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Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network

Oil well cement is prone to corrosion and damage in carbon dioxide (CO(2)) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well ce...

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Autores principales: Zhao, Jun, Chen, Rongyao, Liu, Shikang, Zhou, Shanshan, Xu, Mingbiao, Dai, Feixu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674542/
https://www.ncbi.nlm.nih.gov/pubmed/38006165
http://dx.doi.org/10.3390/polym15224441
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author Zhao, Jun
Chen, Rongyao
Liu, Shikang
Zhou, Shanshan
Xu, Mingbiao
Dai, Feixu
author_facet Zhao, Jun
Chen, Rongyao
Liu, Shikang
Zhou, Shanshan
Xu, Mingbiao
Dai, Feixu
author_sort Zhao, Jun
collection PubMed
description Oil well cement is prone to corrosion and damage in carbon dioxide (CO(2)) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO(2), and the corrosion depth was increased. The R(2) of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO(2), the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance.
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spelling pubmed-106745422023-11-17 Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network Zhao, Jun Chen, Rongyao Liu, Shikang Zhou, Shanshan Xu, Mingbiao Dai, Feixu Polymers (Basel) Article Oil well cement is prone to corrosion and damage in carbon dioxide (CO(2)) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO(2), and the corrosion depth was increased. The R(2) of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO(2), the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance. MDPI 2023-11-17 /pmc/articles/PMC10674542/ /pubmed/38006165 http://dx.doi.org/10.3390/polym15224441 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Zhao, Jun
Chen, Rongyao
Liu, Shikang
Zhou, Shanshan
Xu, Mingbiao
Dai, Feixu
Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title_full Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title_fullStr Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title_full_unstemmed Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title_short Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
title_sort corrosion degree evaluation of polymer anti-corrosive oil well cement under an acidic geological environment using an artificial neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674542/
https://www.ncbi.nlm.nih.gov/pubmed/38006165
http://dx.doi.org/10.3390/polym15224441
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