Geoenvironmental engineering aspects of carbon capture, utilisation, and storage
Bibliographic record
Abstract
The unprecedented carbon dioxide (CO2) concentrations in the atmosphere, followed by a global surface temperature increase above pre-industrial levels during 2011–2020, of about 1.1°C (over land 1.59°C), have put an urgency on the UN goal of attaining net zero emissions by 2050. Until the transition to clean energy sources is attained, carbon dioxide capture, utilisation, and storage (CCUS) remain a near-term, high-priority mitigation measure to control carbon dioxide emissions from fossil-fuel-based processes. The present article contributes to the topic of CCUS by assessing, initially, the maturity for industrial-level application of current carbon dioxide capture technologies. Subsequently, the advantages and limitations of geoenvironmental applications of carbon dioxide in the neutralisation of industrial by-products are detailed, as well as the use of carbon dioxide as a working fluid for geothermal heat extraction from abandoned oil and gas wells. The challenges of subsurface formation characteristics for the storage of carbon dioxide, with emphasis on geomechanical behaviour, are discussed. Injection of carbon dioxide into hydrate sediments constitutes another carbon dioxide storage option that can also allow the use of methane as an energy source. Finally, the paper analyses the liability issues of carbon storage projects and the challenge of assessing long-term risks to provide insurance coverage to them.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".