Derisk Exploration Targets Offshore NW Scotland by Combination of Angle Domain Imaging and Machine Learning Techniques
Bibliographic record
Abstract
Summary West of Shetland (WoS) area is one of location of the UKCS largest remaining hydrocarbon reserves. For de-risking the WoS exploration Generalized Radon Transform (GRT) migration has been applied to preserve true amplitude and output exact angle gathers which are two important factors for Reservoir Characterization. GRT is ray-based migration scheme but it carried out in angle domain. Machine learning methods have been integrated into Quantitative Interpretation workflow. First, the Fuzzy C-means clustering algorithm is used to quickly scan for AVO anomalies from a large area. The machine learning picked AVO anomaly area matches with Extended Elastic Impedance (EEI) fluid (+200) slice at target horizon very well, and both can confirm with well information (dry well and hydrocarbon well), which means the higher confidence can be achieved on the potential reservoir since two sides of inputs show very similar outputs. After the target area has been chosen unsupervised machine learning algorithms like Principal Component Analysis (PCA) and Self- Organizing Map (SOM) are applied on seismic attribute volumes to pick geo-bodies/sweet spots. The final conclusion is GRT migration integrated with machine learning methods in QI workflow can derisk WoS exploration and to precisely understand the amplitude anomaly for any further field development.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".