Rolling Depth-Of-Cut Control Improves Tool-Face Control and Overall ROP During Steerable Motor Applications
Bibliographic record
Abstract
Abstract Rolling depth-of-cut control (RDOCC) on fixed cutter drill bits can improve rate of penetration (ROP) and reduce vibrations during steerable motor applications by reducing torque fluctuations caused by sliding friction. Torque fluctuations are reduced by absorbing weight-on-bit (WOB) variations using a rolling bearing element, without generating additional torque that leads to tool-face control issues. Laboratory testing validated RDOCC efficiency compared to traditional depth-of-cut control limiters (DOCLs) by evaluating frictional force, load, and material resistance to axial and lateral forces. At the field level, potential applications, where tool-face control was identified as a drilling performance limiter, were analyzed and selected for the initial field tests. Baseline performance and target depth of cut (DOC) were determined using foot-based data. Additional drilling parameters, such as WOB, torque, and vibration, were analyzed to measure the RDOCC influence on drilling performance. The vibration data were recorded using an at-bit data collection device (ABDCD) and later compared to data from bits with traditional DOCLs. Modified bit designs that included RDOCC with rolling elements on three blades to engage at the target DOC were run in the selected applications and results compared to historical data, performance, and dull bit condition. In southeastern Saskatchewan, Canada, the curve section of Bakken wells experienced overall ROP improvement and reduced vibrations at the bit, as indicated by improved dull condition and ABDCD data. Five consecutive field record runs were achieved using bits equipped with RDOCC technology. In McKenzie County, North Dakota, USA, direct comparisons between polycrystalline diamond compact (PDC) bits with RDOCC and those with traditional DOCLs show a 39.5% reduction in hours or better to drill the curve section from 10,300 to 11,100 ft measured depth (MD) on the same pad site. The improved tool-face control allowed higher WOB application from a lower inclination through landing the curve, thereby improving ROP throughout the run. In the Wadi Rafash Field, northern Oman, a PDC bit with RDOCC drilled the curve section, improving dogleg severity (DLS) by 45%, ROP by 23% and reducing distance by 30% over the average offset performance in the field. The novelty of the RDOCC is the ability to absorb WOB fluctuations without creating large torque fluctuations that inhibit tool-face control. By maintaining a consistent tool face, operators and directional drillers can apply more energy to the system, resulting in overall higher ROP.
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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 | 0.001 |
| 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) | 0.002 | 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".