Rock physics analysis for time‐lapse seismic at Schiehallion Field, North Sea
Bibliographic record
Abstract
ABSTRACT Rock physics analysis plays a vital role in time‐lapse seismic interpretation because it provides the link between changes in rock and fluid properties and the resulting seismic data response. In this case study of the Schiehallion Field, we discuss a number of issues that commonly arise in rock physics analyses for time‐lapse studies. We show that: Logarithmic fits of dry bulk (Kdry) and shear (Gdry) moduli vs. effective pressure (Peff) are superior to polynomial fits. 2D surface fits of Kdry and Gdry over porosity (φ) and effective pressure using all the core data simultaneously are more useful and accurate than separate 1D fits over φ and Peff for each individual core. One average set (facies) of Kdry(φ, Peff) and Gdry(φ, Peff) can be chosen to represent adequately the entire Schiehallion reservoir. Saturated velocities and densities modelled by fluid substitution of Kdry(φ, Peff), Gdry(φ, Peff) and the dry bulk density ρdry(φ) compare favourably with well‐log velocities and densities. P‐ and S‐wave impedance values resulting from fluid substitution of Kdry(φ, Peff), Gdry(φ, Peff) and ρdry(φ) show that the largest impedance changes occur for high porosities and low effective pressures. Uncertainties in Kdry(φ, Peff) and Gdry(φ, Peff) derived for individual cores can be used to generate error surfaces for these moduli that represent bounds for quantifying uncertainties in seismic modelling or pressure–saturation inversion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".