An Overview of Active Large-Scale CO2 Storage Projects
Bibliographic record
Abstract
Abstract CO2 Capture and geological Storage (CCS) is a technology that is available today and that can cost-effectively solve up to a quarter of the global Greenhouse gas (GHG) problem. CCS can be applied to any fixed, point-source of CO2, and will likely be most cost-effective when applied to large sources close to large sinks. While CCS has application in the oil and gas sector (both upstream and downstream), the largest sources of CO2 exist in the power sector. Oil and gas sources are typically less than one million metric tonnes per annum (mmtpa) CO2, whereas power sector sources are typically more than 5mmtpa CO2. Hence a large-scale sequestration project should store in the order of lmmtpa CO2. Around 30mmtpa CO2 is being injected into EOR projects, mostly in the USA and Canada. Those EOR projects are being managed to recover and re-inject the CO2 (that they have to buy), rather than sequester it - little or no monitoring is carried out for the purpose of assuring CO2 geological storage As of today, there are only 4 large-scale projects on the planet which sequester anthropogenic CO2 on the lmmtpa-scale: Sleipner (Norway), In Salah (Algeria), Weyburn-Midale (Canada) and Snøhvit (Norway). Of these the two most significant (in terms of cumulative volume injected and experience of CO2 storage) are Sleipner (which has been in operation for 13 years) and In Salah (5 years). Weyburn-Midale is a CO2 EOR project involving CO2 cycling and monitoring. Although a portion of the cycled CO2 will be permanently stored, the primary objective of the project is to recover EOR oil. Snøhvit is relatively new (starting injection in 2008) and has not yet stored a significant volume of CO2. We focus therefore on the experience from the two large and mature projects Sleipner and In Salah. These two projects both capture CO2 produced during natural gas processing and store CO2 in deep saline formations. For both projects, the storage was part of the integrated Field Development Plan. They were both permitted under hydrocarbon law, and they illustrate significantly different aspects of storage: technical and commercial.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".