Sketch modeling of seismic horizons from uncertainty
Bibliographic record
Abstract
Petroleum reservoir model building is a fundamental but complex task present in all stages of oil/gas exploration and production (E&P). Reservoir models are built incrementally using multi-disciplinary data (e.g. from geo-physics, geology, reservoir engineering) and the domain expert interpretation of that data. The first reservoir models are constructed at the appraisal stage, where the available data presents inaccuracies and a high degree of uncertainty. In this paper we present a set of sketch-based interface and modeling operators integrated in a system for the early appraisal stage in oil/gas E&P for the tasks of seismic interpretation and reservoir model building. Our system allows the user to sketch directly over the raw seismic reflection volume and its derived data. These data guide the expert in the key tasks of seismic interpretation and building the structural framework of the reservoir. We propose a novel set of sketch-based modeling operators designed by specific domain require-ments from geophysics and geology. A novel architecture using adaptive meshes is also developed to create a more flexible sketch-based system.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".