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Record W2037462752 · doi:10.2118/140317-ms

Extension of Gauge Length to Reduce Vibrations and Improve Drilling Performance

2011· article· en· W2037462752 on OpenAlexaboutno aff
Gregory F. Smyth, Andrew Evans, Jonathan Babb, Neil Bolivar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrill bitDrillingGauge (firearms)Bit (key)VibrationRate of penetrationMechanical engineeringEngineeringComputer scienceAcousticsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract During extended reach drilling operations at the Hibernia Platform, operated by Hibernia Management and Development Company Ltd. utilizing ExxonMobil Canada Ltd. resources, bit gauge length and profile have been systematically varied to improve drilling performance while using a standard bottomhole assembly (BHA) and bit design. Prior to developing the standard BHA design and implementing a global performance management process, the ExxonMobil Fast Drill Process, multiple bit runs were required to drill a typical high angle, 10,000-ft interval of 12¼-in. hole. Tool failure and bit damage caused by vibration resulted in multiple bit runs. In order to reduce vibrations and improve drilling performance, bit gauge length has been systematically increased in 1-in. increments while utilizing a standard BHA. Partially tapered gauge was used in conjunction with the increased gauge length to allow directional control. This paper presents the results of progressing from a 4-in. non-tapered gauge bit to a 5-, 6-, and 7-in. partially tapered gauge bit from 2005 to 2009. Mechanical specific energy (MSE), vibration, rotary steerable wear, and directional response data is presented to show the impact of the incremental changes in gauge length over a group of seven wells. The tapered profile and increased gauge length provided comparable directional response with significantly improved drilling performance, resulting in multiple field record rates of penetration (ROPs) and bit run lengths.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.309

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 head0.015
GPT teacher head0.187
Teacher spread0.171 · 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 designBench or experimental
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

Citations11
Published2011
Admission routes1
Has abstractyes

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