 | | Mechanics of Solids
A Journal of Russian Academy of Sciences | | Founded
in January 1966
Issued 6 times a year
Print ISSN 0025-6544
Online ISSN 1934-7936 |
Archive of Issues
| Total articles in the database: | | 12788 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8028
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| In English (Mech. Solids): | | 4760 |
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| Y. Xia, B. Zhou, C. Zhang, X. Zhu, S. Zhou, J. Li, H. Wang, and C. Wang, "Analytical Method Based on Machine Learning (AM-BML) for a Cased Borehole under Anisotropic In-Situ Stresses in Formation," Mech. Solids. 59 (3), 1807-1822 (2024) |
| Year |
2024 |
Volume |
59 |
Number |
3 |
Pages |
1807-1822 |
| DOI |
10.1134/S0025654424603884 |
| Title |
Analytical Method Based on Machine Learning (AM-BML) for a Cased Borehole under Anisotropic In-Situ Stresses in Formation |
| Author(s) |
Y. Xia (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580 China;CNPC Tianjin Bo-Xing Engineering Science &Technology Co., Ltd., Tianjin, 300451 China)
B. Zhou (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China, zhoubo@upc.edu.cn)
C. Zhang (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China)
X. Zhu (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China)
S. Zhou (Institute of Advanced Machines, Zhejiang University, Hangzhou, 311106 China)
J. Li (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China)
H. Wang (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China)
C. Wang (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580 China, wangcw@upc.edu.cn) |
| Abstract |
In this work, an analytical method based on machine learning (AM-BML) is proposed to predict the stress distribution around a cased borehole in the formation with anisotropic in-situ stresses. Firstly, the stress field equations with undetermined coefficients are derived using the elasticity theory to formulate the stress field near the cased borehole. Secondly, the regression functions of a machine learning algorithm, least squares support vector machine (LS-SVM), are constructed
according to the derived stress field equations. Thirdly, the undetermined coefficient equations are
developed to determine the undetermined coefficients in the derived stress field equations according
to the constructed LS-SVM regression functions and the derived stress field equations. The derived
stress field equations and the developed undetermined coefficient equations together constitute the
proposed AM-BML, which can well predict the stress distribution around a cased borehole in the formation with anisotropic in-situ stresses. Compared with the traditional analytical methods, the proposed AM-BML is more convenient for practical applications because it is difficult and complex to
determine the undetermined coefficient in the stress field equations according to the traditional analytical methods. Finally, the proposed AM-BML is validated through the comparisons with numerical
simulation experiments; and it is also used to investigate the influencing factors on the stress field of a
cased borehole system, which gives some useful results. This work is helpful for the study of borehole
stability and the other study related to the petroleum engineering. |
| Keywords |
stress field, cased borehole, anisotropic in-situ stresses, analytical method, machine learning |
| Received |
16 May 2024 | Revised |
19 June 2024 | Accepted |
20 June 2024 |
| Link to Fulltext |
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