Research on Logging Evaluation of Reservoir Contamination Based on PSO-BP Neural Network

  • Authors:

  • Tao Li;Libo Guo;Yuanmei Wang;Feng Hu;Li Xiao;Yanwu Wang;Qin Cheng

  • Affiliations:

  • College of Electronics and Information, Yangtze University, Jingzhou, China 434023;School of Geosciences, Yangtze University, Jingzhou, China 434023 and School of Energy Resources, China University of Geosciences(Beijing), Beijing, China 100083;College of Electronics and Information, Yangtze University, Jingzhou, China 434023;Oil & Gas storage & transportation company, petrochina xinjiang oilfield company, Changji, China 831100;Oil & Gas storage & transportation company, petrochina xinjiang oilfield company, Changji, China 831100;Department of Control Science and Engineering, Huazhong University of science and technology, Wuhan, China 430074;College of Electronics and Information, Yangtze University, Jingzhou, China 434023

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

The skin-friction coefficient which indicates the degree of the stratum damage and the loss of production is important for evaluating reservoir contamination. A skin-friction coefficient prediction model based on PSO-BP neural network is presented in this paper, which integrates PSO and BP algorithm and takes full use of the global optimization of PSO and local accurate searching of BP. The examples of skin-friction coefficient prediction show that the prediction model works with quicker convergence rate and higher forecast precision, and can be applied to evaluate the degree of reservoir contamination effectively.