Time-lapse seismic performance of a sparse permanent array: Experience from the Aquistore CO2 storage site
Bibliographic record
Abstract
ABSTRACT A sparse areal permanent array of buried geophones was deployed at the Aquistore CO2 storage site in Saskatchewan, Canada. The purpose of this array is to facilitate 4D seismic monitoring of CO2 that is to be injected to the deep subsurface. Use of a sparse buried array is designed to improve the repeatability of time-lapse data and to economize the monitoring effort. Prior to the start of CO2 injection, two 3D dynamite seismic surveys were acquired in March 2012 and May 2013 using the permanent array. The objective of acquiring these data was to allow an assessment of the data repeatability and overall performance of the permanent array. A comparison of the raw data from these surveys and with a conventional high-resolution 3D vibroseis survey demonstrated that (1) the signal-to-noise ratio for the buried geophones was increased by 6–7 dB relative to surface-deployed geophones and by an additional 20 dB for dynamite relative to a vibroseis source, (2) the use of buried sensors and sources at this site did not appear to be significantly degraded by the effects of ghosting, (3) repeatability for the permanent array data was excellent with a mean normalized root-mean-square (nrms) value of 57% for the raw baseline-monitor difference, (4) the variance of nrms values was higher for shot gathers (18%) compared with receiver gathers (7%), and (5) the raw data repeatability was a factor of three improved over that of comparable surface-geophone data acquired at a nearby location. The use of a sparse buried permanent array at the Aquistore site has demonstrably achieved a reduction in ambient noise levels and overall enhanced data repeatability, both of which are keys to successful 4D seismic monitoring.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".