Dimension Analysis-Based Model for Prediction of Shale Compressive Strength
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 (Longmax...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/7948612 |
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| author | Xiangyu Fan Fenglin Xu Lin Chen Qiao Chen Zhiwei Liu Guanghua Yao Wen Nie |
| author_facet | Xiangyu Fan Fenglin Xu Lin Chen Qiao Chen Zhiwei Liu Guanghua Yao Wen Nie |
| author_sort | Xiangyu Fan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-899725058e8e4fbfa35de8e1186102eb |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-899725058e8e4fbfa35de8e1186102eb2025-02-03T06:13:11ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422016-01-01201610.1155/2016/79486127948612Dimension Analysis-Based Model for Prediction of Shale Compressive StrengthXiangyu Fan0Fenglin Xu1Lin Chen2Qiao Chen3Zhiwei Liu4Guanghua Yao5Wen Nie6School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Sciences, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, ChinaChongqing Mineral Resources Development Co., Ltd., Chongqing 40042, ChinaSchool of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, ChinaThe 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.http://dx.doi.org/10.1155/2016/7948612 |
| spellingShingle | Xiangyu Fan Fenglin Xu Lin Chen Qiao Chen Zhiwei Liu Guanghua Yao Wen Nie Dimension Analysis-Based Model for Prediction of Shale Compressive Strength Advances in Materials Science and Engineering |
| title | Dimension Analysis-Based Model for Prediction of Shale Compressive Strength |
| title_full | Dimension Analysis-Based Model for Prediction of Shale Compressive Strength |
| title_fullStr | Dimension Analysis-Based Model for Prediction of Shale Compressive Strength |
| title_full_unstemmed | Dimension Analysis-Based Model for Prediction of Shale Compressive Strength |
| title_short | Dimension Analysis-Based Model for Prediction of Shale Compressive Strength |
| title_sort | dimension analysis based model for prediction of shale compressive strength |
| url | http://dx.doi.org/10.1155/2016/7948612 |
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