PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

被引：7
|
作者：
Apio, Andressa [1 ]
Dambros, Jonathan W., V [1 ,2 ]
Diehl, Fabio C. [1 ,3 ]
Farenzena, Marcelo [1 ]
Trierweiler, Jorge O. [1 ]
机构：
[1] Fed Univ Rio Grande do Sul UFRGS, GIMSCOP, Chem Engn Dept, R Engn Luiz Englert S-N,Campus Cent, Porto Alegre, RS, Brazil
[2] Univ Kaiserslautern, Dept Comp Sci, D-67663 Kaiserslautern, Germany
[3] Res & Dev Ctr, BR-21941915 Petrobras, RJ, Brazil
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 01期
关键词：
Modelling and System Identification; Data Analytics and Machine Learning; Inferential sensing; State Estimation and Sensor Development; GAS-LIFT;
D O I：
10.1016/j.ifacol.2019.06.113
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：508 / 513
页数：6
相关论文
共 50 条
  • [41] Location Estimation Using RSSI and Application of Extended Kalman Filter in Wireless Sensor Networks
    Karthick, N.
    Prashanth, Keshav
    Venkatraman, K.
    Nanmaran, Amutha
    Naren, J.
    [J]. INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL : ICACC 2009 - PROCEEDINGS, 2009, : 337 - +
  • [42] Extended Kalman filter based neural networks controller for hot strip rolling mill
    Moussaoui, A. K.
    Abbassi, H. A.
    Bouazza, S.
    [J]. INTELLIGENT SYSTEMS AND AUTOMATION, 2008, 1019 : 234 - +
  • [43] Multi-stream extended Kalman filter training for static and dynamic neural networks
    Puskorius, GV
    Feldkamp, LA
    [J]. SMC '97 CONFERENCE PROCEEDINGS - 1997 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5: CONFERENCE THEME: COMPUTATIONAL CYBERNETICS AND SIMULATION, 1997, : 2006 - 2011
  • [44] Convergence Study in Extended Kalman Filter-based Training of Recurrent Neural Networks
    Wang, Xiaoyu
    Huang, Yong
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (04): : 588 - 600
  • [45] An augmented extended Kalman filter algorithm for complex-valued recurrent neural networks
    Goh, Su Lee
    Mandic, Dando P.
    [J]. 2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13, 2006, : 5419 - 5422
  • [46] Training of Convolutional Neural Networks for Image Classification with Fully Decoupled Extended Kalman Filter
    Gaytan, Armando
    Begovich-Mendoza, Ofelia
    Arana-Daniel, Nancy
    [J]. ALGORITHMS, 2024, 17 (06)
  • [47] Artificial Neural Network Modelling and Flood Water Level Prediction Using Extended Kalman Filter
    Adnan, Ramli
    Ruslan, Fazlina Ahmat
    Samad, Abd Manan
    Zain, Zainazlan Md
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE 2012), 2012, : 535 - 538
  • [48] Self-sensing SMA Actuator using Extended Kalman Filter and Artificial Neural Network
    Gurung, H.
    Banerjee, A.
    [J]. INTERNATIONAL CONFERENCE ON VIBRATION PROBLEMS 2015, 2016, 144 : 629 - 634
  • [49] New Artificial Neural Network and Extended Kalman Filter Hybrid Model of Flood Prediction System
    Adnan, Ramli
    Ruslan, Fazlina Ahmat
    Samad, Abd Manan
    Zain, Zainazlan Md
    [J]. 2013 IEEE 9TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS (CSPA), 2013, : 252 - 257
  • [50] A critical evaluation and experimental verification of Extended Kalman Filter, Unscented Kalman Filter and Neural State Filter for state estimation of three phase induction motor
    Kumar, S.
    Prakash, J.
    Kanagasabapathy, P.
    [J]. APPLIED SOFT COMPUTING, 2011, 11 (03) : 3199 - 3208
12345