Improving fracture initiation and potential impact on fracture coverage by implementing optimal well-planning and drilling methods for typical stress conditions in the Cooper Basin, central Australia
Bibliographic record
Abstract
Drilling conditions involving high mean and deviatory stresses, and natural fractures in the Cooper Basin present challenges in drilling and introduce wellbore rugosities, leaving a damaged wellbore subject to a stress cage effect. Fracture initiations have been problematic in vertical Cooper Basin wells, exhibiting high initiation and treating pressure frac treatments, and with high stress conditions posing greater risks in non-vertical completions. While far-field fracture complexity should be simplified, the near wellbore complexity results in reduced fracture conductivity. The authors believe that present drilling practices and wellbore azimuths may be contributing to sub-optimal hydraulic fracture initiations and complexities. Present analytical modelling methodologies can derive initiation pressures for circular wellbores, but require more complex numerical models to include flaws and ellipticity to represent natural fractures and wellbore rugosities. This study compares initiation pressures and presents graphical results, comparing circular and elliptical wellbore cases with flaws. This extended abstract outlines the criteria used in these models and remarks on areas needing further research and model development. The authors also propose improved drilling techniques to achieve more stable, smoother wellbores, potentially reducing some rugosity and drilling induced fractures. By using data from recent research and other cases with complex stress environments, it is proposed that initiation pressures might be reduced by inclining wells for hydraulic fracturing treatments in a favourable alignment in the maximum horizontal stress direction (sH-Max), and implementing completion techniques that aid better fracture initiation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".