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, Brown. 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.