Sketch-based modelling with flow diagnostics: Prototyping geomodels for better resource modelling decisions
Bibliographic record
Abstract
Summary Sketch-based modelling with flow diagnostics provides a prototyping approach to quickly build geomodels and generate quantitative results to evaluate volumetrics and flow behaviour. This approach allows users to rapidly test the sensitivity of model outputs to different geological concepts and uncertain parameters, and informs selection of geological concepts, scales and resolutions to be investigated in more detailed models. Rapid Reservoir Modelling (RRM) is a sketch-based modelling tool with an intuitive interface that allows users to rapidly sketch geological models in 3D. Geological models that capture the essence of heterogeneity of interest and related uncertainty can be created within minutes. Flow diagnostics then instantly computes key indicators of predicted flow and storage behaviour within seconds. Here we apply the prototyping approach to three aspects of geoenergy modelling: (1) scenario screening to identify heterogeneities with the most impact; (2) use of mini-models and hierarchical models to derive effective properties; and (3) training of geoscientists and engineers to investigate the impact of geological interpretations on storage volumes and connectivity. Geomodels addressing all three aspects are constructed and analysed quickly, using simple, geologically intuitive workflows that do not require prior geomodelling expertise.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".