Hypergravity experiments on meter-scale porous media flow for geological carbon sequestration
Bibliographic record
Abstract
Reducing atmospheric CO 2 , 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 CO 2 behavior and tracking its migration underground. Laboratory-scale experiments that replicate subsurface storage conditions provide valuable benchmarks for validating numerical simulations. This study investigated CO 2 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 CO 2 entry into the sample. Afterwards, the injection rate was adjusted to 2 ml/min, and a gas cap formed, followed by continuous CO 2 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 CO 2 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 CO 2 sequestration research and provide quantitative datasets for benchmarking and validating numerical simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".