Estimating indicators of oil-bearing fractured reservoirs from frequency components of azimuthal seismic data
Bibliographic record
Abstract
Based on a model of attenuative cracked rock, we derive a simplified and frequency-dependent stiffness matrix, and we present a new indicator of oil-bearing fractured reservoirs, which is related to pressure relaxation in cracked rocks and influenced by fluids. We set up a linearized P-wave to Pwave reflection coefficient as an azimuthally- and frequency dependent function of dry rock elastic properties, dry fracture weaknesses, and the new indicator. By varying this reflection coefficient with azimuthal angle, we derive a further expression referred to as the quasi-difference in elastic impedance, or QdEI, which is primarily affected by the dry fracture weaknesses and the new indicator. An inversion approach can be set up based on the QdEI which through differences in seismic amplitudes with frequency produces estimates of these weaknesses and the indicator. In synthetic inversion tests, we conclude the approach produces interpretable parameter estimates in the presence of data with a moderate signal-to-noise ratios (SNR). Testing on a real data set reveals that the dry fracture weakness estimates are reliable fracture predictors, and we conclude further that the estimated indicator provides an important discrimination tool for fluids in cracks. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: 217D 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".