Estimation of modified fluid factor and dry fracture weaknesses using azimuthal elastic impedance
Bibliographic record
Abstract
ABSTRACT We consider the problem of fluid identification and fracture detection in unconventional reservoir (tight gas sand and shale gas) characterization. We begin with a simplification of the stiffness parameters and the derivation of a linearized reflection coefficient and azimuthal elastic impedance (EI). The accuracy of the simplification is confirmed in application to gas-bearing fractured rocks with low porosity and small fracture density. We have developed a modified fluid factor that is more sensitive to fluid type and less influenced by porosity. A two-step inversion workflow is evaluated based on the derived linearized reflection coefficient and azimuthal EI, including (1) a damped least-squares inversion for azimuthal EI, constrained by an initial model, and (2) a Bayesian Markov chain Monte Carlo inversion for the modified fluid factor and dry fracture weaknesses. Stability and accuracy are examined with synthetic data, from which we conclude that the modified fluid factor and dry fracture weaknesses can be stably determined in the presence of moderate data error/noise. The stability of our approach is further confirmed on a fractured tight gas sand field data set, within which we observe that geologically reasonable parameters (Lamé constants, the modified fluid factor, and dry fracture weaknesses) are determined. We conclude that our inversion workflow and its underlying assumptions form realistic predictions/discriminations of reservoir fracture and fluid parameters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".