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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Veröffentlicht in:Processes Jg. 10; H. 7; S. 1327
Hauptverfasser: Li, Dongfeng, Fan, Heng, Wang, Rui, Yang, Shangyu, Zhao, Yating, Yan, Xiangzhen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.07.2022
Schlagworte:
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
Copyright 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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References Zhang (ref_2) 2018; 40
Zhang (ref_8) 2017; 40
ref_12
Liu (ref_21) 2020; 40
Tan (ref_9) 2020; 42
ref_19
Han (ref_1) 2001; 24
Li (ref_13) 2015; 25
Lin (ref_18) 2014; 21
Xia (ref_6) 2021; 44
ref_15
Negash (ref_10) 2020; 47
Zhang (ref_4) 2018; 33
Jiao (ref_23) 2017; 40
Pan (ref_14) 2012; 2
Xiao (ref_22) 2020; 17
ref_24
Shi (ref_17) 2019; 57
ref_20
Lou (ref_3) 2012; 41
Negash (ref_11) 2018; 12
Mohamadian (ref_25) 2020; 196
Shi (ref_16) 2018; 161
ref_5
ref_7
References_xml – ident: ref_7
– ident: ref_5
– volume: 44
  start-page: 5
  year: 2021
  ident: ref_6
  article-title: PEMFC stack modeling based on Bayesian regularization BP neural network
  publication-title: J. Hefei Univ. Technol.
– volume: 40
  start-page: 94
  year: 2020
  ident: ref_21
  article-title: Calculation and experiment of anti-collapse performance of titanium alloy tubing and casing
  publication-title: Nat. Gas Ind.
– volume: 41
  start-page: 38
  year: 2012
  ident: ref_3
  article-title: Experimental study on the main influencing factors of casing collapse strength
  publication-title: Pet. Field Mach.
– volume: 33
  start-page: 1319
  year: 2018
  ident: ref_4
  article-title: Research on the application of Bayesian neural network method in casing loss prediction
  publication-title: Prog. Geophys.
– volume: 21
  start-page: 3470
  year: 2014
  ident: ref_18
  article-title: Theoretical and experimental analyses of casing collapsing strength under non-uniform loading
  publication-title: J. Cent. South Univ.
  doi: 10.1007/s11771-014-2324-6
– volume: 40
  start-page: 736
  year: 2018
  ident: ref_2
  article-title: Quantitative failure risk analysis of shale gas well casing deformation based on Bayesian network
  publication-title: Pet. Drill. Prod. Technol.
– volume: 40
  start-page: 4
  year: 2017
  ident: ref_8
  article-title: Hidden layer node estimation algorithm of BP network based on simulated annealing
  publication-title: J. Hefei Univ. Technol.
– volume: 57
  start-page: 131
  year: 2019
  ident: ref_17
  article-title: Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform
  publication-title: J. Loss Prev. Process Ind.
  doi: 10.1016/j.jlp.2018.10.009
– volume: 17
  start-page: 39
  year: 2020
  ident: ref_22
  article-title: Model and optimization of the collapse strength of casing-cement ring combination under non-uniform ground stress
  publication-title: J. Yangtze Univ.
– volume: 25
  start-page: 75
  year: 2015
  ident: ref_13
  article-title: Bayesian dynamic model for risk analysis of submarine oil and gas pipeline leakage accidents
  publication-title: Chin. Saf. Sci. J.
– ident: ref_12
– volume: 2
  start-page: 45
  year: 2012
  ident: ref_14
  article-title: Application of Bayesian Normalization Algorithm in Reservoir Parameter Fitting
  publication-title: Internet Things Technol.
– volume: 24
  start-page: 48
  year: 2001
  ident: ref_1
  article-title: Preliminary study on casing collapse strength under non-uniform load
  publication-title: Drill. Technol.
– volume: 12
  start-page: 67
  year: 2018
  ident: ref_11
  article-title: Application of artificial neural networks for calibration of a reservoir model
  publication-title: Intell. Decis. Technol.
– volume: 196
  start-page: 107811
  year: 2020
  ident: ref_25
  article-title: A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning
  publication-title: J. Pet. Sci. Eng.
  doi: 10.1016/j.petrol.2020.107811
– volume: 161
  start-page: 98
  year: 2018
  ident: ref_16
  article-title: Robust data-driven model to study dispersion of vapor cloud in offshore facility
  publication-title: Ocean. Eng.
  doi: 10.1016/j.oceaneng.2018.04.098
– ident: ref_15
– ident: ref_19
– volume: 47
  start-page: 357
  year: 2020
  ident: ref_10
  article-title: Production prediction of waterflooding reservoir based on artificial neural network
  publication-title: Pet. Explor. Dev.
  doi: 10.1016/S1876-3804(20)60055-6
– volume: 42
  start-page: 7
  year: 2020
  ident: ref_9
  article-title: Optimization of shale gas fracturing construction parameters based on PCA-BNN
  publication-title: J. Southwest Pet. Univ.
– ident: ref_24
  doi: 10.1109/SAMI.2017.7880337
– ident: ref_20
– volume: 40
  start-page: 20
  year: 2017
  ident: ref_23
  article-title: P110 steel grade Φ139.7mm × 10.54mm high collapse resistance performance analysis and collapse strength prediction
  publication-title: Welded Pipe
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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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