From a regional AVA screening to focused elastic attributes estimation: An integrated solution from seismic acquisition to reservoir characterization
Bibliographic record
Abstract
Understanding the elastic properties of reservoirs in exploration can be very challenging – due mainly to the lack of well information, limited regional geological understanding, and the quality and reliability of seismic data available. This case study illustrates how this challenge can be addressed through an integrated solution from broadband seismic acquisition to elastic properties, applied recently to the underexplored deep-water Orphan Basin, offshore Eastern Canada. A multisensor seismic dataset covering close to 22,500 km2 was acquired over three seasons (from 2017 to 2019) and now constitutes a great platform for understanding the geology as well as extracting elastic properties. The understanding of the prospectivity was performed at the regional scale, then on more localized area using one of the very few wells present in the area. On this localized area the newest depth imaging technologies with Full Waveform Inversion (FWI) and Least-Squares Migration (LSM) were used. In addition, a three term Amplitude Versus Angle (AVA) inversion was performed and matched at the well without using the well actively in the process but by using the broadband seismic data only: velocity and amplitude. Turnaround time was also a key element in this regional project. To significantly reduce this, an integrated team of imaging and quantitative interpretation specialists was dedicated to the project from the start. Pre-stack AVO QC was performed iteratively during the processing to ensure that the final pre-stack data was fit for purpose and AVO/AVA compliant for further Quantitative Interpretation (QI) analysis. Presentation Date: Monday, October 12, 2020 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: 361A Presentation Type: Oral
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".