Estimation of fluid/porosity and dry fracture weaknesses using azimuthal elastic impedance
Bibliographic record
Abstract
Fluid identification and fracture detection are important tasks in unconventional reservoir (tight gas sand and shale gas) characterization. We establish an approach to directly predict the fluid/porosity term and dry fracture weaknesses based on azimuthal elastic impedance inversion. Under the assumptions of small fracture weakness and low porosity, we simplify expressions relating stiffness parameters, and derive P-to-P reflection coefficient and azimuthal elastic impedance in terms of fluid/porosity term and dry fracture weaknesses. A least-squares algorithm is employed to invert seismic data, partially-stacked over the incidence angle, for elastic impedance at different azimuths, and subsequently a Bayesian Markov Chain Monte Carlo method is used to transform the azimuthal elastic impedance values into the fluid/porosity term and dry fracture weaknesses. Stability and accuracy are analyzed on synthetic data, wherein we conclude the fluid/porosity term and dry fracture weaknesses are correctly estimated in the presence of moderate data error or noise. The stability of the proposed inversion approach is confirmed on a field data set, within which we observe that reasonable parameters are determined. We conclude that this particular workflow and its underlying geophysical assumptions form a potentially powerful approach for fracture prediction and fluid discrimination. Presentation Date: Wednesday, September 27, 2017 Start Time: 11:25 AM Location: 360D 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".