Permeability prediction from MICP and NMR data using an electrokinetic approach
Bibliographic record
Abstract
Abstract The accurate modeling of oil, gas, and water reservoirs depends fundamentally upon access to reliable rock permeabilities that cannot be obtained directly from downhole logs. Instead, a range of empirical models are usually employed. We propose a new model that has been derived analytically from electrokinetic theory and is equally valid for all lithologies. The predictions of the new model and four other common models (Kozeny-Carman, Berg, Swanson, and van Baaren) have been compared using measurements carried out on fused and unfused glass bead packs as well as on 91 rock samples representing 11 lithologies and three coring directions. The new model provides the best predictions for the glass bead packs as well for all the lithologies. The crux of the new model is to have a good knowledge of the relevant mean grain diameter, for example, from MICP data. Hence, we have also predicted the permeabilities of 21 North Sea well cores using all five models and five different measures of relevant grain size. These data show that the best predictions are provided by the use of the new model with the geometric mean grain size. We have also applied the new model to the prediction of permeability from NMR data of a 500 m thick sand-shale succession in the North Sea by inverting the T2 spectrum to provide a value for the geometric mean grain size. The new model shows a good match to all 348 core measurements from the succession, performing better than the SDR, Timur-Coates, HSCM, and Kozeny-Carman predictions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".