Estimating subsurface uncertainties by combining seismic interpretation with earth modeling
Bibliographic record
Abstract
Abstract Earth models are routinely used in the oil and gas industry to integrate multidisciplinary data for subsurface property predictions. Even though most earth models predict reasonably well at the field scale, they often fail to accurately predict the subsurface conditions at a specific location, especially in geologically complex reservoirs. Earth models can become more predictive by integrating information routinely extracted from 3D seismic such as faults, stratigraphy, facies, and rock properties. But their integration into earth models is often done without accounting for their uncertainties, potentially leading to the misprediction of the subsurface properties. We aimed to review several seismic interpretation techniques that provided useful input to better constrain earth models and to suggest ways to account for the interpretation uncertainty in these models. We determined the pitfalls and practical solutions for a successful quantitative seismic interpretation that can lead to more predictive earth models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".