DocumentCode :
3299791
Title :
The BP Network Study of the Time Series Overrolling Model for Forecasting the Oilfield Output
Author :
Liang Hui-zhen ; Xie Jun ; Yu Jiang-tao ; Meng Ning-ning
Author_Institution :
Shandong Univ. of Sci. & Technol., Qingdao, China
fYear :
2009
fDate :
11-12 July 2009
Firstpage :
307
Lastpage :
310
Abstract :
Based on analyzing fundamental principle of Back Propagation Network Model, in view of the limitations of BP algorithm, this paper proposed the homologous improved-algorithm from the two aspects of quickening the BP learning speed and raising the degree of convergence. In the course of the complex water flood development, the paper, considering the adaptability feature of the different random factors affecting the wells yield, built the BP time series overrolling model for forecasting the oilfield output, and predicted the wells output using the model, the result indicate that the model have better predicted-accuracy, and fitting to predict the oil production rate and water production rate for different development phase.
Keywords :
backpropagation; neural nets; oil technology; time series; well logging; back propagation network; neural network; oil well production rate; oilfield; time series overrolling model; water production rate; Artificial intelligence; Artificial neural networks; Conference management; Convergence; Neural networks; Neurons; Petroleum; Predictive models; Production; Time series analysis; Back Propagation Network; Forecast Model; Neural Network; Oilfield Output; Time Series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Services Science, Management and Engineering, 2009. SSME '09. IITA International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-0-7695-3729-0
Type :
conf
DOI :
10.1109/SSME.2009.102
Filename :
5233288
Link To Document :
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