Attribute-assisted interpretation of deltaic channel system using enhanced 3D seismic data, offshore Nova Scotia
Bibliographic record
Abstract
Deltaic channels are good exploration targets and form potential hydrocarbon reservoirs . Generally, distributary sand-filled deltaic channels have high porosity and high permeability sandstones hence can form good quality reservoirs. We delineate deltaic channels within Cree Sand member of the Logan Canyon formation in the Penobscot field, offshore Nova Scotia, by devising a workflow that includes seismic data enhancement and attribute studies integrating coherence attributes, amplitude curvature and spectral decomposition attributes. An exhaustive seismic data conditioning improves considerably the signal-to-noise ratio in the conditioned seismic data (-4 dB at dominant frequency) compared to the input seismic (−22 dB at dominant frequency). We perform an integrated seismic attribute study which helps in effectively mapping the deltaic channel systems at different stratigraphic levels of the Cree Sand interval. We carry out a novel attribute analysis by comparing two types of volumetric curvature attributes namely, structural and amplitude curvatures. The structural curvature although depicting the fault patterns clearly, does not delineate the channels due to the absence of any flexure across the channel. Interestingly, the amplitude curvature attribute delineates different channel systems because of the amplitude variation across the channel edges. We identify narrower and thinner channels at the deeper stratigraphic level, while wider and thicker channels appear at the shallower level. Channel width varies from 870 m to 420 m and thickness from 110 m to 52 m from shallow to deeper level. Based on the integrated seismic attributes analysis, we identify varying channel width and thickness at different stratigraphic levels, that correlates to varying sea level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".