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Abstract

Flowrate allocation is a complicated task, especially for multiphase flow combined with several reservoir zones and / or branches. The result depends heavily on the available production data, and the accuracy of these. In the application we show here, downhole pressure and temperature data are available, in addition to the total flowrates at the outlet. The developed methodology inverts these observations to the fluid flowrates (oil, water and gas) that enters two production branches in a real full-scale producer. A major challenge is accurate estimation of flowrates during rapid operational changes in the well, e.g. due to choke variations. The Auxiliary Sequential Importance Resampling (ASIR) filter was developed to handle such challenges, by introducing an auxiliary step, where the particle weights are recomputed based on how well the particles reproduce the observations. However, the ASIR filter suffers from large computational time when the number of unknown parameters increase. The Adaptive Gaussian Mixture (AGM) filter combines a linear update, with the particle filters ability to capture non-Gaussian behavior. This makes it possible to achieve good performance with fewer model evaluations. In this work we present a new filter which combines the ASIR filter and the AGM filter (denoted ASGM), and demonstrate improved estimation of rapid parameter variations, while maintaining reasonable computational cost.

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/content/papers/10.3997/2214-4609.201601870
2016-08-29
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601870
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