RESEARCH ARTICLE


Permeability Prediction of Tight Sandstone Reservoirs Using Improved BP Neural Network



Peng Zhu, Chengyan Lin, Peng Wu, Ruifeng Fan, Hualian Zhang, Wei Pu
1 School of Geosciences in China University of Petroleum, Qingdao 266580, China;
2 Binnan Oil Factory of Sinopec Shengli Petrochemical Company, 256600, China;
3 Chongqing Institute of Geology Mineral Resources, Chongqing, 400042, China;
4 Shixi Operation District, Xinjiang Oilfield Company, Petro China, Karamay, Xinjiang 834000, China

Article Information


Identifiers and Pagination:

Year: 2015
Volume: 8
First Page: 288
Last Page: 292
Publisher Id: TOPEJ-8-288
DOI: 10.2174/1874834101508010288

Article History:

Received Date: 13/10/2014
Revision Received Date: 20/1/2015
Acceptance Date: 23/1/2015
Electronic publication date: 19/8/2015
Collection year: 2015

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© 2015 Zhu et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Correspondence: * Address correspondence to this author at the School of Geosciences in China University of Petroleum, Qingdao 266580, China; Tel: +8618765925521; E-mail: 65814011@qq.com


Abstract

By analyzing the permeability controlling factors of tight sandstone reservoir in Wuhaozhuang Oil Field, the permeability is considered to be mainly controlled by porosity, clay content, irreducible water saturation and diagenetic coefficient. Because the conventional BP algorithm has its drawbacks such as slow convergence speed and easy falling into the local minimum value, an improved three-layer feed-forward BP neural network model is built by MATLAB neural network toolbox to predict permeability according to the four permeability controlling factors, while studying samples of model are selected based on the representative core analysis data. The simulation based on improved neural network model shows that the improved model has a faster convergence speed and better accuracy. The consistency between model prediction value and lab test value is good and the mean squared error is less. Therefore, the new model can meet the needs of the development geology research of oil field better in the future.

Keywords: BP neural network, improved BP algorithm, network simulation training, permeability prediction.