In liquid lines with significant elevation changes, the presence of slack line flow can be a significant operational problem. Firstly, slack line flow 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.

Via an uncertainty analysis, it is shown that it is not possible for an online model to predict slack flow accurately, particularly if the pipeline is batched and/or utilizes DRA. Both false positive and false negative predictions of slack line conditions are common. Instead of a deterministic model, a statistical approach is proposed. Monte Carlo analysis was used to determine the distribution of conditions around the slack flow conditions including a detailed analysis of the pipeline at the high points of the pipeline where slack conditions will first appear. The derived distribution was used to determine probability of slack conditions at any time.

The new, 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 such that a pattern-matching algorithm could be applied to enhance the detection of slack conditions.

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