Improvement of Torque-and-Drag Modeling in Long-Reach Wells
Bibliographic record
Abstract
Torque and drag modeling is regarded as an invaluable process to assist in well planning and to predict and prevent drilling problems. It discusses how to use torque and drag calculations and measurements to plan long-reach well profiles, to execute drilling operations that minimize torque and drag effects and to monitor hole cleaning. In this study a general overview on most of the available literature on the subject is presented. Different models that have been developed for torque and drag predictions along with pros and cons of the models will be discussed. The application of our new fully 3-dimensional analytical friction model will be presented. Moreover a new criterion has been implemented into the new model in order to capture the effect of weight in the horizontal section especially when BHA is dragged into/out of the wellbore. The model validity will be checked by applying the model for two field cases of ERD wells in North Sea. Field cases also demonstrate the importance of buoyancy effects, tripping speed, hydraulic piston force, pipe stiffness as well as well path effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".