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Record W2609485266 · doi:10.4043/27892-ms

A Low Motion FPSO Design Hosting SCR and TTR in Harsh Environment

2017· article· en· W2609485266 on OpenAlexaff
Alaa Mansour, Chunfa Wu, Peng Cheng, Ricardo Zuccolo, Ashish Bagaria, Shankar Bhat, Jim Yu, Hong Gun Sung

Bibliographic record

VenueOffshore Technology Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsIntecsea (Canada)
FundersKorea Research Institute of Ships and Ocean Engineering
KeywordsMarine engineeringCatenaryEngineeringHullRobustness (evolution)Capital costElectrical engineering

Abstract

fetched live from OpenAlex

Abstract In the development of remote offshore oil and gas fields with minimal or no infra-structure, the Floating Production Storage and Offloading (FPSO) unit is the preferred solution. The FPSO has the advantage of providing the required storage in the hull and direct offloading to tankers of opportunity. Steel Catenary Risers (SCRs) are the preferred solution in wet-tree applications due to their simplicity, robustness and low Capital cost (CAPEX) and Operational cost (OPEX) compared to other riser options. Also, Top Tensioned Risers (TTRs) have their well-known advantages in allowing direct vertical access to production wells and hence enhanced recovery. However, due to its relatively high dynamic motion, conventional FPSO is not a feasible host for TTRs or SCRs in most environments. In this paper, a Low Motion FPSO (LM-FPSO) design with the ability to host SCRs and TTRs is presented and evaluated for applications in deepwater field developments. A case study is prepared to compare the technical and commercial aspects of a field development in 250m water depth using a conventional FPSO to those using a wet-tree LM-FPSO supporting SCRs and a dry-tree LM-FPSO hosting TTRs. The constructability, transportability and installability of the new design along with its performance verification through physical scale model testing are discussed and presented in this paper.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.019
GPT teacher head0.205
Teacher spread0.186 · 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.

Study designOther design
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

Citations0
Published2017
Admission routes1
Has abstractyes

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