Improved Differential Simulation Method for Oilfields Development Indices Forecast

Author

Min, Chao ; Liu, Zhi-Bin

Author_Institution

Coll. of Sci., Southwest Pet. Univ., Chengdu, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

174

Lastpage

178

Abstract

A differential simulation method is proposed for the oilfields development indices forecast in this paper. Then it is improved through BP neural networks with varying learning rate, which is employed in the process of parameter identification and makes the accuracy of medium and long-term prediction better. This model based on time-varying systems is applied in a practical example in the end of this paper and the results demonstrate that the accuracy is indeed improved.

Keywords

backpropagation; digital simulation; forecasting theory; neural nets; petroleum industry; production engineering computing; BP neural networks; differential simulation method; long-term prediction; medium prediction; oilfields development indices forecasting; parameter identification; time-varying systems; varying learning rate; Chaos; Computational modeling; Decision making; Educational institutions; Input variables; Neural networks; Parameter estimation; Petroleum; Predictive models; Time varying systems; differential simulation; varying learning rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.277

Filename

5365480