A Geometric Approach to Drill Path Collision Avoidance
Bibliographic record
Abstract
Tremendous research activities took place on the proximity analysis of points and straight lines in three-dimensions using Voronoi diagrams. However, less attention has been paid to the proximity of curves in three-dimensions due to the complexity involved in computation. Hence, in this paper, we present a geometric approach to collision detection between curves of restricted topologies in a three-dimensional space. The proximity of petroleum well drilling paths under an oilfield has been chosen as the area of application. Mature oilfields with many existing boreholes, which are exploited by directional drilling, have complex three-dimensional structure underneath. To avoid "blowouts", which may lead to fires and explosions, interference check or collision avoidance calculation is often necessary while planning a new drilling path for such an oilfield. In this paper, we present an efficient volume-sweep approach to collision detection between a newly planned drilling path and the existing drilling paths under such a multiwell oilfield. Upon collision detection, we make use of 2D Voronoi graph topology representation to facilitate further in interactive drill path corrections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".