On Permeability Prediction From Complex Conductivity Measurements Using Polarization Magnitude and Relaxation Time
Bibliographic record
Abstract
Abstract Geophysical length scales determined from complex conductivity (CC) measurements can be used to estimate permeability when the electrical formation factor F is known. Two geophysical length scales have been proposed: (1) the specific polarizability normalized by the imaginary conductivity and (2) the time constant multiplied by a diffusion coefficient . The parameters and account for the control of fluid chemistry and/or varying minerology on the geophysical length scale. We evaluated the predictive capability of two CC permeability models: (1) an empirical formulation based on or normalized chargeability and (2) a mechanistic formulation based on . The performance of the CC models was evaluated against measured ; and further compared against that of well‐established estimation equations that use geometric length scales. Both CC models predict permeability within one order of magnitude for a database of 58 sandstone samples, with the exception of samples characterized by high pore volume normalized surface area . Variations in and likely contribute to the poor model performance for the high samples, which contain significant dolomite. Two observations favor the implementation of the ‐based model over the ‐based model for field‐scale estimation: (1) a limited range of variation in relative to and (2) field measurements are less time consuming to acquire relative to . The need for a reliable field‐estimate of limits application of either model, in particular the model due to a high power law exponent associated with .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".