Bibliographic record
Abstract
BHA lateral vibration has been identified as one limiter that may hinder the rate of penetration (ROP). Based on Newton’s equations of motion and Euler-Bernoulli beam bending equation, a steady-state forced-frequency response dynamic model was developed to analyze vibration performance for a single point mass in the BHA surrogate. Wherein, the connection between points relied on massless springs or dampers. The frequency-domain model more accurately represented actual mechanical states for a particular BHA configuration. On this basis, the state vector for a mass point was calculated by the semi-analytical transfer function matrix method at any given position in the BHA surrogate, which greatly reduced the number of discrete elements and the associated computing time. It caused rapid screening of a large number of design alternatives on a PC. The state vector included the lateral and angular deflections, as well as the beam bending moment and shear load, which were integrated as a dynamic vibration performance index called Lateral Vibration Strength Estimate (LSE) utilized to quantitatively evaluate the lateral vibration state. The field application demonstrates that the methods for modeling bottom hole assembly (BHA) vibration performance during drilling to enable improved design in pre-drill and operation for enhanced drilling rate of penetration, to reduce downhole equipment failure in drilling. Field validation for the surveillance tool was performed by comparing high-frequency downhole memory sensor data (100samples/second data rate).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".