Hydrate Induced Vibration in an Offshore Pipeline
Bibliographic record
Abstract
Abstract Gas hydrate plugging is considered a very challenging issue in offshore petroleum production and transportation. The phenomenon of hydrate plug formation involves low temperature and high pressure condition. Hydrates can damage equipment of petroleum transport system. In this study, a computational fluid dynamics (CFD) model is proposed to analyze the effect of hydrate flow in pipelines using ANSYS FLUENT multiphase flow modeling techniques. This study has been carried out with a joint industrial collaboration with GRi simulations, Canada. Two case studies have been investigated. The first one is with a pipeline with a dimension specified by an existing literature (Balakin et al., 2010a) for validating the simulation results. The other one is with a more complex geometry of M-shaped jumper including six elbows. Eulerian-Eulerian method was used to model the multiphase hydrate flow. Moreover, the population balance method (PBM) was used to model the hydrate agglomeration and breaking up mechanism. A parametric study of stress analysis due to the flow-induced vibration on pipelines was also investigated. This study helps to identify the regions where the maximum stress and deformation due to various flow conditions. The overall objective is to integrate the ANSYS FLUENT model with GRi simulation's IDEA-FDK platform. Petroleum industry can effectively use the proposed tool to prevent the risky operating conditions in offshore structures. The results from the analysis will help to identify the cause of the pipeline failure, regions of the maximum stress occurred in the pipeline and the plastic deformation of the pipeline due to hydrate flow in pipeline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".