Seismic facies discrimination incorporating relative rock physics
Bibliographic record
Abstract
Abstract Seismic facies discrimination is usually performed based on a rock-physics-driven quantitative interpretation approach. The accuracy of the study of rock physics largely impacts the reservoir and fluid recognition. However, the study is commonly conducted with absolute well logs without removing the trend effect. Such an approach may introduce inappropriate low-frequency information and bias further analysis of seismic data (crossplotting, facies probability density function generation, and projection angle determination). By contrast, relative rock physics with the trend decomposed reflects the rock-property variation of the overburden and underlying formation. The relative portions are more consistent with the seismic reflectivity, providing an alternative tool to facies interpretation through a seismic inversion scheme. A workflow for seismic facies discrimination has been investigated that incorporates relative rock physics, long short-term memory-based nonlinear seismic inversion, and Bayesian classification. This workflow is employed in a case study from Songliao Basin in northeast China, through which the results of relative and absolute approaches in key steps are analyzed and compared. The consistency of facies, determined through relative and absolute methods with petrophysical interpretation, is calculated. The relative analysis exhibits improved agreement with petrophysical interpretation in overall facies and reservoir sand discrimination of the blind wells. This indicates the potential to minimize the trend bias by integrating relative rock physics in quantitative interpretation.
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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 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".