Foundations of geological carbon storage modelling and the Atlantic Canada context
Bibliographic record
Abstract
Geological Carbon Storage (GCS) is an essential climate mitigation strategy, enabling the long-term storage of carbon dioxide (CO₂) in deep subsurface formations. Offshore Atlantic Canada offers significant potential due to favourable geology, extensive subsurface data, and infrastructure from past hydrocarbon development. Major sedimentary basins such as the Scotian, Jeanne d’Arc, and Orphan contain structural traps, thick saline aquifers, and effective caprock seals that are critical for secure CO2 storage. Successful GCS depends on robust geological modelling workflows that incorporate subsurface heterogeneity, trapping mechanisms, and containment integrity. This paper reviews the foundational components of geological models - structural, stratigraphic, geometric, and topological frameworks – combined with numerical simulators to predict plume migration, pressure evolution, and geochemical interactions. Modelling supports all project stages, from site screening to post-injection monitoring, and is guided by parameters such as capacity, injectivity, containment, and storage efficiency. International offshore analogs such as Sleipner, Snøhvit, Northern Lights, Tomakomai, and Porthos provide valuable lessons in infrastructure reuse, regulatory development, and public engagement. These projects highlight the importance of tailored monitoring and verification plans, hub-based infrastructure models, and early-stage demonstration projects to build public trust. Offshore Atlantic Canada faces unique challenges including complex structural geology, overpressure zones, and salt tectonics, necessitating detailed technical evaluation. Recommended actions include high-resolution geologic modelling, probabilistic capacity assessments, and the creation of a regional carbon storage atlas. As regulatory frameworks evolve and carbon management becomes increasingly urgent, offshore Atlantic Canada is well-positioned to become a leader in safe, large-scale geological CO₂ storage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".