Bypass Pigging Operation Experience and Flow Assurance Study
Bibliographic record
Abstract
Abstract Pigging has been employed over the decades to provide liquid, solid and depositmanagement in pipelines. Pigging of subsea tie-backs to manage liquid holdupand deposits is a significant flow assurance challenge. Deepwater developmentscontinue to increase this challenge, with longer flowlines, longer risers andgreater potential for solids deposition. Significant production risks include aseparator trip caused by a surge in liquids/solids into topsides equipment, andthe potential for lost production due to a stuck pig in the offshore flowline. By-pass pigging has been widely used to reduce the pigging risks in longflowlines by spreading the collected liquid and/or scraped deposits in front ofthe pig. By-pass pigging is a complex fluid dynamics operation. Only limitedsuccess in predicting slug size and duration has been reported for by-passpigging operations, even with sophisticated modeling tools. This paper presentsinformation about by-pass pigging experience for a wet gas/condensate subseaflowline (ID = 20.6" and L = ~10 km) running from a wellhead platform to acentral processing platform. The operational experience includes optimizationof the by-pass opening in the pig, separator liquid drain rate control, andseparator level control. Solids recovery will be discussed. Field data fromby-pass pigging operations will be compared to predictions from two models. Based on the field data, updated recommendations for modeling by-pass piggingoperations will be discussed. Introduction Pigging has been used for many decades to perform various tasks such aspipeline inspection and cleaning. Numerous pipeline systems rely on pigging asa major flow assurance control for hydraulics (liquid inventory management), corrosion management (under deposit corrosion and/or top of line corrosion), and solid management such as wax, asphaltene, sand and scale control. Asoil/gas production continues to move to deep sea areas, pigging of long subseatie-backs to manage liquid holdup and terrain slugging/liquid surge due to longrisers is a significant flow assurance challenge. Production risks include aseparator trip caused by a surge in liquids/solids into topsides equipment, andthe potential for lost production due to a stuck pig in the offshoreflowline. In order to minimize the liquid/solid surge risks in long flowlines/pipelines, by-pass pigging has been used in various offshore flowlines. Properly designedby-pass pigging can effectively minimize the liquid/solid surge by spreadingthe collected liquid and/or scraped deposits in front of the pig, as shown inFigure 1. The gas bypassing the pig elongates the liquid slug in front of thepig by creating a certain void fraction in the slug. Several pigging models are widely used to predict liquid surge risk. It isimportant to validate the by-pass pigging models with field data to haveconfidence in surge capacity design; however, very few attempts have beenreported. Wu et al (1996) reported the size and duration of the liquid surgeobserved during a field test of by-pass pigging operations was about 30% of thepredicted surge volume. This paper presents liquid surge data collected during by-pass piggingoperations for a wet gas/condensate subsea flowline (ID = 20.6" and L = ~10 km)running from a wellhead platform to a central processing platform in anIndonesian offshore field. Field data from by-pass pigging operations arecompared to predictions from the OLGA model and to a model proposed by Fan andDanielson (2009). This field data and model prediction results are valuable todevelop liquid surge prediction practices for wet gas/condensate flowlines. These results will help pipeline design engineers to accurately estimate therequired surge capacity and will assist production staff to optimize pipelinepigging operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".