Bibliographic record
Abstract
The paper presents a generalized buoyancy model for application in wells, called the generalized Archimedes’ principle. There exists confusion in the oil industry about buoyancy effects as related to the force-area method (also known as piston force method) or the Archimedes’ principle, the stability force in buckling and axial forces during well intervention operations. This paper will present a general model called the generalized Archimedes’ principle, which is valid for all cases. The paper presents examples of application to ordinary drilling operations, deviated wells and different fluid densities inside and outside the pipe. For well intervention scenarios, the effects of wellhead shut-in pressures on buoyancy are demonstrated. This simple model, not requiring use of a “stability force”, can also analyze buckling. Two field cases are presented to demonstrate the use of the generalized Archimedes’ principle in torque and drag analysis. When the drill string is run in hole having a float valve installed, the buoyancy factor changes depending on how much air is in the drill string. The generalized Archimedes’ principle may simply be applied to the torque and drag model for accurate lowering force calculations. Using the generalized Archimedes’ principle, a correct assessment of the real axial load of the pipe is obtained, a critical factor for tubular work in petroleum wells. The model is also significant as it is simple and can be applied to all well operations.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".