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Record W2205332070 · doi:10.1115/omae2015-42385

Dynamic Model of a Mobile Offshore Drilling Unit in Deep Water Environments for Drilling Simulation

2015· article· en· W2205332070 on OpenAlexaff
Farid Arvani, Geoff Rideout, Stephen Butt

Bibliographic record

VenueVolume 10: Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDrillingVibrationSubmarine pipelineCoupling (piping)Offshore drillingDrilling fluidTorsional vibrationMarine engineeringOffshore geotechnical engineeringGeologyEngineeringAcousticsGeotechnical engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

In this study, a dynamic model of a Mobile Offshore Drilling Unit (MODU) is described that simulates drilling scenarios, imposed by the environmental factors in offshore drilling. The Response Amplitude Operators (RAOs) of an industry-recognized semi-submersible MODU are modeled for all six degrees of freedom. A stochastic modeling of waves in the North Sea is used and heave disturbance induced by elevation motion of sea surface is modeled using the JONSWAP spectrum. A bond graph model of a MODU predicts axial vibration, torsional vibration, and coupling between axial and torsional vibration due to bit-rock interaction. Axial and torsional submodels use a lumped-segment approach. The model can predict the expected coupling between Weight On Bit (WOB), bit speed, and bit-rock interface conditions. A series of sensitivity analyses were performed to investigate the significance of MODU motion on WOB fluctuations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2015
Admission routes1
Has abstractyes

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