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Record W3006915478 · doi:10.2118/199565-ms

Rolling Depth-Of-Cut Control Improves Tool-Face Control and Overall ROP During Steerable Motor Applications

2020· article· en· W3006915478 on OpenAlexaboutno aff
Stephen G. Hawkins, Van Brackin, Mohammad Taleb Ameri

Bibliographic record

VenueIADC/SPE International Drilling Conference and Exhibition · 2020
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsTorqueDrillingRate of penetrationDrillVibrationLimiterDrill bitPenetration depthEngineeringComputer scienceSimulationMechanical engineeringAcousticsElectrical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.199
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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