Quantification of 4D signal and generating density change maps: An East Coast Canada case study
Bibliographic record
Abstract
This abstract describes how an East Coast Canada 4D data set was calibrated for quantitative analysis.We found a linear relationship between 4D amplitude change seen in the 4D seismic at well locations, to the modeled acoustic impedance change from the rock physics relationships to known saturation and pressure changes. From this linear relationship we converted the 4D amplitude change cube into a % impedance change cube. The range of the scatter to the best fit line is the seismic-to-model uncertainty +/- 1%: the noise floor.Through our rock physics relationships, the seismic-to-model noise floor is translated into something more practical. Defining expectations of what the 4D can and cannot see in terms of pressure or saturation change.By differentiating the acoustic impedance equation, we show how a density change map was made by subtracting a velocity change map from its equivalent impedance change map. The 4D velocity change cube is generated from the time alignment process of base line to monitor.A density change map is not subject to the impact of pressure changes of a standard 4D amplitude change analysis. It directly shows sweep in the reservoir. Shortcomings of the density map generation is the low resolution (~60m) of the velocity change seismic cube.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".