Assessing the potential to use repeated ambient noise seismic tomography to detect CO2 leaks: Application to the Aquistore storage site
Bibliographic record
Abstract
The Aquistore project in Saskatchewan, Canada provides carbon dioxide (CO2) storage for the world's first combined commercial power plant and carbon capture and storage (CCS) project. CO2 has been injected at a depth of 3.2 km since April 2015 and a permanent near surface geophone array provides passive seismic monitoring. The ability to identify any containment breach is a vital part of risk management and reduction for CO2 storage sites. We therefore investigate the potential to monitor seismic velocity changes following a hypothetical leak of CO2 from the reservoir using passive monitoring methods. We estimate the expected shear-wave velocity change with CO2 saturation, and using data from the geophone array we investigate whether ambient noise interferometry (ANI) and a tomographic inversion for Rayleigh wave group-velocity maps could provide a suitable CO2 leakage detection tool. To assess the repeatability of the method, we conduct, for the first time, a time-lapse ambient noise tomography survey of a CO2 storage site to cover time periods preceding and following injection start-up. Sensitivity analysis results indicate that usable surface wave data derived from the current array configuration are sensitive to depths of ∼400 m and shallower. We do not expect to observe any changes due to CO2 migration at such shallow depths and the estimated seismic velocities pre- and post-injection agree to within 60 m s−1, which is on the order of double the predicted velocity change with CO2 saturation. Therefore, due to uncertainties in travel-time picks (5–15%) and variations in the obtained velocity structure between consecutive days (up to 20%), we would be unable to resolve the expected seismic velocity change with an influx of CO2 at 400 m (∼3–4%). Additionally, the noise source variability does not allow stable velocity estimates to be made in the time-frame of currently-available data. Consequently, in the event of a CO2 leak at the Aquistore site, using the standard ambient noise analysis methods applied herein, Rayleigh wave tomography could be deployed to detect velocity changes due to CO2 saturation only if (a) a wider aperture surface array was in place to allow longer period surface waves to be used, providing sensitivity at greater depths, (b) arrival times of interferometrically-synthesised surface waves could be picked with increased accuracy, and (c) there is stability of the noise source distribution between repeated surveys. However, a map of three-dimensional near surface velocities, as obtained in this study, could nevertheless be useful for near surface static corrections when using active-source seismic reflection surveys to image and monitor the reservoir. More generally, further similar studies are required to assess the applicability of ANI for leak detection at other CO2 storage sites.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".