Measuring miscible fluid displacement in porous media with magnetic resonance imaging
Bibliographic record
Abstract
Abstract The development of new quantitative magnetic resonance imaging (MRI) technologies open new opportunities for measurements of mass transport in porous media. The current work examines a simple miscible displacement process of H2O and D2O in porous media samples. Laboratory measurements of dispersion in porous media traditionally monitor the effluent intensity of an injected tracer. We employ MRI to obtain quantitative water saturation profiles, and to measure dispersion in rock core plugs. The saturation profiles are modeled with PHREEQC, a fluid transport modeling program. We demonstrate how independent magnetic resonance measurements can be employed to estimate three important input parameters for PHREEQC, mobile porosity, immobile porosity, and dispersivity. Bulk Carr Purcell Meiboom Gill (CPMG) T2 distribution measurements were undertaken to estimate mobile and immobile porosity. Bulk alternating‐pulsed‐gradient‐stimulated‐echo (APGSTE) measurements were undertaken to measure dispersivity. The imaging method employed, T2 mapping Spin Echo Single Point Imaging (SE‐SPI), also provides information about the pore size distributions in the rock cores, and how the fluid occupancy of the pores changes during the displacement process.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".