Improving Oil Recovery and Enabling CCS: A Comparison of Offshore Gas-recycling in Europe to CCUS in North America
Bibliographic record
Abstract
This paper presents a comparison of European offshore gas-recycling in the North Sea to an exemplar onshore carbon capture-utilization-and-storage project (CCUS) in North America. Natural gas recycling has a long and successful history in the North Sea; while North America has pioneered the utilization of CO 2 for enhanced oil recovery. With renewed interest in CO 2 EOR as a means of stimulating carbon capture and storage (CCS), a simple comparison of these two gas injection cultures illuminates the potential of CCUS to change the velocity of, and cost- of-entry for, large CCS projects in Europe. The comparison of Weyburn, a large CO 2 EOR operation in Canada, with Åsgard, offshore Norway, a large natural gas recycling operation, is based on a comparative CO 2 price of 70 USD per tonne and conservative oil price for the last decade of 70 USD per barrel. A hypothetical offshore CO 2 EOR scenario is described to illustrate how the revenue-expenditure ratios are similar for offshore and onshore projects – around 5:1 for the additional oil produced from acquired CO 2 . A nominal carbon tax of 35 USD per tonne increases this to 10:1, demonstrating the potential for CO 2 EOR to stimulate CCS. However, large upfront capital investments and a regional shortage of captured CO 2 are significant hurdles to offshore European CCUS. The comparison also suggests a CO 2 emissions-storage ratio of 2:1. While this is a low carbon footprint for oil, in order for these projects to have a zero carbon footprint, they would require a transition to significant associated storage. It follows that the role of CO 2 EOR in a European CCUS context is primarily to stimulate the role-out of capture and transport infrastructure, and to access to large offshore CO 2 storage hubs in the North Sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".