Combined use of Optical-fiber DAS and a Permanent Seismic Source for Vertical Seismic Profiling Demonstrated at the Aquistore CO2 Storage Site
Bibliographic record
Abstract
Abstract Seimic reservoir monitoring is a method to monitor fluid displacement in the reservoir. Long-term monitoring to measure differences over several years has been successful recently. However, short-term monitoring to measure changes in nearly real-time is still challenging because the expected changes in such a short-term are small. Permanent reservoir monitoring might enable short-term monitoring because we can increase data repeatability since sources and receivers are permanently fixed at the same position. This method saves the acquisition cost once the equipment is deployed, but the number of sources and receivers is limited due to the high initial install cost. To address this challenge, we have demonstrated VSP monitoring with a combination of a permanent rotary source and DAS sensor. DAS can record a wavefield at very dense and extensive points along an optical fiber, but the quality is regarded as less than for conventional geophones. By comparing data recorded in 2015 and 2016, we investigated the improvement of the signal-to-noise ratio of DAS. Hourly repeatability was checked by arranging the waveforms by the acquisition time. The depth migrated image of the offset VSP extended the imaging are further away from the receiver well. Our study confirmed the complementary relationship between the permanent source and DAS acquisition. Combining these technologies might enable us to monitor small changes in the reservoir in the short-term.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".