Geomechanical insights in the Bedout Sub-basin: exploiting technologies for understanding reservoir settings
Bibliographic record
Abstract
An extensive collection of drilling and wireline data, core and lab strength tests, pressure tests and advanced geomechanical techniques has provided an unprecedented data set to better understand the impact that the stress regime has on characterising reservoir in the Bedout Basin area. An important cost effective method for collecting data while drilling has been the deployment of logging-while-drilling image data followed by wireline image data to document the immediate impact that drilling has on well integrity and potential time-dependent wellbore integrity. Reliable estimates of the minimum horizontal stress (Shmin) were based on carefully executed extended leak-off-tests. Fine-scale observations of drilling-induced isotropic wellbore breakouts, tensile cracks, anisotropic breakouts, and drilling-enhanced natural fractures were collectively used to constrain the Bedout Basin stress regime to be strike-slip (SHmax > SV > Shmin, where SV is the vertical stress and SHmax is the maximum horizontal stress) with stress magnitudes sufficiently high to induce shear failure and fracture permeability on a selected population of natural fracture orientations. Advanced geomechanical modelling of breakouts in the Phoenix South-2 and Roc-2 wells indicated that a unique set of natural fracture induced anisotropic breakouts, which could not be explained as isotropic breakouts because of the high rock strength. In many situations, the responsible natural fracture or joint was undetectable in the image data, but the effect of the natural fracture systems was evident in the anisotropic breakouts. The Phoenix South-2 well suddenly encountered elevated pore pressures (1.56 SG or greater) at total depth where there was no pronounced indication of a systematic pore pressure ramp in the overburden. Geomechanical modelling was used to independently confirm near normal pore pressures in the overburden by predicting that excessive breakouts would have formed if there was a pressure ramp given the ~1.1 SG mud weight used to drill the well.
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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 | 0.002 | 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".