Initial 4D seismic results after CO<sub>2</sub> injection start-up at the Aquistore storage site
Bibliographic record
Abstract
ABSTRACT The first post-CO2-injection 3D time-lapse seismic survey was conducted at the Aquistore CO2 storage site in February 2016 using the same permanent array of buried geophones used for acquisition of three previous pre-CO2-injection surveys from March 2012 to November 2013. By February 2016, 36 kilotons of CO2 have been injected within the reservoir between 3170 and 3370 m depth. We have developed time-lapse results from analysis of the first post-CO2-injection data and three pre-CO2-injection data sets. The objective of our analysis was to evaluate the ability of the permanent array to detect the injected CO2. A “4D-friendly simultaneous” processing flow was applied to the data in an effort to maximize the repeatability between the pre- and post-CO2-injection volumes while optimizing the final subsurface image including the reservoir. Excellent repeatability was achieved among all surveys with global normalized root-mean-square (Gnrms) values of 1.13–1.19 for the raw prestack data relative to the baseline data, which decreased during processing to Gnrms values of approximately 0.10 for the final crossequalized migrated data volumes. A zone of high normalized root-mean-square (nrms) values (0.11–0.25 as compared with background values of 0.05–0.10) is identified within the upper Deadwood unit of the storage reservoir, which likely corresponds to approximately 18 kilotons of CO2. No significant nrms anomalies are observed within the other reservoir units due to a combination of reduced seismic sensitivity, higher background nrms values, and/or small quantities of CO2 residing within these zones.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".