Seismic Pore-pressure Imaging in Deepwater Offshore West Africa
Bibliographic record
Abstract
Abstract Pore-pressure imaging in the frontier, deepwater offshore West Africa poses enormous challenges. The presence of large and deep radial canyons in the area makes conventional seismic velocity analysis methods inappropriate. Due to relatively younger and highly water-saturated deepwater sediments in the shallow sections, no compaction trend may be discernible for empirical methods for pore-pressure prediction. Moreover, because of the absence of deepwater wells, the calibration of mo del parameters for pore-pressure prediction is not possible. Using automated, high-resolution, and spatially consistent 3D velocity analysis and a first-principle-based rock model, we have developed a methodology for pore-pressure imaging in the frontier, deepwater basins beset with complex geology. The rock model emphasizes burial history and shale diagenesis for pore-pressure generation. The methodology has been successfully applied to provide pre-drill prediction of pore pressure over a 600-km2 area (Figure 1) in deepwater offshore West Africa. A 2D seismic line tying a remote, shallow-water well greatly helped in developing the appropriate rock model. The results, based on primarily seismic data and the associated geological interpretations, suggest a regressive pore-pressure system in the area. The onset of overpressure takes place at the seafloor. The pressure gradient increases slowly, but almost continuously, then rapidly to a maximum value, and then gradually reverses back to a lower value. The pressure profile closely follows certain geologic horizons and structures. Radial canyons on the seafloor significantly affect pore pressure. This paper describes these results in detail. Also presented in the paper are pore-pressure uncertainty estimation and a sensitivity analysis of the rock-model parameters for pore-pressure prediction.
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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 | 0.000 |
| 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 | 0.000 |
| 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".