Proven Methods and Techniques to Reduce Torque and Drag in the PrePlanning and Drilling Execution of Oil and Gas Wells
Bibliographic record
Abstract
Abstract During the period of 2007 to 2009, the operator drilled several wells in the Gulf of Mexico (GOM), in which the predicted surface torque from the pre-well planning phase was higher than topdrive limitations and/or drillpipe specifications. This paper describes the torque and drag reduction methods that were found to be effective when fully applied over a series of wells. These methods were confirmed and proven through the analysis of field data and the use of torque and drag modeling software. This study is based on a case history of a 26,079-ft measured depth (MD), complex S-shaped well, but data from other deepwater GOM wells are presented as well to illustrate those proven techniques. Drilling engineers, application engineers and drilling supervisors can use this comprehensive collection of torque and drag reduction methods in the pre-well planning phase, as well as the drilling execution phase, to minimize risk. Applying these methods will ensure that critical and challenging wells can be successfully drilled to total depth as intended. The torque and drag reduction techniques discussed in this paper range from: Modeling drillstring and BHA modifications Optimizing well path design Using non-rotating drillpipe protectors Deploying mud additives Selecting and integrating a complete drilling systems approach These diverse techniques are proven through the use of data collected during the drilling process, and graphic representations that are discussed and explained in this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".