Matrix-fluid decoupling-based joint PP-PS-wave seismic inversion for fluid identification
Bibliographic record
Abstract
ABSTRACT Seismic fluid identification is the main goal of current prestack seismic inversion. Various kinds of fluid indicators are used for fluid detection in industry today. However, the existing methods cannot always provide reliable fluid prediction owing to the insensitivity to fluid response and the lack of converted wave constraints. The equivalent fluid bulk modulus is an effective fluid factor based on matrix-fluid decoupling, which can provide persuasive evidence for fluid detection. Combining poroelasticity theory and matrix-fluid decoupling theory, we have deduced a new PS-wave linear amplitude versus offset approximation equation that provides estimations of equivalent fluid bulk modulus, rigidity, porosity, and density. Then, the joint inversion of PP- and PS-waves based on matrix-fluid decoupling was executed in a Bayesian framework with constraints from rock physics and well-log data obtaining elastic parameter estimation of high precision directly. We tested the new method on a synthetic example and field multicomponent data, and the results indicated that the estimated fluid factor matched with well-data interpretation and geology information because of adding converted wave information and avoiding indirect inversion error. This demonstrated that the new method can enhance the quality of fluid detection and provide reliable geophysical evidence for reservoir characterization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".