Application of nuclear magnetic resonance permeability models in tight reservoirs
Bibliographic record
Abstract
Abstract Tight reservoir permeability with values ranging from a few nD–0.1 mD is a challenging parameter to measure. Since the 1970s, many correlations were applied to estimate the permeability of tight formations using the nuclear magnetic resonance (NMR) technique. Due to increasing interest in tight reservoir recovery, each year many papers are published on the Timur‐Coates and Kenyon models and their modification on estimating the NMR permeability of tight reservoirs. It brings up the question of which is the most accurate, reliable, and feasible model to be used for predicting tight NMR permeability. The first part of this paper is dedicated to introducing the existing models for NMR‐based estimates of tight reservoir permeability. The second part compares these models by applying them on tight reservoirs from North America, Asia, and Europe. NMR experiments were conducted on 150 cores. NMR T2 relaxation times were measured and effective and total porosities, the geometric mean of relaxation time (T2gm), irreducible bulk volume (BVI), and free fluid index (FFI) were calculated. For validation of the models, the cross plots of NMR permeability versus the Klinkenberg gas permeability are presented. Comparison of the model is based on the calculated standard error of these two independently measured permeabilities. This paper is beneficial in understanding existing tight reservoir NMR permeability models. The results can be used as a guide for choosing the best NMR permeability estimation model for tight reservoirs 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".