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Record W2073205141 · doi:10.2118/147205-ms

Materials Selection: A Systems Engineering Approach

2011· article· en· W2073205141 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2011
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsGibson Energy (Canada)
Fundersnot available
KeywordsSelection (genetic algorithm)Material selectionProcess (computing)SubseaRisk analysis (engineering)Computer scienceScheduleSustainabilityConstruction engineeringMaterials processingSystems engineeringBiochemical engineeringProcess engineeringEngineeringBusinessMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Subsea and topsides materials selection is becoming a vital part in the development and long term sustainability of deepwater oil and gas production facilities. Increasing challenges associated with capital and operating cost constraints, schedule compression, remote locations, and the need to deploy materials ever closer to their known limits makes fit for purpose materials selection a complex and difficult issue that crosses many different discipline boundaries. Materials selection is primarily governed by corrosion engineering principles and applied chemical inhibition practices, and then by project specifics. However, there are two different practices that are generally followed that dictate how materials are ultimately selected. The first is by a standard materials selection process using guidance such as that provided in NORSOK M-001, and the second is by using a more informal system with limited guidance that involves individually selecting materials for a specific project. In actuality, the materials selection process is a combination of both. The selection process to identify which materials are considered appropriate is routine and straightforward and is dictated by various corrosion parameters and associated risks. Often this high-level assessment does not appropriately address project specifics, so causing the final material selections to be substantially different from those initially proposed. One of the specific items that often drives this change in materials selection philosophy is the use of chemical inhibitors for corrosion inhibition and the perceived feasibility and level of risk associated with this. Use of a systems engineering approach to material selection can be used beneficially as a process that accelerates the determination and initial optimization of the materials, and the selection of chemicals and their injection locations, and associated monitoring methods and locations in a given topsides, subsea or water injection system design. This process can also be used to address management of change items more readily because alternate materials, chemicals, and injection locations are either already determined or can be rapidly identified by the selection process. The use of a systems engineering approach to materials selection has been instrumental in reducing and/or eliminating risks to ensure maximum productivity and longevity of deepwater production and injection systems while accommodating the unique factors pertaining to each individual project.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.645

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 head0.023
GPT teacher head0.197
Teacher spread0.174 · 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