Detection threshold of a shallow CO2 plume with VSP data from the CaMI Field Research Station
Bibliographic record
Abstract
Reliable geophysical monitoring is needed for effective Measurement, Monitoring and Verification (MMV) of geological CO2 sequestration. The Containment and Monitoring Institute’s Field Research Station (CaMI.FRS) provides unique field data relevant to shallow injection and shallow leak detection for Carbon Capture and Storage (CCS) and Enhanced Oil Recovery (EOR) operations in sedimentary basins. CO2 injection at the FRS simulates a shallow leak from a deeper reservoir. VSP data were collected between 2017 and 2021 using geophones and Distributed Acoustic Sensing (DAS). Monitor surveys provided snapshots of the reservoir after injection of 7 t, 15 t, and 33 t of CO2. Permanently installed seismic sensors and repeated shot locations allowed for highly repeatable time- lapse surveys. Dissimilarity between baseline and monitor data was principally caused by seasonal variations in surface conditions and in near-surface filtering. A time-lapse compliant processing workflow was developed to detect the subtle amplitude effects of a small amount of CO2. The 10 Hz – 150 Hz field data required cautious processing, relying on deterministic deconvolution to properly scale and balance amplitudes across most of the 10 Hz – 150 Hz frequency bandwidth. Remaining spectral differences caused by near- surface filtering were removed with high-cut filters. The CO2 plume was confidently detected with geophone VSP data after 33 t of injection. Equivalent DAS VSP data was collected with different interrogator units for baseline and monitor surveys. This led to greater dissimilarity between datasets and ambiguous time-lapse results. Establishing a detection threshold of 33 t of CO2 approaches the limit of detectability in the geological setting at CaMI.FRS. These results provide insight into the challenges and capabilities of shallow reservoir monitoring and shallow leak detection for CO2 sequestration operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".