Velocity modeling workflows for sub‐salt geopressure prediction: a case study from the Lower Tertiary trend, Gulf of Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Some of the most active and high profile hydrocarbon plays currently being explored and developed around the world lie below a complex salt canopy. Accurate predrill prediction of sub‐salt pore and fracture pressures is technically challenging, yet remains critical for mitigating drilling risk and reducing exploration and development costs. The objective of this paper is to highlight how 3‐D velocity modeling methodologies can be applied to accurately predict sub‐salt geopressures. An example data set from the Lower Tertiary trend of deep water Gulf of Mexico is utilized to demonstrate the key data requirements and earth modeling procedures, and to compare predicted results with postwell drilling reports and measured well data. Central to this approach is a 3‐D layered earth model. It is the basis for cross‐discipline data integration and provides an ideal platform for well property interpolation, velocity–density–pressure transformations, characterization of geomechanical rock properties, multiwell planning, and drilling risk assessment. Although the main goal of the work is accurate predrill predictions of both pore pressure and fracture pressure for improved well design, these multi‐attribute models also provide superior depth prognoses and can be utilized for hydrocarbon column height assessment and seal breach risking, as well as for lithological discrimination. Furthermore, model properties can be incorporated into geomechanical models for detailed wellbore stability analysis. By adopting an earth‐model centric workflow, more reliable and robust predrill geopressure predictions have resulted. This has had a positive impact on well design efficiencies and minimized drilling downtime arising from well control events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it