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Record W2022151878 · doi:10.4043/15286-ms

Running Fairings for Deepwater Drilling in the Gulf of Mexico - A Cost-Benefit Approach to Deciding the Faired Length

2003· article· en· W2022151878 on OpenAlexaff
Erin Balch, W.K. Kavanagh, Paul Griffin, Luc Chouinard, Cortis Cooper, Hugh Thompson

Bibliographic record

VenueOffshore Technology Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsDowntimeDrillDrill pipeEngineeringMarine engineeringDrillingCurrent (fluid)Reliability engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The decision to run fairings on drilling risers is critical in terms of the additional cost to run fairings versus downtime cost from suspended operations due to current conditions causing excessive flex-joint angles. Making an informed decision, enhanced by actual predicted current conditions for a drilling program, is a valuable capability in planning to maximize drilling uptime and minimize downtime costs. This paper presents an innovative model which has been developed as a rational decision tool for determining the fairing requirements to mitigate predicted loop current events for proposed drilling programs in the Gulf of Mexico. Introduction The Fairing Optimization Model provides an innovative approach to fairing length requirements based on an evaluation of the cost and benefits of running fairings for a planned drilling program. The model has been applied to several rigs under contract to ChevronTexaco. It provides the operator with a systematic approach to decide on limiting loop current conditions beyond which VIV(Vortex Induced Vibrations) suppression fairings will be required for a range of water depths in the Gulf of Mexico and to decide on the optimum number of fairings to run for a series of proposed drill sites. This model combines a loop-current eddy environmental prediction tool with results from an extensive analytical database of riser response. For a proposed drill-site, this model estimates the probability that a loop current eddy will adversely affect a proposed drill-site in terms of its severity and duration. An example model was developed for water depths ranging between 2,000ft and 9,000ft. The model clearly demonstrated that for a given drilling scenario and environmental forecast there exists an optimum (zero or non-zero) length of fairings below which cost of downtime exceeds the cost of running fairings and above which the cost of running fairings exceeds that of likely downtime. Methodology. The flowchart showing the steps taken to achieve an optimized fairing solution is given in Figure 1. The main steps to achieve an optimized solution are as follows:Riser Response AnalysisEddy PredictionFaired Length Optimization Riser Response To obtain an accurate representation of the riser responses in the field, it is important to model the riser according to the drilling contractor's operating philosophy. This entails using the actual riser properties, tensions, buoyancy distribution and mud weights that the contractor would use for a given drilling program. Once this data is collected, a load case matrix can be set up to encompass various water depths, top tensions, mudweights, and even vessel offsets. This allows for an extensive database of riser response to be created to emulate most drilling scenarios. The inclusion of a tension that represents the operating strategy of the contractor and varied vessel offsets is imperative because these two parameters are the most common methods used to mitigate VIV aside from auxiliary devices. An increased tension will damp out VIV motions and moving the vessel downstream will improve the upper flex joint angle to a certain extent. However, moving the vessel too far downstream will adversely effect the lower flex joint angle.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
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 head0.024
GPT teacher head0.228
Teacher spread0.203 · 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
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

Citations1
Published2003
Admission routes1
Has abstractyes

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