Integrated pore‐pressure prediction in Gunnison field
Bibliographic record
Abstract
An integrated pore pressure prediction technique has been applied to the Gunnison field, in the Garden Banks area of Gulf of Mexico, for the purpose of understanding the hydrodynamic system of sub-surface hydrocarbon distributions. The pore pressure prediction technique presented in this paper is based on the integration of a 3D high resolution and high density velocity field derived from seismic PSTM gathers and acoustic impedance inverted from a calibrated 3D seismic migration volume. The pore pressure gradient, excess pressure, minimum horizontal effective stress volumes resulted from the integrated technique reveal higher resolution than those generated from the conventional approach which is simply based on a 3D seismic velocity field. The high resolution pressure attributes in the Gunnison field exhibit a good correlation between the occurrences of hydrocarbon reservoirs with pressure gradient regression, relatively lower excess pressure and high effective stress intervals. The pressure attributes derived simply from 3D seismic velocity field has a tendency of unable to reveal a true subsurface pressure distribution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".