A Visual Analytics Framework from Geological Modeling to Reservoir Simulation
Bibliographic record
Abstract
Abstract Immersive technologies such as virtual reality has shown great potential in to enhance many important workflows the oil and gas industry. Immersive technologies have many natural advantages. These include enhanced spatial perception of 3D data, spatial user interfaces and interactions, the ability to provide collaboration space and incorporate analysis techniques. To take advantage of these technologies in the reservoir engineering domain, a visual analytics framework is established to demonstrate how these technologies may be used effectively. This framework allows a user to progress from geological modeling to reservoir simulation in an interactive and highly effective application. First, geological uncertainty analysis is conducted to screen representative realizations as candidates for simulation. Visualization and analysis of these representative realizations may then be performed in virtual reality using reservoir connectivity analysis of a tight oil reservoir. This application supports natural interactions, improved working space, and effective perception of underground connectivity. For this reason, it is more convenient and natural to work between a reservoir scale model and a set of candidate local realizations. This platform provides a basis for future data analysis methods and ways of interacting and visualizing data that support this analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".