Magnetic-Resonance Imaging of Fines Migration in Berea Sandstone
Bibliographic record
Abstract
Summary Fines migration is a phenomenon of practical importance in the petroleum-production and drilling industry. The movement of clay particles, induced by incompatible aqueous-phase chemistry or high flow rate, obstructs pore throats downstream of the fluid flow, leading to permeability reductions that can be as large as two orders of magnitude. Magnetic-resonance-imaging (MRI) methods derived from the Carr-Purcell-Meiboom-Gill (CPMG) method (Meiboom and Gill 1958) can map T2 distributions in porous rocks, hence showing the spatial variation of the pseudo-pore-size distribution. In this work, the traditional water-shock experiment was used to mobilize clay particles in the aqueous phase flowing in Berea core plugs. Spin-echo single-point imaging (SE-SPI), a phase-encoding MRI method derived from the CPMG method, was used to determine spatially resolved T2 spectra of the samples, and therefore the pseudo-pore-size distributions. The shift in the T2 spectra of the core inlet and outlet showed opposite trends. The pore-size distribution of the inlet and outlet, inferred from T2 distributions, were shifted to larger and smaller values, respectively. Therefore, the average pore size was increased at the inlet of the core and reduced at the outlet of the core. This MRI method provides a new analytical approach to screen reservoirs for potential fines-migration problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".