Abstract

Gas pipeline internal surface typically undergoes degradation for a variety of reasons such as erosion, corrosion and deposits of objectionable materials that occasionally enter the gas stream at receipt points. Accurate monitoring of the pipe internal surface condition can hugely benefit the planning of cleaning activities. Theoretically the pipe wall roughness for a given pipe segment can be extracted based on measured flow data and other system parameters. The challenge lies in the fact that measured data all contain varying degrees of uncertainty, and the system becomes more complex to analyze when it contains different segments connected in series or parallel like many typical gas gathering and lateral networks. This paper demonstrates the application of the Error-in-Variable Model (EVM) using the Markov Chain Monte Carlo (MCMC) solution method in analyzing a complex pipeline network on the TransCanada NGTL System. EVM, a well-established Bayesian parameter estimation technique, accounts for uncertainties in the measured variables, such as flow and pressure data, when determining the most probable estimates of unknown parameters such as pipe internal wall surface roughness. In this work, the EVM problem is solved using the MCMC Metropolis-Hastings algorithm. The MCMC approach is demonstrated to be robust, easy to implement and capable of handling large quantities of data. It has the potential to analyze complex networks and monitor the pipe wall surface condition on-line with SCADA (Supervisory Control And Data Acquisition) data. Using this method, the internal wall surface roughness for the segments of interest in this network were extracted from measured data collected before and after the pigging operation. Results demonstrate the model's capability in estimating the degradation of the pipe wall internal surface and the effectiveness of pigging. Details on implementation and challenges in applying such methodology to analyze complex gas networks are discussed.

Introduction and Background

Gas pipeline internal surface typically undergoes degradation under normal operating conditions for a variety of reasons such as erosion, corrosion and deposits of objectionable materials that occasionally enter the gas stream at receipt points. Such degradation leads to higher energy consumption and can limit the capacity of the pipeline system. Therefore, deploying cleaning pigs periodically to restore the internal pipe wall surface is common practice in industry. However, determining the frequency of pigging, which pipe segments require cleaning, and the effectiveness of cleaning in restoring the internal pipe wall surface to its original state can be a challenge.

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