Estimation of Production Rates With Transient Well-Flow Modeling and the Auxiliary Particle Filter
- Rolf J. Lorentzen (International Research Institute of Stavanger (IRIS)) | Andreas Stordal (International Research Institute of Stavanger (IRIS)) | Geir Nævdal (International Research Institute of Stavanger (IRIS)) | Hans A. Karlsen (University of Bergen) | Hans J. Skaug (University of Bergen)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- February 2014
- Document Type
- Journal Paper
- 172 - 180
- 2013. Society of Petroleum Engineers
- 1.7.5 Well Control, 2.3 Completion Monitoring Systems/Intelligent Wells
- 2 in the last 30 days
- 232 since 2007
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In the current work, we combine a detailed transient multiphase well-flow model and modern estimation techniques into a tool for better representation of flow rates in petroleum wellbores (influx or outflux). Accurate flow rates lead to better well control and reservoir management, which again are important for the improved recovery of oil from existing petroleum fields. To achieve this, it is possible to use the fact that smart wells with multiple zones and laterals are more common, and they may be equipped with permanent instrumentation and control. Many wells have pressure and temperature gauges in each zone, or even distributed temperature sensing with high spatial resolution. Today, accurate flow-rate measurements or accurate estimates for each zone are lacking, and existing tools are often limited to steady-state models with no uncertainty analysis. The estimation technique applied here is the auxiliary-sequential-importance-resampling (ASIR) filter, which has the advantage of being more-robust and -reliable than the traditional particle filter (PF). The ASIR filter is used to tune the output of specific stochastic models of the flow rates. To perform this tuning, we have chosen a jump-type model for the flow rates. These kinds of models are popular within areas such as econometrics and finance. More specifically, the model implies that the flow-rate process changes structure governed by an underlying Markov jump process. Using this type of model makes us capable of capturing not only smooth transitions but also more-abrupt changes of the flow rates. We have applied the methodology on two synthetic studies (involving multiple zones and fluids), and our case studies clearly demonstrate the feasibility of the automatic identification of reservoir flow-rate distribution from wellbore measurements.
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