Permanent Seismic Reservoir Monitoring for Real-time Surveillance of Thermal EOR at Peace River
Bibliographic record
Abstract
Summary Permanent seismic reservoir monitoring (PSRM) solutions, if of high enough sensitivity and low enough cost, can be used to tackle the many known problems faced by seismic monitoring onshore and thereby increase the profitability of such developments. Here we focus on thermal EOR monitoring using continuous seismic, as provided by SeisMovie®, a registered trademark of CGG. We review the PSRM staircase that Shell has climbed since 2009, introduce the most areally extensive deployment at Peace River in Alberta, Canada, and discuss some of the initial findings and plans ahead. We show progress with PSRM to generate better onshore data that will lead to higher recovery, higher production, and safer and cleaner operations. Significant steps were made towards on-demand, lower footprint PSRM, and next steps are set towards cheaper, non-intrusive automated systems that are required for broad application.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".