Monitoring oil displacement processes with <i>k</i>‐t accelerated spin echo SPI
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a robust tool to monitor oil displacement processes in porous media. Conventional MRI measurement times can be lengthy, which hinders monitoring time-dependent displacements. Knowledge of the oil and water microscopic distribution is important because their pore scale behavior reflects the oil trapping mechanisms. The oil and water pore scale distribution is reflected in the magnetic resonance T2 signal lifetime distribution. In this work, a pure phase-encoding MRI technique, spin echo SPI (SE-SPI), was employed to monitor oil displacement during water flooding and polymer flooding. A k-t acceleration method, with low-rank matrix completion, was employed to improve the temporal resolution of the SE-SPI MRI measurements. Comparison to conventional SE-SPI T2 mapping measurements revealed that the k-t accelerated measurement was more sensitive and provided higher-quality results. It was demonstrated that the k-t acceleration decreased the average measurement time from 66.7 to 20.3 min in this work. A perfluorinated oil, containing no (1) H, and H2 O brine were employed to distinguish oil and water phases in model flooding experiments. High-quality 1D water saturation profiles were acquired from the k-t accelerated SE-SPI measurements. Spatially and temporally resolved T2 distributions were extracted from the profile data. The shift in the (1) H T2 distribution of water in the pore space to longer lifetimes during water flooding and polymer flooding is consistent with increased water content in the pore space. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".