Framework for the assessment of interaction between CO<sub>2</sub>geological storage and other sedimentary basin resources
Bibliographic record
Abstract
Sedimentary basins around the world considered suitable for carbon storage usually contain other natural resources such as petroleum, coal, geothermal energy and groundwater. Storing carbon dioxide in geological formations in the basins adds to the competition for access to the subsurface and the use of pore space where other resource-based industries also operate. Managing potential impacts that industrial-scale injection of carbon dioxide may have on other resource development must be focused to prevent potential conflicts and enhance synergies where possible. Such a sustainable coexistence of various resource developments can be accomplished by implementing a Framework for Basin Resource Management strategy (FBRM). The FBRM strategy utilizes the concept of an Area of Review (AOR) for guiding development and regulation of CO2 geological storage projects and for assessing their potential impact on other resources. The AOR is determined by the expected physical distribution of the CO2 plume in the subsurface and the modelled extent of reservoir pressure increase resulting from the injection of the CO2. This information is used to define the region to be characterised and monitored for a CO2 injection project. The geological characterisation and risk- and performance-based monitoring will be most comprehensive within the region of the reservoir containing the carbon dioxide plume and should consider geological features and wells continuously above the plume through to its surface projection; this region defines where increases in reservoir pressure will be greatest and where potential for unplanned migration of carbon dioxide is highest. Beyond the expanse of the carbon dioxide plume, geological characterisation and monitoring should focus only on identified features that could be a potential migration conduit for either formation water or carbon dioxide.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".