Drilling Cutting Analysis to Assist Drilling Performance Evaluation in Hard Rock Hole Widening Operation
Bibliographic record
Abstract
Abstract Optimum production from vein-type deposits requires the Narrow Vein Mining (NVM) process where excavation is accomplished by drilling larger diameter holes. To drill into the veins to successfully extract the ore deposits, a conventional rotary drilling rig is mounted on the ground. These operations are generally conducted by drilling a pilot hole in a narrow vein followed by a hole widening operation. Initially, a pilot hole is drilled for exploration purposes, to guide the larger diameter hole and to control the trajectory, and the next step in the excavation is progressed by hole widening operation. Drilling cutting properties, such as particle size distribution, volume, and shape may expose a significant drilling problem or may provide justification for performance enhancement decisions. In this study, a laboratory hole widening drilling process performance was evaluated by drilling cutting analysis. Drill-off Tests (DOT) were conducted in the Drilling Technology Laboratory (DTL) by dint of a Small Drilling Simulator (SDS) to generate the drilling parameters and to collect the cuttings. Different drilling operations were assessed based on Rate of Penetration (ROP), Weight on Bit (WOB), Rotation per Minute (RPM), Mechanical Specific Energy (MSE) and Drilling Efficiency (DE). A conducive schedule for achieving the objectives was developed, in addition to cuttings for further interpretation. A comprehensive study for the hole widening operation was conducted by involving intensive drilling cutting analysis, drilling parameters, and drilling performance leading to recommendations for full-scale drilling operations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".