Site-Specific Seismic Challenges in the Early-Stages of a Bioenergy with CCS Project in Saskatchewan, Canada
Bibliographic record
Abstract
Summary We present a case study of the early stages of the storage development of an onshore Bioenergy with Carbon Capture and Storage project in Saskatchewan, Canada. The focus is on how 2D seismic is used to help determine the project’s suitability, siting, risk management, and Initial Characterization. We highlight the intersection of site specificity, economics and data availability. The project is locally unique due to its modest scale, proximity to sedimentary provenance in the storage complex— the Deadwood formation—its shallow depth and paucity of both wells and modern log data, including a complete lack of sonic logs. These site-specific issues increased the need for seismic imaging, while at the same time creating challenges for an accurate seismic interpretation. The lack of sonic logs was an exigent problem, necessitating the creation of an ad-hoc method for creating pseudosonic and pseudo-synthetic seismic and seismic models. The method was more successful than expected and resulted in synthetic models of sufficient quality to inform the location and magnitude of erosion of the DDWD seal section on the 2D seismic. In turn, this led to the critical decision to relocate the site and optimized subsequent efforts in the Initial Characterization stage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".