Real-Time Congealing and Pipeline Monitoring System
- Ullas Pathak (Statistics & Control, Inc.) | Daniel P. Theis (Statistics & Control, Inc.) | John Hooker (Statistics & Control, Inc.)
- Document ID
- Pipeline Simulation Interest Group
- PSIG Annual Meeting, 9-12 May, Atlanta, Georgia, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. Pipeline Simulation Interest Group
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- 72 since 2007
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In recent years, pipeline operators have faced reduced production environments caused by declining brownfield operations and capital constraints induced by oil prices, among other factors, which have led to pipelines operating well under their designed capacity and challenges such as congealing—the precipitation of wax solids in a crude oil pipeline. This paper discusses how models are built using scientific principles and how simulation may be used to predict where congealing is or may occur inside a pipeline. Finally, a case study from a major oil and gas company’s site demonstrates how these modeling and simulation techniques may be effectively applied in the field.
INTRODUCTION AND BACKGROUND
Pipeline operators are currently challenged with operating pipelines safely in reduced production environments, which have been caused by declining brownfield operations, capital constraints brought on by oil prices, and the lack of drilling rigs to keep pipelines full. These present conditions result in pipelines operating well under their designed capacity and challenges such as congealing.
Congealing refers to the precipitation and nucleation of wax solids in a crude oil pipeline. It is initiated by a temperature gradient between the pipe wall and the centerline flow, leading to high-yield flow stress and causing changes in flow behavior.
This paper discusses the physical considerations that contribute and are necessary to detect congealing followed by a series of modeling steps to accurately simulate when and where congealing occurs in a pipeline while accounting for multiphase flow of differing compositions from multiple producers. In turn, this information can automatically be displayed as a visual pipeline profile, allowing operators to understand their entire pipeline operation from remote locations and view critical parameters and events, such as congealing, leak detection, and slugging.
These modeling and congealing algorithms were implemented and validated at a major oil and gas company’s site on a 150-km (~93.2 mi) commercial pipeline network used to transport roughly 50,000 BOPD (7,949 m3/day) from 11 gathering stations to a distribution tank farm. The main transportation pipeline was designed to transport 500,000 BOPD (79,490 m3/day). Congealing events were detected and verified by comparing the simulated and assayed pipeline data. Prediction time averaged between three and six hours in advance of the congealing event, allowing the pipeline operator take appropriate mitigation actions and reduce lost production opportunity (LPO).
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