How well can we predict permeability in sedimentary basins? Deriving and evaluating porosity–permeability equations for noncemented sand and clay mixtures
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Bibliographic record
Abstract
Abstract The permeability of sediments is a major control on groundwater flow and the associated redistribution of heat and solutes in sedimentary basins. While porosity–permeability relationships of pure clays and pure sands have been relatively well established at the laboratory scale, the permeability of natural sediments remains highly uncertain. Here we quantify how well existing and new porosity–permeability equations can explain the permeability of noncemented siliciclastic sediments. We have compiled grain size, clay mineralogy, porosity, and permeability data on pure sand and silt ( n = 126), pure clay ( n = 148), and natural mixtures of sand, silt and clay ( n = 92). The permeability of pure sand and clay can be predicted with high confidence (R 2 ≥ 0.9) using the Kozeny–Carman equation and empirical power law equations, respectively. The permeability of natural sediments is much higher than predicted by experimental binary mixtures and ideal packing models. Permeability can be predicted with moderate confidence (R 2 = 0.26– 0.48) and a mean error of 0.6 orders of magnitude as either the geometric mean or arithmetic mean of the permeability of the pure clay and sand components, with the geometric mean providing the best measure of the variability of permeability. We test the new set of equations on detailed well‐log and permeability data from deltaic sediments in the southern Netherlands, showing that permeability can be predicted with a mean error of 0.7 orders of magnitude using clay content and porosity derived from neutron and density logs.
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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 | 0.001 | 0.000 |
| 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 it