Building a CCS Hub at Scale in North America
Bibliographic record
Abstract
Abstract Carbon Capture, storage (CCS) and utilization is an integrated solution that can help to abate against carbon dioxide emissions aiding to meet local and global net zero goals. A suite of technical solutions works to capture CO2 that is emitted, followed by compression into pipeline or barging systems that transport into storage sites. At these storage sites, CO2 is injected into rock formations that permanently sequester the CO2 for a long time. This paper discusses Shell's (refered as "company") ADIP-ULTRA solvent technology, which provides pre-combustion CO2 removal and has been applied to more than 500 units around the world, including the company's operated Quest project in Canada. Quest in Canada is one of the most mature CCS operations in the world having sequestered nearly 8MT so far safely. As part of this technology our company also currently licenses a post combustion capture technology across multiple industries including power, steel, cement, and refining. In this paper, we will share some of the successful ways of working to overcome barriers of development to create a CCS hub including aspects of successful capture, transport and storage (in underground reservoirs) at scale in North America.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".