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 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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".