Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning

With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational...
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Gespeichert in:

Autor*in:	Peng Wang [verfasserIn] Chengxu Zhong [verfasserIn] Shuai Fan [verfasserIn] Dongfeng Li [verfasserIn] Shengyue Zhang [verfasserIn] Peihang Liu [verfasserIn] Yu Ji [verfasserIn] Heng Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	prediction of collapsing strength high-strength collapse-resistant casing machine learning

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 11(2023), 3007, p 3007
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:3007, p 3007

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr11103007

Katalog-ID:	DOAJ096835958

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520			\|a With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements.
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spelling	10.3390/pr11103007 doi (DE-627)DOAJ096835958 (DE-599)DOAJ2078ab1fbe8f460dadeb7961b957f9f3 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Peng Wang verfasserin aut Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements. prediction of collapsing strength high-strength collapse-resistant casing machine learning Chemical technology Chemistry Chengxu Zhong verfasserin aut Shuai Fan verfasserin aut Dongfeng Li verfasserin aut Shengyue Zhang verfasserin aut Peihang Liu verfasserin aut Yu Ji verfasserin aut Heng Fan verfasserin aut In Processes MDPI AG, 2013 11(2023), 3007, p 3007 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3007, p 3007 https://doi.org/10.3390/pr11103007 kostenfrei https://doaj.org/article/2078ab1fbe8f460dadeb7961b957f9f3 kostenfrei https://www.mdpi.com/2227-9717/11/10/3007 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3007, p 3007
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allfieldsGer	10.3390/pr11103007 doi (DE-627)DOAJ096835958 (DE-599)DOAJ2078ab1fbe8f460dadeb7961b957f9f3 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Peng Wang verfasserin aut Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements. prediction of collapsing strength high-strength collapse-resistant casing machine learning Chemical technology Chemistry Chengxu Zhong verfasserin aut Shuai Fan verfasserin aut Dongfeng Li verfasserin aut Shengyue Zhang verfasserin aut Peihang Liu verfasserin aut Yu Ji verfasserin aut Heng Fan verfasserin aut In Processes MDPI AG, 2013 11(2023), 3007, p 3007 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3007, p 3007 https://doi.org/10.3390/pr11103007 kostenfrei https://doaj.org/article/2078ab1fbe8f460dadeb7961b957f9f3 kostenfrei https://www.mdpi.com/2227-9717/11/10/3007 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3007, p 3007
allfieldsSound	10.3390/pr11103007 doi (DE-627)DOAJ096835958 (DE-599)DOAJ2078ab1fbe8f460dadeb7961b957f9f3 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Peng Wang verfasserin aut Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements. prediction of collapsing strength high-strength collapse-resistant casing machine learning Chemical technology Chemistry Chengxu Zhong verfasserin aut Shuai Fan verfasserin aut Dongfeng Li verfasserin aut Shengyue Zhang verfasserin aut Peihang Liu verfasserin aut Yu Ji verfasserin aut Heng Fan verfasserin aut In Processes MDPI AG, 2013 11(2023), 3007, p 3007 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:3007, p 3007 https://doi.org/10.3390/pr11103007 kostenfrei https://doaj.org/article/2078ab1fbe8f460dadeb7961b957f9f3 kostenfrei https://www.mdpi.com/2227-9717/11/10/3007 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 3007, p 3007
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callnumber-first	T - Technology
author	Peng Wang
spellingShingle	Peng Wang misc TP1-1185 misc QD1-999 misc prediction of collapsing strength misc high-strength collapse-resistant casing misc machine learning misc Chemical technology misc Chemistry Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning
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topic_title	TP1-1185 QD1-999 Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning prediction of collapsing strength high-strength collapse-resistant casing machine learning
topic	misc TP1-1185 misc QD1-999 misc prediction of collapsing strength misc high-strength collapse-resistant casing misc machine learning misc Chemical technology misc Chemistry
topic_unstemmed	misc TP1-1185 misc QD1-999 misc prediction of collapsing strength misc high-strength collapse-resistant casing misc machine learning misc Chemical technology misc Chemistry
topic_browse	misc TP1-1185 misc QD1-999 misc prediction of collapsing strength misc high-strength collapse-resistant casing misc machine learning misc Chemical technology misc Chemistry
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title	Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning
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title_full	Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning
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author_browse	Peng Wang Chengxu Zhong Shuai Fan Dongfeng Li Shengyue Zhang Peihang Liu Yu Ji Heng Fan
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title_sort	prediction of collapsing strength of high-strength collapse-resistant casing based on machine learning
callnumber	TP1-1185
title_auth	Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning
abstract	With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements.
abstractGer	With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements.
abstract_unstemmed	With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements.
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title_short	Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning
url	https://doi.org/10.3390/pr11103007 https://doaj.org/article/2078ab1fbe8f460dadeb7961b957f9f3 https://www.mdpi.com/2227-9717/11/10/3007 https://doaj.org/toc/2227-9717
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author2	Chengxu Zhong Shuai Fan Dongfeng Li Shengyue Zhang Peihang Liu Yu Ji Heng Fan
author2Str	Chengxu Zhong Shuai Fan Dongfeng Li Shengyue Zhang Peihang Liu Yu Ji Heng Fan
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doi_str	10.3390/pr11103007
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up_date	2024-07-03T22:33:59.743Z
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The correlation coefficient R<sup<2</sup<, root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R<sup<2</sup< is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). 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Prediction of Collapsing Strength of High-Strength Collapse-Resistant Casing Based on Machine Learning

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