Permeability Estimation in Chalk Using NMR and a Modified Kozeny Equation
Bibliographic record
Abstract
An NMR logging-based permeability estimator was implemented for chalk. Several authors have identified the inability of accurate prediction of permeability from NMR logs in carbonates. Current models (namely Coates and SDR) may yield unreliable permeability data and require extensive calibration of parameters. Also, calibration requires data from core analysis, which undermines one of the key advantages of the technology: minimizing the need of expensive coring runs. A modified Kozeny method is used for permeability estimation from NMR logs. Unlike the model of Coates and SDR, does not require calibration. To translate the T2 relaxation distribution into pore size, an analogy is made between the NMR T2 data and the MICP output. Specific surface data acquired by the Brunauer Emmett Teller method (BET) was used to aid the interpretation of the surface relaxivity. The model was tested in a chalk reservoir borehole in the North Sea, for which NMR logs and permeability data for 4 core plugs are available. Results achieved using the modified Kozeny equation are in better agreement with the Klinkenberg permeability of core plugs than both the SDR and Coates methods. They are also superior to the results found by the application of Kozeny's equation when used without input from NMR data.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".