Integration of Production History and Time-lapse Seismic Data Guided by Seismic Attribute Zonation
Bibliographic record
Abstract
Abstract Cluster analysis is used to construct fluid flow zones from seismic attributes. The steps are (1) remove grid points that contain outliers in any seismic attribute; (2) scale each attribute to zero mean and unit variance; (3) use principal component analysis to transform the scaled attributes to uncorrelated principal component attributes; (4) principal component attributes are grouped into categories of similar seismic response using cluster analysis; (5) upscale the seismic grid to the computational grid scale using a weighted voting procedure (morphing); (6) spatially filter cluster assignments to remove small, isolated spots using a weighted voting scheme; (7) assign a seismic zone to spatially connected elements with the same cluster category. After zonation, rock properties such as porosity and permeability are perturbed as a group within each zone instead of node by node in order to match historical production and seismic data. Using a Gulf of Mexico test case, the zoning procedure produced useful computational zones and reduced the time required for history matching.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".