A Review of Permeability-Prediction Methods for Carbonate Reservoirs Using Well-Log Data
Bibliographic record
Abstract
Summary The prediction of permeability in heterogeneous carbonates from well-log data represents a difficult and complex problem. Generally, a simple correlation between permeability and porosity cannot be developed, and other well-log parameters need to be embedded into the correlation. The first part of this paper covers an extensive review of the existing correlations in the literature. The use of porosity and other petrophysical properties of rock in permeability prediction is discussed for carbonaceous rocks. This discussion also covers the usefulness of a wide variety of correlations developed using pore-scale (Kozeny-Carman, percolation, and fractal models) to field-scale models (well logs). In the second part of the paper, a case study is presented. The data are obtained from a complex carbonate field in Oman. Conventional and nonconventional (mainly nuclear magnetic resonance, or NMR) well-log data are evaluated to seek the parameters reflecting a good correlation with permeability. After testing each independent variable against core permeability, the variables yielding the highest correlation coefficient (CC) are included in multiple regression analysis. Data collected from seven wells are used to obtain the permeability correlations for the whole field and for four geological units separately. The test of the correlations is achieved through the comparison of the estimated permeability values to core permeability. Finally, the correlations are compared with the core permeability of the eighth well (data from this well are not included in the development of the correlation) for validation. The correlations are obtained for the four geological units. Two of these units responded well to conventional well-log data; the other two units yielded reasonable correlations only with NMR log data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".