Hypergravity experiments on meter-scale porous media flow for geological carbon sequestration
Bibliographic record
Abstract
Reducing atmospheric CO2, the main driver of global warming, is essential for climate sustainability. Geological storage offers a promising solution for large-scale, long-term sequestration; however, challenges remain in predicting subsurface CO2 behavior and tracking its migration underground. Laboratory-scale experiments that replicate subsurface storage conditions provide valuable benchmarks for validating numerical simulations. This study investigated CO2 migration using a geotechnical centrifuge at 50 G. The setup combined a pH-sensitive solution for visualization and a 12-sensor array for pressure monitoring during injection. Multilevel pressure data and image sequences were analyzed across early, middle, and late stages of the test. The early period was marked by a pressure increase associated with CO2 entry into the sample. Afterwards, the injection rate was adjusted to 2 ml/min, and a gas cap formed, followed by continuous CO2 vertical and lateral migration. During the mid-stage (5 ml/min), flank pressure declined by 0.07–0.08 kPa/s, while the drop in the central sensors was approximately 0.11 kPa/s. At a later period (10 ml/min), gravity-driven instabilities developed, followed by a second gas cap, and the pressure beneath seal $$\:S₁$$ increased from 81 to 108 kPa, followed by a dissolution-induced drop to approximately 92 kPa as the CO2 plume advanced into fault-bounded zones. Dimensionless numbers were used to assess flow regimes and transport mechanisms, as well as to evaluate model-to-prototype scaling laws across the test periods. These findings demonstrate the potential of centrifuge-based hypergravity experiments for CO2 sequestration research and provide quantitative datasets for benchmarking and validating numerical simulations.
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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".