Achievement and future prospects of the demonstration test of the DAS-VSP reservoir monitoring system using permanent seismic source（ACROSS）
Bibliographic record
Abstract
This paper focuses on a high accuracy permanent reservoir monitoring system that integrates a permanent seismic source named accurately controlled routinely operated signal system（ACROSS）and a fiber optic sensing technology called distributed acoustic sensing（DAS）. To evaluate the effectiveness and benefits of this system, we have conducted a DAS-VSP data acquisition demonstration test at the Aquistore CO2 storage site in Saskatchewan, Canada. We have acquired four monitoring data sets in this field since 2016 when ACROSS was moved to a location about 750 m away from the observation well. During data acquisition, ACROSS was remotely controlled from Japan to reduce the HSE risk and cost. We constructed an efficient data processing flow including ACROSS signal processing, data matching, VSP data processing and 4D noise suppression. A 4D response evaluation method was established using two different types of repeatability indexes. The data acquisition, processing and evaluation were successful and a high- repeatability seismic section was obtained. In addition, we performed advanced data acquisition using a wireline DAS method and data processing using reverse time migration（RTM）. Lastly, we compared the latest data processing results with 3D seismic monitoring results acquired in the same time and discussed future prospects of reservoir monitoring in a CCUS and EOR field. We think that our monitoring system will be implemented as a useful reservoir monitoring system, so we plan to continue associated research, including the preparation for the new data acquisition in 2022.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".