Target-oriented data conditioning for prestack inversion in an unconventional reservoir: A Canadian case study
Bibliographic record
Abstract
Target-oriented data conditioning is a key part of any reservoir characterization workflow. Data conditioning is used to optimize the match between the synthetic data, used in the prestack inversion, and the real data. When this is done correctly, the accuracy and confidence of inversion results may be greatly improved. This is proved on prestack seismic inversion results from a resource play in Canada. The flow is broken down into prestack gather conditioning, which improves the signal-to-noise and gather flatness, and poststack conditioning, which further improves the coherency prior to applying spectral balancing. As a final key step, spatially variant amplitude balancing is used to calibrate the angle stacks to the expected background trend from the well synthetics. The combination of all steps is demonstrated via [Formula: see text] and mu-rho versus lambda-rho crossplots, between the inverted results and the well measurements, to provide a significant improvement on the resolution and accuracy of the final prestack inversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".