Trialling the application of hydraulic preconditioning at Creighton Deep
Bibliographic record
Abstract
Vale has completed several phases of work to explore the use of hydraulic preconditioning as a mechanism for the reduction of the maximum magnitude of mining-induced seismic events in highly stressed ground. Hydraulic preconditioning involves the isolation and pressurisation of diamond drillhole intervals to create additional fracture systems to reduce the seismic response by lowering the rock mass quality. The ultimate goal is to enable rotation of the major principal stress around future mining areas to reduce stress related interaction between adjacent orebodies. The phased work plan was executed at Vale’s Ontario Operations mines between 2017 and 2024. Phase 1 was designed around determining if fracture initiation could be executed under high-stress conditions in strong rock using a high-pressure, low flow rate pumping system at Copper Cliff Mine. Phase 2a involved the underground deployment of a prototype high-pressure, high flow rate pumping system at Creighton Mine to evaluate the ability to initiate and propagate fractures in rock mass at greater depth. Phase 2b involved a full-scale preconditioning curtain application, which was completed in early 2024, to trial stress shadowing between two different orebodies at Creighton Mine. This paper will briefly review each of the phased work plans and results, with particular comment on the preparation logistics that evolved as the project advanced. Emphasis will be placed on the full-scale preconditioning curtain application (Phase 2b) and the associated results obtained so far. A review of the challenges and benefits of utilising a hydraulic preconditioning approach in deep mining applications will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".