A comparison of magnetic resonance imaging methods for fluid content imaging in porous media
Bibliographic record
Abstract
Quantitative measurements are important for imaging fluid content in porous media. Conventional MRI methods suffer from contrast because of relaxation times in porous media, resulting in measurements of apparent fluid content, not the true fluid content. We compare four magnetic resonance imaging methods for fluid content imaging in several water-saturated reservoir core plugs: frequency-encoded spin echo, single point ramped imaging with T1 enhancement, hybrid spin echo single point imaging (SE-SPI), and T2 mapping SE-SPI. 1-D profiles obtained with each method were compared in terms of image quality, image sensitivity, and quantification of water content. The image quality of short T2 lifetime samples suffered from blurring in hybrid SE-SPI images. Image sensitivity was the highest in the profiles obtained with frequency-encoded spin echo. The quantification of frequency-encoded spin echo, T2 mapping SE-SPI, and hybrid SE-SPI suffered in core plugs with a significant population of short T2 components because of T2 attenuation. Overall, single point ramped imaging with T1 enhancement was found to be the most general method for fluid content imaging.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".