Frontier Exploration Insights Using Simultaneous Inversion of Velocity and Reflectivity: a Case Study, Offshore Canada
Bibliographic record
Abstract
Summary Quantitative Interpretation (QI) workflows have evolved significantly since the last decade or so. In 2007, the introduction of the broadband marine seismic using multisensor streamer has created a significant step change in the offshore industry. This step change had/has some significant implications on the whole chain from the seismic acquisition to reservoir properties estimation. On the latter, the requirement of a well or model based seismic inversion is significantly reduced allowing a seismic inversion to be more data driven than model driven. With more reliable seismic data being acquired, we have also seen the rapid development of inversion-based techniques such as Full Waveform Inversion, Least-Squares Migration and their integration. The objective of this paper will be to present how in a very frontier exploration setting the simultaneous inversion of velocity and angle dependent reflectivity can have an impact on the quantitative interpretation workflow benefiting for an improved prospectivity assessment and understanding of the area concerned. This will be presented through the mean of a case study in the Offshore Newfoundland and Labrador, Canada and by analyzing the Amplitude versus Angle (AVA) response of this new depth imaging inversion scheme.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".