Updating the Geomechanical Model and Calibrating Pore Pressure From 3D Seismic Using Data From the Gnu-1 Well, Dampier Subbasin, Australia
Bibliographic record
Abstract
Summary A post-mortem analysis of the Gnu-1 well was conducted to help us to understand drilling experiences in the context of the pore-pressure and stress profiles. The post-mortem involved a review of the drilling experiences and an analysis of circumferential acoustic scanning tool (CAST) image data, wireline-log data, and the logging-while-drilling (LWD) data. This information was used to refine and verify a geomechanical model (i.e., in-situ stress, pore pressure, and rock-mechanical properties) in the vicinity of the Gnu-1 well. Of prime concern was the verification of the predrill pore-pressure prediction previously undertaken using 3D-seismic-velocity data and offset-well data. Wellbore-failure and natural-fracture analyses were integral parts of the post-mortem. Wellbore breakouts seen in the image data allowed the pore pressure in the 8.5-in. hole section of Well Gnu-1 to be constrained. Modeling using image data collected in the Athol formation indicates that the pore pressure does not increase as rapidly as was estimated in the predrill study. Pore pressures in the North Rankin formation and in deeper formations were consistent with the predrill study. The geomechanical model was able to explain the losses seen in the Athol formation in Well Gnu-1 when using the mud weights experienced by the open hole at the time of drilling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".