Stick/Slip Detection and Friction-Factor Testing With Surface-Based Torque and Tension Measurements
Bibliographic record
Abstract
Summary Recently, there has been a strong push toward automation and the use of real-time models in the drilling industry. However, it has been recognized that these new methods require a dramatic improvement in the quality of sensor data gathered at the rig. In this paper, we investigate how accurate measurement of drillpipe torque and tension at the surface can be used to diagnose downhole conditions. A surface-based torque-and-tension sub (TTS) was used to perform measurements while drilling several horizontal wells in the Dilly Creek area of the Horn River Basin, which is onshore in Canada. A filtered version of surface torque was used to calculate a stick/slip metric, which was compared to stick/slip measurements acquired with a downhole tool. The results show that there is reasonable correlation between surface and downhole metrics, but the correlation is highly dependent on torque filter start and stop frequencies. A comparison is also performed between the hookload measured with a deadline sensor and the tension measurement from the surface sub. The results show a systematic discrepancy of approximately 5% that is likely caused by sheave friction. A commercial torque-and-drag (T&D) software package is used to show that values for casing friction factor (FF) may be underestimated if sheave friction is present but ignored in the analysis. The results from this study show that an advanced measurement sub placed below the topdrive can provide valuable information regarding drilling performance. Specifically, the torque signal can be used to estimate the level of downhole stick/slip, which alleviates the need for an expensive downhole-dynamics tool. Also, the tension signal can be used to obtain accurate measurements of wellbore FF, which can be compared with theoretical values obtained with T&D analysis. With an appropriate-software implementation, these measurements can be performed in real time, which would enable rig crews to react quickly whenever excessive stick/slip or wellbore drag is encountered during drilling operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".