Characterizing Spatial Heterogeneity of Hydraulic Conductivity Using Borehole NMR in a Complex Groundwater Flow System
Bibliographic record
Abstract
Abstract Borehole nuclear magnetic resonance (NMR) logging can yield estimates of hydraulic conductivity ( K ) in unconsolidated sediments. Previous studies focused on establishing petrophysical models relating NMR responses to K and calibrating model constants for optimized K estimation. However, research has yet to explore the potential of NMR logging to derive spatial K distributions, which would enable its utilization in numerical groundwater flow and transport models. In this study, we construct various spatial K models based on NMR logging data. Characterization of spatial heterogeneity between NMR logs is explored using: (a) geostatistical interpolation approaches, including ordinary kriging and indicator kriging, (b) a zonation approach using clustering with spatial constraints for improved extraction of zone geometry, and (c) a hybrid model of multi‐level spatial heterogeneity nesting a zonal representation with zonally kriged K . The representativeness of NMR‐derived spatial K models is evaluated by reproducing a permeameter‐based K profile at an unsampled location and by comparing the numerically simulated drawdown responses with field observations of ten pumping tests. Results reveal that the spatially associated zonation model can effectively represent the K patterns between boreholes. Incorporating intralayer heterogeneity further refines the characterization of K heterogeneity, achieving optimal drawdown predictions. More importantly, its drawdown prediction performance remains stable with a limited NMR data set. This study provides a framework for using high‐resolution NMR‐derived K profiles from multiple boreholes to characterize spatial heterogeneity at sub‐meter scales in a highly heterogeneous, layered geologic deposit.
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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 | 0.001 | 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".