Prediction of Casing Collapse Strength Based on Bayesian Neural Network

With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of strin...

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Vydáno v:Processes Ročník 10; číslo 7; s. 1327
Hlavní autoři: Li, Dongfeng, Fan, Heng, Wang, Rui, Yang, Shangyu, Zhao, Yating, Yan, Xiangzhen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2022
Témata:
Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
Collapse
Continuous extrusion
Data transfer (computers)
Empirical analysis
Error analysis
Expected values
Gas industry
Machine learning
Mechanical properties
Neural networks
Prediction models
Regularization
Regularization methods
Residual stress
Training
Trial and error methods
Yield stress
ISSN:2227-9717, 2227-9717
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Abstract With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of string ovality, uneven wall thickness, residual stress, and other factors to high anti-collapse casing, the API formula has a big error in predicting the anti-collapse strength of high anti-collapse casing. Therefore, Bayesian regularization artificial neural network (BRANN) is used to predict the external collapse strength of high anti-collapse casing. By collecting full-scale physical data, including initial defect data, geometric size, mechanical parameters, etc., after data preprocessing, the casing collapse strength data set is established for model training and blind measurement. Under the classical three-layer neural network, the Bayesian regularization algorithm is used for training. Through empirical formula and trial and error method, it is determined that when the number of hidden neurons is 12, the model is the best prediction model for high collapse resistance casing. The prediction results of the blind test data imported by the model show that the coincidence rate of BRANN casing collapse strength prediction can reach 96.67%. Through error analysis with API formula prediction results and KT formula prediction results improved by least square fitting, the BRANN-based casing collapse strength prediction has higher accuracy and stability. Compared with the traditional prediction method, this model can be used to predict casing strength under more complicated working conditions, and it has a certain guiding significance.
AbstractList With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of string ovality, uneven wall thickness, residual stress, and other factors to high anti-collapse casing, the API formula has a big error in predicting the anti-collapse strength of high anti-collapse casing. Therefore, Bayesian regularization artificial neural network (BRANN) is used to predict the external collapse strength of high anti-collapse casing. By collecting full-scale physical data, including initial defect data, geometric size, mechanical parameters, etc., after data preprocessing, the casing collapse strength data set is established for model training and blind measurement. Under the classical three-layer neural network, the Bayesian regularization algorithm is used for training. Through empirical formula and trial and error method, it is determined that when the number of hidden neurons is 12, the model is the best prediction model for high collapse resistance casing. The prediction results of the blind test data imported by the model show that the coincidence rate of BRANN casing collapse strength prediction can reach 96.67%. Through error analysis with API formula prediction results and KT formula prediction results improved by least square fitting, the BRANN-based casing collapse strength prediction has higher accuracy and stability. Compared with the traditional prediction method, this model can be used to predict casing strength under more complicated working conditions, and it has a certain guiding significance.
Author Yang, Shangyu
Zhao, Yating
Yan, Xiangzhen
Fan, Heng
Li, Dongfeng
Wang, Rui
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Cites_doi 10.1007/s11771-014-2324-6
10.1016/j.jlp.2018.10.009
10.1016/j.petrol.2020.107811
10.1016/j.oceaneng.2018.04.098
10.1016/S1876-3804(20)60055-6
10.1109/SAMI.2017.7880337
ContentType Journal Article
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Snippet With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API...
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SubjectTerms Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
Collapse
Continuous extrusion
Data transfer (computers)
Empirical analysis
Error analysis
Expected values
Gas industry
Machine learning
Mechanical properties
Neural networks
Prediction models
Regularization
Regularization methods
Residual stress
Training
Trial and error methods
Yield stress
Title Prediction of Casing Collapse Strength Based on Bayesian Neural Network
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