Comparison of <scp>NMR</scp> ‐Derived Hydraulic Conductivity with Various Hydraulic Testing Methods
Bibliographic record
Abstract
Borehole nuclear magnetic resonance (NMR) can be used to estimate the hydraulic conductivity (K) of unconsolidated materials. Various petrophysical models have been developed to predict K based on NMR response, with considerable efforts on optimizing site-specific constants. In this study, we assessed the utility of NMR logs to estimate K within highly heterogeneous glaciofluvial deposits by comparing them with K measurements from three types of co-located hydraulic testing methods, including permeameter, multi-level slug, and direct-push hydraulic profiling tool (HPT) logging tests. Four NMR models, including Schlumberger-Doll Research (SDR), Seevers, Sum-of-Echoes (SOE), and Kozeny-Godefroy (KGM), were applied to construct K profiles at four locations with model constants optimized using permeameter-based K. Model constants suitable for glaciofluvial deposits were provided. Results showed that NMR logging can provide reliable K estimates for interbedded layers of sand/gravel, silt, and clay. Through cross-hole comparison of NMR-derived K profiles, the trends and magnitudes of K for aquifers/aquitards were readily mapped. Quantitatively, the NMR-derived K coincided with hydraulic-testing K, with optimal model fits within one order of magnitude. We noticed that (1) Seevers performed similarly but no better than SDR in predicting permeameter and slug testing measurements; (2) SOE yielded slightly better predictions than SDR; (3) the removal of porosity in SDR did not deteriorate its prediction, and the optimized SDR constant resembled the literature-based values for glacial deposits; and (4) KGM yielded the optimal fits with slug-based K, demonstrating its reliable performance. Lastly, we made recommendations on selecting suitable petrophysical models.
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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) | 0.000 | 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 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".