Rapid-repeat time-lapse vertical seismic profile imaging of CO2 injection
Bibliographic record
Abstract
ABSTRACT In a carbon capture and storage (CCS) project, monitoring CO2 during the injection period is crucial for ensuring the safety and effectiveness of the injection process. The real-time observation of CO2 behavior allows for adjustments to the injection parameters to enhance performance and enables the refinement of the reservoir simulation models to predict plume migration accurately. Achieving this with seismic surveys requires significantly shorter intervals between the surveys compared with the conventional monthly or yearly timescales. This study aims to identify an effective seismic monitoring method, encompassing acquisition design and an imaging technique, for capturing the short-term, dynamic subsurface changes resulting from CO2 injection. We present images generated through elastic full-waveform inversion applied to rapidly repeated time-lapse vertical seismic profile (VSP) field data. The data are acquired using a fixed source shooting across an injection plume at approximately 15 min intervals over several days. These images, created from a combination of sparse geophone data and densely distributed acoustic sensing (DAS) data, provide clear snapshots of transient subsurface changes near the injection well. The results are further validated using a synthetic inversion experiment, demonstrating that the combined geophone and DAS-VSP approach offers a cost-effective and informative monitoring solution for CCS projects.
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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 | 0.000 |
| 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 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".