Keynote: Comparing Benefits of CO2 storage and CO@EOR from a Climate Mitigation Perspective
Bibliographic record
Abstract
Summary Over the coming decades our society has a significant challenge in achieving globally significant reductions in greenhouse gas emissions. Numerous studies show that large-scale geologic disposal of CO2 from industrial emissions will be essential to achieve this objective. There are currently 21 large-scale CCS facilities in operation. Of these large-scale CCS projects, five use geologic storage in saline formations (Sleipner, Snehvit, Quest, IBDP & Gorgon) and together inject nearly 6 million tonnes CO2 per annum (Mtpa). The remaining large-scale projects mainly use CO2EOR as the storage vehicle. Enhanced oil recovery using carbon dioxide (CO2EOR) can have a dual purpose: (a) To recover additional oil, thereby supplying energy and additional revenues; and (b) to mitigate climate change by reducing anthropogenic CO2 emissions. Historically, CO2EOR projects have tended to maximize oil production as a function of the CO2 injected. There are various options proposed to enhance the climate mitigation effect of CO2EOR projects by maximizing the ratio of the CO2 injected to the oil produced, or by transiting projects from CO2EOR in the initial stages to pure storage projects in the later stages. However, to achieve net zero-emissions, CO2EOR projects need to include a significant fraction of non-EOR CO2 storage. CO2EOR projects also play an important role in building the infrastructure needed for large-scale carbon capture, utilisation, and storage. We illustrate these potential pathways using examples of large CCUS/CCS projects, both from offshore Norway and onshore Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.004 |
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".