Enhancing production rates at El Teniente's black cave mine through optimizing HF hole distribution using discrete fracture network modeling and geostatistical simulation methods
Bibliographic record
Abstract
This study at the Esmeralda Mine, part of the El Teniente Division of CODELCO, investigates optimizing hydraulic fracturing (HF) holes’ spatial distribution to improve rock material production in one of the world's largest copper-molybdenum deposits. Utilizing diverse data sources, including borehole, oriented borehole, and photogrammetry data, along with hang-up frequency and hydrofracturing details, we applied discrete fracture network (DFN) modeling to analyze in-situ block size distribution and fragmentation. These results are based on 12,000 realizations of discrete fracture network (DFN) models using R-Dis-Frag computer pacakge at real cave volumes of 200 m × 200 m × 200 m, with varying parameters, which significantly enhances their reliability. The incorporation of DFN modeling and geostatistical simulation allows for capturing the interaction berween several spatial variables and explaining the variations observed in the production results at the draw points. Key findings of spatio-statistical analysis highlight the significance of volumetric fracture intensity (P32) and extraction column height in reducing hang-up events and enhancing fragmentation efficiency. The study integrates HF-induced and natural fracture intensities, revealing that higher P32 values and higher draw columns correlate with fewer hang-ups and better fragmentation. We recommend non-regular HF patterns for high P32 zones to improve operational efficiency. This research provides insights into optimizing mining operations, acknowledging the limitations of HF propagation efficacy and paving the way for further exploration into the interplay between hydraulic fracturing and natural discontinuities. • Optimizes HF in Esmeralda, El Teniente Mine for better copper production. • DFN modeling used to analyze in-situ block size and fragmentation. • Spatio-statistical study links higher P32 and higher columns to fewer hang-ups. • Recommends non-regular HF patterns for high P32 areas. • Insights into HF and natural discontinuities interplay.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".