Table of Contents Author Guidelines Submit a Manuscript
Advances in Materials Science and Engineering
Volume 2016, Article ID 7948612, 12 pages
http://dx.doi.org/10.1155/2016/7948612
Research Article

Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

1School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China
2Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
3School of Sciences, Southwest Petroleum University, Chengdu 610500, China
4Chongqing Mineral Resources Development Co., Ltd., Chongqing 40042, China

Received 17 January 2016; Accepted 22 May 2016

Academic Editor: Fernando Lusquiños

Copyright © 2016 Xiangyu Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.