Evaluation of slim-hole NMR logging for hydrogeologic insights into dolostone and sandstone aquifers
Bibliographic record
Abstract
This study assesses the performance and limitations of slim-hole borehole nuclear magnetic resonance (NMR) technology from a hydrogeologic perspective in fractured, porous rock. NMR logging was carried out in dolomitic and sandstone bedrock boreholes at two research test sites in Ontario, Canada, where aquifer and aquitard units provide a range of clay contents as well as a variety of primary and secondary porosity types (e.g. discrete fractures, reefal structures, vugs and karstic conduits). Results were compared to core measurements, geophysical logs, and hydrogeophysical testing. The vertical response curve of the instrument tested was found to produce 60% of the signal from within a 0.2m span surrounding the measuring point. The repeatability of the total porosity measurements in stationary mode is excellent where the porosity is greater than 0.15. Below that threshold, repeatability is scattered at ±0.05 porosity about the mean, with the variability primarily within the clay- and capillary-bound fractions. The NMR porosity estimates agreed with core measurements to within ±0.04 porosity in both the dolostone and sandstone, but the correlation deteriorates in finely bedded lithologies, and where fracturing is present. Much of the discrepancy is attributed to scaling in a finely layered geologic sequence, as the core samples are much smaller than the entire volume measured with NMR probes. Data collection with the probe in motion (continuous logging) added variability to the response when compared to stationary recordings. Although broadscale trends were comparable, the details and depth-specific insights of the bound fluid fractions varied with logging rates. Overall, NMR provides a robust measurement of the bulk matrix porosity and pore size distribution of lithologies intersected, both of which are critically important parameters in understanding hydrogeologic conditions and contaminant distributions in layered sedimentary rock systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".