Session: 04-01-02 Leak Detection

Paper Number: 133978

133978 - A Stochastic Approach to Accounting for Slack Line Volume Uncertainties in RTTM
Based Leak Detection 

Abstract: 

In liquid pipelines with significant elevation changes, the presence of slack line flow can be a significant operational problem.  Firstly, slack conditions make control downstream of the slack condition difficult.  Secondly, and more importantly, when the vapor bubble from slack flow is decreasing in size, the downstream flow rate will be less than upstream flow rate, which will appear as a leak to an online model.

“A Stochastic Approach to Slack Line Flow in Online Models” (Okungbowa N, Brown T. PSIG 2023), presented an approach based on Monte Carlo analysis to estimate the probability of slack existing considering uncertainties in physical and modeling parameters.  However, it did not attempt to quantify the associated uncertainties in the calculation of the vapor and liquid volumes in the pipeline.

From a leak detection perspective, an important factor is the uncertainty in the calculation of the rate of change of pipeline fluid volume resulting from operating near or in slack line flow.  In this new paper, we evaluate applying a Monte Carlo analysis in real time to calculate the uncertainty distribution inherent in the slack volume computations and to incorporate those uncertainties into leak detection parameters.

We examine several slack and near slack events from operating pipelines.  As a function of time, we calculate the following for each of these events:

·         The probability of being in slack as a function of time.

·         The probability distribution of the fluid volume uncertainties resulting from operating near or in slack line flow.

·         We evaluate the impact of these uncertainties on leak detection reliability and sensitivity.

The statistical approach is illustrated with multiple examples from operating pipelines. Over time it is expected that a sufficient number of events will be detected and analyzed that a pattern-matching algorithm could be applied to enhance the detection of slack conditions.

Presenting Author: Norense Okungbowa ENBRIDGE PIPELINES INC

Presenting Author Biography: Norense Okungbowa is Supervisor, CPM Maintenance, with the Leak Detection department in Enbridge Pipelines Inc., Edmonton. He has more than 15 years of experience in the hydraulic simulation of single-phase gas and liquid systems, complex multi-phase flow lines systems, and leak detection systems. Norense received a M.Sc. degree in mechanical engineering with specialization in the areas of fluid power hydraulics and computational fluid dynamics (CFD).

Authors:

Norense Okungbowa ENBRIDGE PIPELINES INC
Ed Nicholas Nicholas Simulation Services