DocumentCode
3293886
Title
Notice of Retraction
The neural network method of parameter recognition for transient well test
Author
Meng Jiang ; Hu Ze
Author_Institution
Sch. of Oil & Gas Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China
fYear
2011
fDate
15-17 April 2011
Firstpage
848
Lastpage
851
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The neural network with learning and very strong non-linear processing ability, BP neural network used as the neural network model of parameter recognition for transient well test in this paper, the internal connection weights of neural network on representation of the model parameters. Static system by neural network identification method available well test interpretation parameters, BP neural network as the transient well test parameter recognition of the neural network model, overcoming the affecting of human subjectivity with conventional well testing or modern well test analysis methods and nonlinear optimization problems obtained the diversity effects of consequences. Finally, on-site examples given, have proved to be very significant.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The neural network with learning and very strong non-linear processing ability, BP neural network used as the neural network model of parameter recognition for transient well test in this paper, the internal connection weights of neural network on representation of the model parameters. Static system by neural network identification method available well test interpretation parameters, BP neural network as the transient well test parameter recognition of the neural network model, overcoming the affecting of human subjectivity with conventional well testing or modern well test analysis methods and nonlinear optimization problems obtained the diversity effects of consequences. Finally, on-site examples given, have proved to be very significant.
Keywords
backpropagation; neural nets; optimisation; pattern recognition; BP neural network; internal connection weights; neural network identification method; nonlinear optimization; nonlinear processing; parameter recognition; transient well test; Analytical models; Artificial neural networks; Convergence; Media; Reservoirs; Training; Transient analysis; Parameter recognition; neural network method; transient well test;
fLanguage
English
Publisher
ieee
Conference_Titel
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-8036-4
Type
conf
DOI
10.1109/ICEICE.2011.5778353
Filename
5778353
Link To Document