Hydraulic Conductivity from <scp>Nuclear Magnetic Resonance</scp> Logs in Sediments with Elevated Magnetic Susceptibilities
Bibliographic record
Abstract
Abstract This study examined the application of slim‐hole nuclear magnetic resonance (NMR) tools to estimate hydraulic conductivity ( K NMR ) in an unconsolidated aquifer that contains a range of grain sizes (silt to gravel) and high and variable magnetic susceptibilities (MS) (10 −4 to 10 −2 SI). A K calibration dataset was acquired at 1‐m intervals in three fully screened wells, and compared to K NMR estimates using the Schlumberger‐Doll research (SDR) equation with published empirical constants developed from previous studies in unconsolidated sediments. While K NMR using published constants was within an order of magnitude of K , the agreement, overprediction, or underprediction of K NMR varied with the MS distribution in each well. An examination of the effects of MS on NMR data and site‐specific empirical constants indicated that the exponent on T 2ML ( n ‐value in the SDR equation, representing the diffusion regime) was found to have the greatest influence on K NMR estimation accuracy, while NMR porosity did not improve the prediction of K . K NMR was further improved by integrating an MS log into the NMR analyses. A first approach detrended T 2ML for the effects of MS prior to calculating K NMR , and a second approach introduced an MS term into the SDR equation. Both were found to produce similar refinements of K NMR in intervals of elevated MS. This study found that low frequency NMR logging with short echo times shows promise for sites with moderate to elevated MS levels, and recommends a workflow that examines parameter relationships and integrates MS logs into the estimation of K NMR .
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".