Storage Appraisal - Appendix A5.1 - Summary of Dynamic Modelling Scoping Studies
Bibliographic record
Abstract
This document is a supporting document to deliverable MS6.1 UK Storage Appraisal Final Report. It is Appendix A5.1:Summary of Dynamic Modelling Scoping Studies. The purpose of this preliminary work was to define a common approach for the dynamic modelling including the physical processes to be represented, modelling tools to be used, the definition of common/standardised parameters and a basis for the recommendations. This was achieved through an extensive literature review, investigation of modelling software and modelling assessments. The following main recommendations were made.Modelling gravity effects with a sufficiently fine grid where needed is important.The solubility of CO2 in brine and the effect of capillary pressure should normally be included in dynamic models, but the effect of diffusion is not likely to be significant.The effect of hysteresis on relative permeabilities will be required to model residual trapping as it may be an important trapping mechanism after injection has ceased for poorly confined structures.It was concluded that the bulk of the dynamic modelling could be performed isothermally with sufficient accuracy using the industry standard finite difference ‘black-oil’ simulator ECLIPSE100™, and appropriate PVT data input, which was defined. This solution has the advantage of speed over the ECLIPSE300™/CO2STORE module compositional combination. It was proposed that a streamline simulator, such as 3DSL™, be considered for simulation of fine scale models of Exemplar open aquifer units as this would enable greater detail to be modelled due to faster run speeds. Streamline simulation is particularly effective where modelling displacement is more important than pressure changes, as for open aquifers.It was also proposed that a single simulator, GEM™, be used for well injectivity and associated thermal and geomechanical sensitivity calculations.It was recommended that the most comprehensive set of consistent CO2/brine relative permeability and capillary pressure data available from a Canadian dataset be used for the modelling.
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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 | 0.027 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.607 | 0.198 |
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".