Assessing Geologic Uncertainty of CO2 Sequestration Targets in the Jeanne d’Arc Basin
Bibliographic record
Abstract
The Jeanne d’Arc Basin, within the Grand Banks of offshore Newfoundland and Labrador, Canada, holds prolific oil and gas fields and is currently being assessed for its CO2 sequestration potential. Several factors, including the presence of existing infrastructure from conventional energy production, volume of available datasets, and favourable geologic conditions for storage, make the Jeanne d’Arc Basin and sequestration target areas in the basin attractive as CO2 injection sites. We are assessing subsurface geologic conditions in areas of interest for CO2 sequestration to determine the geologic risks and benefits of a variety of targets.The target strata for CO2 sequestration in this assessment are predominantly in post-rift sequences, sedimentary units that have not experienced complex extensional stress regimes and that mostly lack delineated vertical fluid flow pathways. However, a major consideration for targeting potential sequestration formations is understanding how sequestered fluids will behave and identifying possible migration pathways within and between the reservoir units. Understanding how the subsurface changes on a fine scale may become important for ongoing assessments, target ranking, and injection strategies. However, well data constraints are not distributed uniformly across the basin and seismic data are often too coarse to capture the fine details of the subsurface or are non-unique.This presentation documents factors that we are considering for geologic assessment of the CO2 storage potential in the Jeanne d’Arc Basin, such as changes in depositional regimes, fluid migration pathways (both vertical and horizontal), stress regimes and data quality/coverage. We also discuss the uncertainties and potential risk mitigation for storage targets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".