Surface-based modeling of 3D architectural elements controlled by near-wellbore modeling
Bibliographic record
Abstract
Reservoir models of deep-water channels rely upon low-resolution but spatially extensive seismic data, high vertical resolution but spatially sparse well log data and geomodeling methods. The results cannot predict architecture (Figure 1B) below seismic resolution or between well logs (Ringrose and Bentley 2021). Usually, the interpretations and attributes that provide constraints for modeling workflows cannot consider sub-seismic scale architecture, so standard modeling methods do not generate realistic models and constraining models to well and seismic data is problematic. Surface-based methods are better at depicting architecture. We hypothesize that we can leverage >5000 m of measured sections in the Upper Tres Pasos Fm. (Magallanes Basin, Chile) to demonstrate a process of predicting stratigraphic architecture with 1D information analogous to well data (Figure 1A-C). In this research, we perform near-wellbore modeling to generate multiple scenarios of channel stacking patterns constrained by machine learning. These results are the anchor points to correlate deep-water channels between wellbores using surface-based modeling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".