Chip-off-the-old-rock: the study of reservoir-relevant geological processes with real-rock micromodels
Bibliographic record
Abstract
We present a real-rock micromodel approach whereby microfluidic channels are fabricated in a naturally occurring mineral substrate. The method is applied to quantify calcite dissolution which is relevant to oil/gas recovery, CO2 sequestration, and wastewater disposal in carbonate formations - ubiquitous worldwide. The key advantage of this method is the inclusion of both the relevant substrate chemistry (not possible with conventional microfluidics) and real-time pore-scale resolution (not possible with core samples). Here, microchannels are etched into a natural calcite crystal and sealed with a glass slide. The approach is applied to study acidified brine flow through a single channel and a two-dimensional micromodel. The single-channel case conforms roughly to a 1-D analytical description, with crystal orientation influencing the local dissolution rate an additional 25%. The two-dimensional experiments show highly flow-directed dissolution and associated positive feedback wherein acid preferentially invades high conductivity flow paths, resulting in higher dissolution rates ('wormholing'). These experiments demonstrate and validate the approach of microfabricating fluid structures within natural minerals for transport and geochemical studies. More broadly, real-rock microfluidics open the door to a vast array of lab-on-a-chip opportunities in geology, reservoir engineering, and earth sciences.
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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 | 0.000 | 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) | 0.001 | 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".