DocumentCode
553961
Title
Notice of Retraction
The application of improved BP neural network in the division of sedimentary microfacies
Author
Dongping Duan ; Jiageng Hou ; Yuming Liu ; Yanqing Shi
Author_Institution
Earth Sci. Dept., China Univ. of Pet. at Beijing, Beijing, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
272
Lastpage
275
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.
Single-well sedimentary microfacies division is the base of reservoir study. Use the method of BP neural network to automatic identify the sedimentary microfacies in the areas of high well spacing density as well as large amounts of wells. Adopt the inertia correction method and structural response function method to solve the slow convergence speed and local optimal problem of BP neural network. Firstly, preprocessing the logging data, then using the improved BP neural network method to do the sedimentary microfacies automatic identification for coring Wells, and verifies the accuracy, accuracy of 90.5%. Again applying this method in Uncored Wells results in automatic identification of the 236 Wells sedimentary microfacies. Greatly reduces the working time, improve the working efficiency, good result is achieved.
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.
Single-well sedimentary microfacies division is the base of reservoir study. Use the method of BP neural network to automatic identify the sedimentary microfacies in the areas of high well spacing density as well as large amounts of wells. Adopt the inertia correction method and structural response function method to solve the slow convergence speed and local optimal problem of BP neural network. Firstly, preprocessing the logging data, then using the improved BP neural network method to do the sedimentary microfacies automatic identification for coring Wells, and verifies the accuracy, accuracy of 90.5%. Again applying this method in Uncored Wells results in automatic identification of the 236 Wells sedimentary microfacies. Greatly reduces the working time, improve the working efficiency, good result is achieved.
Keywords
backpropagation; hydrocarbon reservoirs; mining industry; neural nets; China oil field; backpropagation neural network; coring wells; inertia correction method; reservoir study; single-well sedimentary microfacies division; structural response function method; Accuracy; Artificial neural networks; Calibration; Convergence; Educational institutions; Mouth; Petroleum; Automatic identification of sedimentary microfacies; BP neural network; Logging curves; Yongan town oilfield;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022043
Filename
6022043
Link To Document