Practical Qualitative and Quantitative Models in Carbon Capture Utilization and Storage Risk Assessment
Bibliographic record
Abstract
Abstract Carbon Capture Utilization and Storage (CCUS) has been recognized as a tool to aid the decarbonisation of carbon heavy industries, with the storage of CO2 in the subsurface over geological timescales being a key component in the CCUS process chain. Assessing and managing the risks involved in subsurface storage of CO2 is required so decisions can be made about the design, operation, monitoring and acceptability of potential projects. As with existing industrial activities, a number of approaches exist for assessing risk ranging from purely qualitative approaches through to quantitative approaches. A structured, proportional risk assessment approach is proposed, aimed at identifying, analysing and evaluating the risk from a candidate subsurface store through a combination of qualitative and quantitative techniques to gain the most benefit. Development of a register of subsurface containment risks allows for the identification of scenarios of concern, which can be coupled with risk matrices to provide an estimation of the risk level together with its acceptability. More in depth qualitative techniques such as bowtie analysis encourage different disciplines to collaborate and enhance communication, both internally and externally, of the controls present for the most significant risks. The combination of bowties with a quantified, event tree-based model can allow for numerical determination of leakage probability and magnitude. Quantitative methods can therefore estimate insurance liabilities and allow comparison of options or with acceptance criteria. However, these assessments are underpinned by the quality of inputs. Given that the CCUS industry is still in its infancy, data surrounding event likelihoods and leak magnitudes, especially for geological leakage pathways, can have large amounts of associated uncertainty; this uncertainty must be taken into account when evaluated the overall acceptability of projects. Whilst both quantitative and qualitative methodologies for risk assessing subsurface CO2 storage have their advantages and disadvantages, it is when combined as part of a structured CCUS risk assessment approach that the most benefit is gained.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".