Vibration and Dynamic Analysis of Oil Well Drillstring Considering Coupled Axial and Torsional Effects Using Cylindrical Superelement
Bibliographic record
Abstract
In this paper axial and torsional vibrations of a drillstring are studied using cylindrical superelement. Drillstring vibration equation is derived by calculating kinetic and potential energy and work done by external forces on drillstring, and utilizing Hamilton’s principle. The model is analyzed by implementing finite element technique with consideration drillstring weight, centrifugal force due to rotation of drillstring, axial force resulting from bit with the formation contact and torsional torque caused by the stick-slip phenomenon. To calculate the vibrational response of drillstring, a computational finite element scheme was developed. For a typical case of oil well drillstring, the time response of axial and torsional vibrations of bit are presented. Also, time history of rotational speed of bit and frictional torque caused by the stick-slip phenomenon on bit, are calculated. Very good agreement is found between findings with implementing superelement and those were reported in the literature. The time consumption by conventional finite element method is very larger than implementing cylindrical superelement for the same problem. This indicates by implementing a few cylindrical superelements, the same results in much smaller computer time than the conventional finite element method, can be extracted.
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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 | 0.000 |
| 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) | 0.001 | 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".