Abstract
This paper mainly focuses on the modeling of oil well pressure data compensation system(OWPCS) based on Neural Networks(NN). Firstly, the operational principle and configuration of OWPCS is described. Then the currently widely used modeling method for OWPCS is given, and its limitations and disadvantages are also illustrated. Secondly, in order to solve the OWPCS modeling problem more reasonably, a new approach based on Neural Network is proposed. Thirdly, the feasibility of using NN to solve this problem is analyzed, and a three-layer BP network is constructed to testify the new modeling method. Fourthly, considering the defect of BP learning algorithm and the special application environment of OWPCS, some improvements are given. Finally, experiment results are presented to show the reasonableness and effectiveness of the new method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Tang, Jl., li, E., Hou, Zg., Zuo, Q., Liang, Zz., Tan, M. (2006). Neural Network Based Modeling for Oil Well Pressure Data Compensation System. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_106
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DOI: https://doi.org/10.1007/978-3-540-37275-2_106
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
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