On the reliability of constraining surface conductivity using induced polarization measurements in sedimentary rocks
Bibliographic record
Abstract
SUMMARY Recent advances in understanding the induced polarization (IP) method have led to improvements in interpreting hydraulic properties from electrical measurements. Distinguishing the effect of surface conduction from conduction through the electrolyte filling the interconnected pore spaces has been an ongoing challenge in interpreting field-scale electrical resistivity data sets. Previously proposed mechanistic models have suggested that this limitation can be overcome by utilizing the coefficient that describes the ratio between IP measurements and surface conductivity. In this study, we examine this proportionality coefficient (ℓ) through the relationship between IP parameters (imaginary conductivity and normalized chargeability) and surface conductivity for a sample group of 98 sedimentary rocks, composed of sandstones, carbonates and mudstones. A strong linear relationship is observed between the IP parameters and surface conductivity. However, values of ℓ vary significantly across each sample group such that low-salinity estimates of formation factor (F) using the single universal estimate of ℓ are poor. Estimates of F for a single rock type (sandstone, carbonate or mudstone) are improved using ℓ values unique to that rock type, although F estimates for mudstones show high sensitivity to changes in ℓ. Using ℓ coefficients calibrated on a sample group with similar lithological properties to the investigated group moderately improves the estimation of F. Consistent with theory, no relationship is observed between the proportionality coefficient and the measured petrophysical parameters of the porous medium. Our results suggest that, although IP measurements provide a valuable field-scale proxy for surface conductivity, improving petrophysical predictions (i.e. in this case, estimating F) remains a challenge.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".