Materials Selection: A Systems Engineering Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".