Seismic inversion for P- and S-wave inverse quality factors using attenuative elastic impedance
Bibliographic record
Abstract
P- and S-wave inverse quality factors, 1/QP and 1/QS, quantify seismic wave attenuation, which are related to several key reservoir parameters (porosity, saturation, viscosity, etc.). Estimating 1/QP and 1/QS from observed seismic data provides additional and useful information for a gas-bearing reservoir prediction. We first derive an approximate reflection coefficient and attenuative elastic impedance involving effects of attenuation, and then we establish an approach to invert for elastic properties (P- and S-wave impedances, and density) and attenuation (P- and S-wave inverse quality factors) from seismic data at different incidence angles and frequencies. The approach includes a model-based and damped least-squares inversion for attenuative elastic impedance, and a Bayesian Markov Chain Monte Carlo inversion for 1/QP and 1/QS. Synthetic data tests confirm that P- and S-wave impedances and inverse quality factors are reasonably estimated in the case of moderate data error or noise. Applying the established approach to a field data set is suggestive of the robustness of the approach, and that physically meaningful inverse quality factors can be derived from seismic data acquired over a gas-bearing reservoir. Presentation Date: Wednesday, September 27, 2017 Start Time: 2:15 PM Location: 370D 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".