Geophysical and Geochemical Methods for In-situ CO2 Mineral Storage Site Characterization and Monitoring
Bibliographic record
Abstract
Summary In a pilot project in Helguvik, Iceland, in-situ CO2 mineral storage is tested for the first time using saline water instead of fresh water for injection in the field scale. We present three geophysical and geochemical techniques that are novel to in-situ CO2 mineral storage site characterization and monitoring. The baseline characterization, employing single-hole electrical resistivity tomography (ERT) and crosshole seismic measurements, revealed decameter-thick basaltic layers and allowed us to interfer the subsurface porosity and permeability distributions. Rock physics modelling predicts significant seismic velocity increases associated with secondary mineral precipitation, suggesting crosshole seismic time-lapse surveys as a valuable monitoring tool. Results of the ERT timelapse measurements show distinct variations between the baseline and the timelapse measurements, indicating potential resistivity changes due to secondary mineral precipitation and fluid substitution. Geochemical monitoring of a Helium tracer confirms that injected water has reached the monitoring well at 100 m distance, whereas the CO2 concentration in the same well shows no significant increase relative to the pre-injection state. This work demonstrates the potential of combined geophysical and geochemical methods for characterizing and monitoring in-situ CO2 mineral storage, highlighting ERT and crosshole seismics as valuable tools.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".