Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network
After drilling a borehole in the ground and in a rocky environment, the materials around the borehole are crushed and separated in layers from the borehole wall; this causes the borehole cross section to lose its original circular shape, which redistributes stresses and further failure. This type of...
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| Format: | Article |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4958004 |
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| author | Somaie Jolfaei Ali Lakirouhani |
| author_facet | Somaie Jolfaei Ali Lakirouhani |
| author_sort | Somaie Jolfaei |
| collection | DOAJ |
| description | After drilling a borehole in the ground and in a rocky environment, the materials around the borehole are crushed and separated in layers from the borehole wall; this causes the borehole cross section to lose its original circular shape, which redistributes stresses and further failure. This type of episodic failure, which occurs symmetrically and V-shaped on both sides of the borehole and along the minor principal stress, is called breakout. The dimensions of breakout, i.e., its depth and width, are two important indicators that have recently been used in estimating in situ stresses; however, the dimensions of the breakout area depend on the in situ stresses and mechanical properties of the rock, which have not been well addressed so far. This paper presents a comprehensive investigation of breakout dimensions using finite element numerical analysis. The proposed numerical model is based on the equations governing the two-dimensional breakout phenomenon under nonisotropic in situ stresses and plane strain condition. According to the results, increasing the failure function of the area around the breakout tip causes the breakout to expand, until the failure function is less than 1 for all points around the breakout tip, at which point the breakout expansion is stopped and breakout reaches stability. In the other part of the article, using 121 datasets obtained from numerical analysis, an artificial neural network is trained to predict breakout dimensions based on the input parameters of the problem. The main finding of this section is a model that shows that among the parameters affecting the borehole breakout, the internal friction angle of the rock has the greatest impact on the dimensions of the breakout. |
| format | Article |
| id | doaj-art-4dc43cae6b984f8f9cd15ce21efbf5d8 |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4dc43cae6b984f8f9cd15ce21efbf5d82025-08-20T02:38:45ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/4958004Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural NetworkSomaie Jolfaei0Ali Lakirouhani1Department of Civil EngineeringDepartment of Civil EngineeringAfter drilling a borehole in the ground and in a rocky environment, the materials around the borehole are crushed and separated in layers from the borehole wall; this causes the borehole cross section to lose its original circular shape, which redistributes stresses and further failure. This type of episodic failure, which occurs symmetrically and V-shaped on both sides of the borehole and along the minor principal stress, is called breakout. The dimensions of breakout, i.e., its depth and width, are two important indicators that have recently been used in estimating in situ stresses; however, the dimensions of the breakout area depend on the in situ stresses and mechanical properties of the rock, which have not been well addressed so far. This paper presents a comprehensive investigation of breakout dimensions using finite element numerical analysis. The proposed numerical model is based on the equations governing the two-dimensional breakout phenomenon under nonisotropic in situ stresses and plane strain condition. According to the results, increasing the failure function of the area around the breakout tip causes the breakout to expand, until the failure function is less than 1 for all points around the breakout tip, at which point the breakout expansion is stopped and breakout reaches stability. In the other part of the article, using 121 datasets obtained from numerical analysis, an artificial neural network is trained to predict breakout dimensions based on the input parameters of the problem. The main finding of this section is a model that shows that among the parameters affecting the borehole breakout, the internal friction angle of the rock has the greatest impact on the dimensions of the breakout.http://dx.doi.org/10.1155/2022/4958004 |
| spellingShingle | Somaie Jolfaei Ali Lakirouhani Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network Advances in Civil Engineering |
| title | Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network |
| title_full | Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network |
| title_fullStr | Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network |
| title_full_unstemmed | Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network |
| title_short | Sensitivity Analysis of Effective Parameters in Borehole Failure, Using Neural Network |
| title_sort | sensitivity analysis of effective parameters in borehole failure using neural network |
| url | http://dx.doi.org/10.1155/2022/4958004 |
| work_keys_str_mv | AT somaiejolfaei sensitivityanalysisofeffectiveparametersinboreholefailureusingneuralnetwork AT alilakirouhani sensitivityanalysisofeffectiveparametersinboreholefailureusingneuralnetwork |