sheds new light on the influence of structural controls on storage or production capacity in mudrocks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".