2016-Sustainable Industrial Processing Summit
SIPS 2016 Volume 9: Molten Salts and Ionic Liquids, Energy Production
| Editors: | Kongoli F, Gaune-Escard M, Turna T, Mauntz M, Dodds H.L. |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2016 |
| Pages: | 390 pages |
| ISBN: | 978-1-987820-24-9 |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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Modeling method based on iterative UKFNN pumping oil production process
Li Taifu1; Xiao-Dong Liang2; Zhou Pan3; Tang Haihong1;1CHONGQING UNIVERSITY OF SCIENCE & TECHNOLOGY, chongqing, China; 2, ÖØÇì, China; 3XI¡¯AN SHIYOU UNIVERSITY, xi'an, China;
Type of Paper: Regular
Id Paper: 272
Topic: 17
Abstract:
It is difficult to use the static modeling methods to describe pumping machine mining process because of multi-variable nonlinear and time-varying characteristics. This paper proposes a new modeling approach based on Iterated Unscented Kalman Filter Neural Networks. Firstly the algorithm uses the input data to predict state variables and the covariance matrix. Secondly using the previous estimate data to resample sigma points and do unscented transforming in order to obtain the latest sampling points. Lastly, the machine mining process model with a good precision is obtained by updating state. After doing an experiment on actual production data of a certain oilfield, the results show that the proposed method in this paper has a higher modeling precision and the stronger generalization ability and stronger real-time tracking ability than UKFNN modeling method, proposed approach is an approach choice for pumping machine mining process.
Keywords:
Engineering; Petroleum; Production;References:
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