A Portfolio of Commercial Scale CCS Demonstration Projects
Bibliographic record
Abstract
Abstract Shell is progressing a portfolio of commercial-scale carbon capture and storage (CCS) demonstration projects covering an array of technologies that target applications of close relevance to the wider oil and gas industry. The portfolio includes projects such as Peterhead, Quest, Technology Centre Mongstad and Gorgon. A number of key learnings on both the technology deployment and critical project development aspects for the different project phases have been obtained. This paper provides an overview of these learnings with a specific focus on the issues faced by CCS project developers. CCS is currently recognised as the only technology available for mitigation of carbon emissions from large-scale fossil fuel use. Before the process can be widely adopted it must be demonstrated at scale end-to-end. Learnings for all different project phases from early assess through to operations of these demonstrators need to be captured and communicated. As additional facilities to existing hydrocarbon operations, CCS projects require an approach similar to the development of other oil and gas projects. To help enable and support other CCS projects, Shell is also committed to knowledge sharing from the projects, often agreed as part of the Knowledge Management provisions of the projects. One of the key observations provided from the demonstration portfolio is the need for regular and informative engagement with both the public and regulators as the project progresses. Early and successful demonstrations can provide the evidence required for regulators, project developers and the public to have the confidence to proceed with future CCS projects. It is also recognised that cost reduction will be key in driving commercial ‘deployability’ of CCS. The Quest and Peterhead projects are ideally placed to enable follow-on projects to learn and further reduce costs. The Shell portfolio of projects has also demonstrated that the drivers for technology optimisation can differ depending on the end user of the CCS technology. There is a need to demonstrate different technology aspects, for example flexibility or availability. This talk will focus on how these issues are being addressed in two of the different projects within the Shell CCS portfolio, and highlight the key lessons learned. Furthermore, the cost-related issues of CCS will be addressed.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".