Assessment of 4D seismic repeatability and CO2 detection limits using a sparse permanent land array at the Aquistore CO2 storage site
Bibliographic record
Abstract
ABSTRACT Two 3D time-lapse seismic surveys were acquired in 2012 and 2013 at the Aquistore CO2 storage site prior to the start of CO2 injection. Using these surveys, we determined the background time-lapse noise at the site and assessed the feasibility of using a sparse areal permanent receiver array as a monitoring tool. Applying a standard processing sequence to these data, we adequately imaged the reservoir at 3150–3350 m depth. Evaluation of the impact of each processing step on the repeatability revealed a general monotonic increase in similarity between the data sets as a function of processing. The prestack processing sequence reduced the normalized root mean squared difference (nrms) from 1.13 between the raw stacks to 0.13 after poststack time migration. The postmigration cross-equalization sequence further reduced the global nrms to 0.07. A simulation of the changes in seismic response due to a range of CO2 injection scenarios suggested that CO2 was detectable within the reservoir at the Aquistore site provided that zones of greater thickness than 6–13 m have reached CO2 saturations of greater than 5%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".