Underground energy-related product storage and sequestration: site characterization, risk analysis and monitoring
Bibliographic record
Abstract
Abstract This paper presents a high-level overview of site characterization, risk analysis and monitoring priorities for underground energy-related product storage or sequestration facilities. The siting of an underground storage or sequestration facility depends on several important factors beginning with the area of review. Collection of all existing and available records and data from within the rock volume, including potential vulnerabilities such as prior containment issues, proximity to infrastructure and/or population centres, must be evaluated. Baselining of natural processes before storage or sequestration operations begin provides the basis for assessing the effects of storage or sequestration on the surroundings. These initial investigations include geological, geophysical and geochemical analyses of the suitability of the geological host rock and environs for storage or sequestration. A risk analysis identifies and evaluates threats and hazards, the potential impact should they develop into unwanted circumstances or events and the consequences to the facility should any of them occur. This forms the basis for framing effective mitigation measures. A comprehensive monitoring programme that may include downhole well surveillance, observation wells, geochemical sampling and well testing ensures that the facility operates as designed and that unforeseen issues, such as product migration or loss of integrity, can be identified and mitigated. In addition to these technical issues, human factors and public perception of a project are a critical part of the site characterization, construction and operational phases of a project. Despite differences between underground storage and sequestration, the characterization, risk analysis and monitoring approaches that were developed for underground natural gas storage or for carbon dioxide sequestration could be used for underground storage or sequestration of any type of energy-related product. Recommendations from this work include: (1) develop an industry-standard evaluation protocol (workflow) for the evaluation of salt beds, saline aquifers, depleted hydrocarbon reservoirs, underground mines and cased wellbores for potential underground storage or sequestration development beyond those in use today; and (2) develop an industry-wide collaborative process whereby incident and near-miss data related to underground storage or sequestration operations can be reported, documented and shared for use in refining risk analysis modelling.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".