CO2 Storage Assessment for Net Zero: Customized Play-Based Exploration Methodology and Datasets Integration, Offshore Newfoundland, Canada
Bibliographic record
Abstract
Summary From 2015 to 2022, several regional petroleum resource evaluations were conducted offshore Newfoundland. Data and results from these studies were integrated into a single database to quantify the CO2 storage potential in the area. The workflow integrated forward stratigraphic model results to construct a regional model of reservoir and seal distribution, creating prospectivity maps. Characteristics of the prospects, such as porosity, possible closure, and pressure, were evaluated using a regional 3D basin model. The oil industry’s “play-based” approach was adapted to build an initial portfolio of plays and prospects, sorted by various criteria. Regionally, 11 plays from Jurassic to Miocene age were identified. Seven are proven hydrocarbon plays, while four younger plays have no hydrocarbon discoveries. After classification, 27 leads with an average cumulative storage capacity of 92 Gt of CO2 were retained. Ten prospects with a potential storage volume of 43 Gt were selected for economic evaluation, with five chosen for detailed assessment. The study concludes that the CO2 storage potential is significant and exceeds needs. Detailed studies are necessary to convert identified leads into potential storage sites. The methods used for oil exploration were effective for this screening, and reusing previous analyses allowed efficient and confident project completion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".