Porewater Content, Pore Structure and Water Mobility in Clays and Shales from NMR Methods
Bibliographic record
Abstract
Abstract Sub-surface clay samples are difficult to characterize using conventional methods so non-invasive Nuclear Magnetic Resonance (NMR) techniques were used to evaluate in a preserved state the pore structure, porosity, water mobility, and affinity of various clay systems. Within the CLAYWAT project launched by the NEA Clay Club, some of the most advanced NMR techniques were applied to samples from 11 clay-rich sedimentary formations (Boom Clay, Yper Clay (both Belgium); Callovo-Oxfordian shale, Upper Toarcian (both France); Opalinus Clay from two sites (Switzerland); Queenston Fm., Georgian Bay Fm., Blue Mountain Fm. (all Canada); Boda Clay (Hungary); and Wakkanai Fm. and Koetoi Fm. (Japan)). The degree of induration within this suite of samples varies substantially, resulting in a wide porosity range of 0.02–0.6. The key finding is the determination of pore-size distribution by NMR cryoporometry in the range of 2 nm–1 μm with the native fluid present in the pore space for most samples. The water volume in pore sizes of <2 nm could also be measured, thus providing a full description of the porosity system. A specific preparation by sample milling was applied to the preserved original cores minimizing disturbances to the samples in terms of water loss. The water content measured by NMR relaxation was comparable to values obtained by drying at 105°C. In general, the narrow T 2 distributions indicate that water was diffusing throughout the pore network during the magnetization lifetime, implying that T 2 distributions cannot be considered as proxies for the pore-size distributions. For the set of samples considered, the T 1 / T 2 varied between 1.7 and 4.6, implying variable surface affinity. Finally, for most samples, a pore-shape factor of ~2.4, intermediate between a sheet (1) and a cylinder (4), was deduced.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".