Improved Permeability Prediction Relations for Low-Permeability Sands
Bibliographic record
Abstract
Abstract This work addresses the problem of estimating Klinkenberg-corrected permeability from single-point, steady-state measurements on samples from low permeability sands. The "original" problem of predicting the corrected or "liquid equivalent" permeability (i.e., referred to as the Klinkenberg-corrected permeability) has been under investigation since the early 1940s — in particular, using the application of "gas slippage" theory to petrophysics by Klinkenberg.1 In the first part of our work, the applicability of the Jones-Owens4 and Sampath-Keighin5 correlations for estimating the Klinkenberg-corrected (absolute) permeability from singlepoint, steady-state measurements is investigated. We also provide an update to these correlations using modern petrophysical data. In the second part of our work, we propose and validate a new "microflow" model for the evaluation of an equivalent liquid permeability from gas flow measurements. This work is based on a more detailed application of similar concepts employed by Klinkenberg. In fact, we can obtain the Klinkenberg result as an approximate form of our result. Our theoretical "microflow" result is given as a rational polynomial in terms of the Knudsen number (the ratio of the mean free path of the gas molecules to the characteristic flow length (typically the radius of the capillary)). The following contributions are derived from this work: Validation and extension of the correlations proposed by Jones-Owens and Sampath-Keighin for low permeability samples. Development and validation of a new "microflow" model which correctly represents gas flow in low permeability core samples. This model is also applied as a correlation for prediction of the equivalent liquid permeability in much the same fashion as the Klinkenberg model, although our new model is substantially more theoretical (and robust) as compared to the Klinkenberg correction model.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".