Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function-Reference-Cited by-同舟云学术

Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function

Published:2012-01-05 Issue:5 Volume:22 Page:947-953
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Wu Xiong-Jun,Jiang Guan-Cheng,Wang Xiao-Jun,Fang Na,Zhao Lin,Ma Ying-Min,Luo Shao-Jie

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-011-0787-z.pdf

Reference20 articles.

1. Zhang SH (1993) Reservoir protecting techniques. Petroleum Industry Press, Beijing

2. Li KX (1993) Drilling and completion techniques for reservoir protection. Petroleum Industry Press, Beijing

3. Baptist OC, White EJ, Land CS (1964) Laboratory predictions of water sensitivity compared with field observations of well damage-Patrick draw. SPE 839:1–8

4. Alegre L, Dougherty EL (1988) Applicability of expert systems to diagnose formation damage problems: a progress report. SPE 17460:559–570

5. Civan F (1992) Evaluation and comparison of the formation damage models. SPE 23787:219–236

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