Modeling of Heat Loss from Offshore Buried Pipeline through Experimental Investigations and Numerical Analysis
Bibliographic record
Abstract
Abstract Offshore oil and gas production in arctic areas is challenging due to harsh environmental conditions. Pipeline burial and trenching in those areas are now one of the prime methods to avoid ice gouge risks and other threats. Thermal management becomes critical when the ambient temperature is low such as the typical seawater temperature on the sea bed. The flow temperature and pressure affect viscosity of the fluid traveling through the pipeline and determines the state of the fluid (single or multiphase). The effect of freezing around oil and gas pipes in the vicinity of permafrost is considered as another concern for flow properties of oil and gas in arctic regions. Theoretical shape factor model has been widely utilized to estimate heat loss from buried pipelines. This study examines the validity of using this method for flow assurance calculations. Several steady state and transient experiments have been carried out to model the heat loss considering different parameters such as burial depth, backfill soil, trench geometries etc. Natural convection can play a significant role in the overall heat loss process from the buried pipeline. The total heat loss increases significantly when the backfill soil is loose or sandy. This paper illustrates the effects of heat conduction and natural convection in the heat loss mechanism from buried pipelines. The outcome of this paper will provide valuable heat loss models based on experimental and numerical analysis results. These outputs can be used largely in petroleum industries for designing pipelines in offshore arctic areas and to mitigate several flow assurance issues (e.g. wax and hydrate formation in the pipeline effectively). The methodology used in this research i.e. analyzing the experimental data with two steps of validation will ensure the validity of the proposed model. Using different parameters such as burial depths, trench geometries, and backfill soil this paper provides an effective model for the offshore pipeline design.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".