Bibliographic record
Abstract
Rapid and accurate characterization of time-lapse seismic data is important to enable operational adjustments to be made and provide a guide for future drilling. A facies-based Bayesian inversion offers some advantages over traditional simultaneous prestack inversion, primarily avoiding the laborious construction of low-frequency models. To implement a facies-based inversion method, we adjust the model parameterization to be the ratio of monitor to baseline elastic properties. With this parameterization, the set of facies is reduced to those corresponding to specific production scenarios (production facies) that characterize expected subsurface changes between the monitor and baseline acquisitions. Production facies’ elastic properties are generally modeled through rock physics relationships. The inversion operates on the difference of the angle stacks directly, and hence requires properly calibrated and registered baseline and monitor data. The result is a rapid workflow that can image changes in elastic properties accurately. We demonstrate the technique on a synthetic example, and also on field data from an operating oil sands thermal recovery project in Alberta, Canada. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Start Time: 10:10 AM Location: 225B 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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".