PS Using Advanced Seismic Attribute Analysis to Reduce Risk in Frontier Exploration - West Newfoundland Offshore*
Bibliographic record
Abstract
The study, covering 3D data set from the Western Newfoundland in Parsons Pond area, is of high interest with the estimated potential of 2-4 Billion BOE. The primary target reservoir consists of the dolomitized carbonate bank of St. George Group of Middle Ordovician age. The objective is to de-risk a frontier prospect, using the latest seismic interpretation techniques, and to integrate it with the knowledge of regional geology. To identify areas with preferential reservoir properties reservoir characterization techniques were applied. The sequence buildups and internal architectures were investigated using the digital sequence stratigraphic workflow. Then a neural network based multi-attribute classification is applied to determine the areas of high potential reservoir (dolomitization). In addition, a similarity cube has provided further indications of shear zones and karsting which is critical to identify play productivity. The data were further investigated for signatures of vertical fluid migration that could identify either dolomitization due to hydrothermal brines and/or the presence of leaking hydrocarbons. The sequence stratigraphy workflow allowed us to break out packages with specific stacking patterns (aggradation, progradation), type of stratal termination and internal architecture of the reflectors. These observations were used to identify zones with prospective reservoir properties. Hydrothermal dolomitization has been one of the major processes of reservoir development in many areas of North America. As karsting within a formation triggers the dolomitization process, we used seismic attributes and neural networks to identify areas with karst morphology, such as rounded collapse features and radial fracturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".