A New Approach for Permeability Prediction With NMR Measurements in Tight Formations
Bibliographic record
Abstract
Summary Prediction of extremely low permeability in tight reservoirs poses major challenges with traditional methods. Several studies have proposed nuclear magnetic resonance (NMR) permeability predictors, but these often give large errors when applied in tight formations. In this report, we describe a new method with NMR well-log measurements that decomposes the T2 spectrum into, at most, three Gaussian components. On the basis of parameters from the decomposition, we build a pore-size-based lithofacies model to predict whole-core horizontal permeability. With these parameters, we also modify the empirical Timur-Coates equation (TIM) to predict permeability. The NMR decomposition allows us to predict proportions of shale and silt. Applied to the tight Cardium formation, the parameters correlate strongly with core image and X-Ray-diffraction (XRD) results. In addition to Cardium data, we apply our approach to published data sets with good results, showing that the model gives accurate lithofacies-proportion estimates. To calibrate the model, Cardium probe permeameter data are used to identify facies permeabilities. Arithmetic-averaged permeability with the NMR-based model was calculated to compare with whole-core horizontal permeability. Monte Carlo analysis confirms the agreement between the model and core-permeability values. Our model provides a “bridge” to relate permeability between the probe scale (<1 cm laminations) and core size (>15 cm thin beds). Without the NMR well-log decomposition, Cardium TIM permeability predictions are in error by more than one order of magnitude in most intervals. The major challenge with the TIM model is obtaining an accurate T2 cutoff value. Compared with core-measured bound-water saturations, the default 33 ms value is too large for our tight samples. Our NMR decomposition, however, shows good correlation with measured bound-water saturations. With several core samples and NMR parameters, we modified the TIM model and found that it provides very good permeability predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".