Application of momentum backpropagation algorithm (MOBP) in identification of low-resistivity pay zones in sandstones

被引：2

作者：

Wu Y. ^{[1
]}

Bao Z. ^{[1
]}

Yuan Y. ^{[2
]}

Zhang L. ^{[1
]}

Feng Y. ^{[3
]}

机构：

[1] College of Geosciences, China University of Petroleum, Beijing

[2] College of Petroleum Engineering, China University of Petroleum, Beijing

[3] Department of Petroleum and Geosystems Engineering, University of Texas at Austin, Austin, 78712, TX

来源：

Journal of Petroleum Exploration and Production Technology | 2017年 / 7卷 / 1期

关键词：

Artificial intelligence recognition; Logging data processing; Low-resistivity pay zones; Momentum backpropagation algorithm (MOBP);

D O I：

10.1007/s13202-016-0253-7

中图分类号：

学科分类号：

摘要：

Low-resistivity pay zones with substantial reserves in many oilfields around the world are drawing more attention than ever before. Through analyzing the features of the logging data in low-resistivity pay zones, a fast model for identification of low-resistivity pay zones was developed in this paper. Momentum backpropagation algorithm was used in the model development. Indicators that can amplify the characteristics of low-resistivity pay zones were designed. The proposed model can be used for reevaluating old wells using conventional logging data. Validations through field examples demonstrated the capability of the model in accurate identification of low-resistivity pay zones. © 2016, The Author(s).

引用

页码：23 / 32

页数：9

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