Microfluidics Underground: A Micro-Core Method for Pore Scale Analysis of Supercritical CO2 Reactive Transport in Saline Aquifers
Bibliographic record
Abstract
Carbon sequestration in microporous geological formations is an emerging strategy for mitigating CO2 emissions from fossil fuel consumption. Injection of CO2 in carbonate reservoirs can change the porosity and permeability of the reservoir regions, along the CO2 plume migration path, due to CO2-brine-rock interactions. Carbon sequestration is effectively a microfluidic process over large scales, and can readily benefit from microfluidic tools and analysis methods. In this study, a micro-core method was developed to investigate the effect of CO2 saturated brine and supercritical CO2 injection, under reservoir temperature and pressure conditions of 8.4 MPa and 40 °C, on the microstructure of limestone core samples. Specifically, carbonate dissolution results in pore structure, porosity, and permeability changes. These changes were measured by X-ray microtomography (micro-CT), liquid permeability measurements, and chemical analysis. Chemical composition of the produced liquid analyzed by inductively coupled plasma-atomic emission spectrometer (ICP-AES) shows concentrations of magnesium and calcium in the produced liquid. Chemical analysis results are consistent with the micro-CT imaging and permeability measurements which all show high dissolution for CO2 saturated brine injection and very minor dissolution under supercritical CO2 injection. This work leverages established advantages of microfluidics in the new context of core-sample analysis, providing a simple core sealing method, small sample size, small volumes of injection fluids, fast characterization times, and pore scale resolution.
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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) | 0.001 | 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".