Hydraulic models of pipelines driven by SCADA usually have many more pressure and flow measurements available that are needed as boundary conditions of the underlying model. These extra measurements can be used to improve the estimate of the true state of the pipeline and to give the hydraulic model some resistance to measurement noise. The process of merging the available SCADA into a form the model can use is called “state estimation.”
A new technique for state estimation based on a maximum-likelihood estimate of the state of the pipeline constrained by the underlying physics will be presented. This method, which will be referred to as Maximum Likelihood State Estimation (MLSE), will be justified by comparison with two traditional methods of state estimation in use in the industry, the approach of breaking the pipeline up into multiple models with pressure boundaries and then rectifying the flows, and the Equal Error Fractions (EEF) method of Van der Hoeven [1].
An example of the application of this approach to a real-world pipeline will also be presented, with performance data as well as some of the deficiencies that were found and how they were corrected.
A Maximum Likelihood Approach to Parallel Imaging With Coil Sensitivity Noise
