Static connectivity of stacked deep-water channel elements constrained by high-resolution digital outcrop models
Bibliographic record
Abstract
ABSTRACT High-resolution digital outcrop models of stacked deep-water channel elements are constructed from the Laguna Figueroa section of the well-exposed Upper Cretaceous Tres Pasos Formation in Chilean Patagonia. The models are based on greater than 1600 m (>5250 ft) of centimeter-scale measured sections, greater than 100 paleoflow measurements, and thousands of differential global positioning system points (10-cm [4-in.] accuracy) from an outcrop belt that is approximately 2.5 km (∼1.5 mi) long and 130 m (425 ft) thick. The models elucidate the effects bed-to-geobody–scale architecture has on static sandstone connectivity among a series of stacked deep-water channel elements and how that connectivity is altered by grid cell size. Static connectivity analyses show that channel element base drapes can strongly influence sandstone connectivity and that smaller channel element widths are more likely to produce disconnected sandstone geobodies. Net-to-gross (NTG) is not directly correlated with connectivity because of the presence of thin channel element base drapes, which do not significantly contribute to NTG. Upscaling the models consistently increases channel element contact (up to 10%) but decreases sandstone connectivity (up to 2%–3%). Channel element stacking patterns strongly impact connectivity. For example, connectivity is reduced in cases of high lateral channel element offsets. Increasing drape coverage markedly decreases connectivity. Evaluating connectivity in a vertical, along-system profile is critical to understanding flow units and reservoir piping. Ultimately, this work constrains uncertainty related to the impact of subseismic-scale stratigraphic architecture on reservoir connectivity by providing concrete knowledge that can be used to guide the model building process.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".