The Use of Distributed Acoustic Sensing for 4D Monitoring Using Vertical Seismic Profiles: Results from the Aquistore CO2 Storage Project
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) is a rapidly developing technology that employs optical fibers to detect acoustic disturbances in the subsurface. The Aquistore CO2 storage project uses time-lapse DAS VSP's to monitor injected CO2 in a deep geological reservoir (>3200 m) beneath the study site near Estevan, Saskatchewan, Canada. Reservoir monitoring is a crucial component to any geological storage project, and aims to ensure the safe and effective containment of the injected fluids, and evaluate the integrity of the storage and sealing units.An assessment of the ability of DAS VSP's to detect the CO2 response has been conducted for the Aquistore site. Prior to injection, fluid flow simulations were performed to predict CO2 distributions in the reservoir. These simulations were used to model the response of the CO2 plume in 2D time-lapse DAS VSP's. The expected responses were compared with estimates of time-lapse noise computed with field data from the baseline survey, and it was demonstrated that the plume would be visible in the reservoir after 27 kt of injection.After 36 kt of CO2 injection, the first monitor dataset was acquired. 4D VSP processing and imaging were applied to produce time-lapse difference volumes of the reservoir. Acceptable repeatability was attained, with normalized root-mean-square (nRMS) values less than 0.4 beneath the observation and injection wells. An anomaly in the lower part of the reservoir near the observation and injection wells was attributed to the replacement of brine with CO2.Updated fluid flow simulations were obtained that better replicated the injection parameters observed at the study site. Forward seismic modeling was then performed for 36 kt and 97 kt injection scenarios that reflected the 3D shot and receiver geometry in the field acquisitions. These data were processed using the same sequence applied to the field data to obtain comparable time-lapse difference volumes. Comparisons between the field and synthetic 36 kt anomalies were used to refine interpretations of the CO2 distribution in the reservoir. However, they also revealed the need to update the geological model to better reproduce the laterally asymmetric plume expansion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".