Drill bit grading using LiDAR and imagery on the apple smart devices
Bibliographic record
Abstract
Reservoir development in the petroleum industry starts with the drill bit. A drill bit’s dull condition must be closely monitored since it significantly influences the efficiency and the cost of drilling operations. The dull condition check procedure is called drill bit grading and is essentially a change detection problem to determine the state of the drill bit, in particular the wear of the cutting teeth inserts. Currently, the grading is conducted manually on-site, which is error-prone and highly subjective. Laser scanning technology offers a potential solution to overcome the shortcomings of existing practice. The integration of LiDAR (Light Detection and Ranging) on the newly-launched iDevices, the iPhone 12 Pro and the iPad Pro 2020 offers new opportunities for close-range measurement given their huge customer base and low cost. The goal of this research is to investigate the performance of these devices, and to develop a tool for the drill bit grading. Since bit grading is significantly impacted by the performance of the sensor, several basic tests were first conducted under controlled experimental conditions, e.g., the room temperature and ambient lighting and measurement surface. The temporal stability of the iDevices was examined by capturing a series of datasets of a flat wall over forty-five (45) minutes, then the effect of range, reflectivity and incidence angle on data quality was tested by measuring the Spectralon targets at different situations. The performance tests found that using only the LiDAR data was not sufficient for drill bit grading. Thus, a preliminary grading system based on the fusion of LiDAR and color camera is proposed by modelling the post-drilling bit and detecting the changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".