Improved recovery of oil from existing petroleum fields is increasingly important. A better representation of production zone information leads to better flowrate control and reservoir management. In order to achieve this, it is possible to utilize the fact that smart wells with multiple zones and laterals are more common, and they may be equipped with permanent instrumentation and control. Today, accurate flowrate measurements or estimates for each zone are lacking, and existing tools are often limited to steady-state models with no uncertainty analysis. Here we combine a transient well flow model and estimation techniques, into a tool for interpretation of wellbore measurements. The estimation technique applied here is the auxiliary sequential importance resampling (ASIR) filter, which has the advantage of being more robust than the traditional particle filter (PF). The ASIR filter is used to tune the output of specific stochastic models of the flowrates. To do this tuning we have chosen a regime type model for the flowrates. More specifically, the model implies that the flowrate process changes structure governed by an underlying Markov jump process. Using this type of models makes us capable of capturing both smooth transitions as well as more abrupt changes of the flowrates.


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