Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods

Abubakar Usman; Alhaji Dodo Ibrahim Sulaiman; Usman Hassan; Ahmed Mohammed Inuwa

doi:doi:10.11648/j.pse.20250902.22

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Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods

Abubakar Usman^*, Alhaji Dodo Ibrahim Sulaiman, Usman Hassan, Ahmed Mohammed Inuwa

Published in Petroleum Science and Engineering (Volume 9, Issue 2)

Received: 27 November 2025 Accepted: 16 December 2025 Published: 31 December 2025

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Abstract

Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.

Published in	Petroleum Science and Engineering (Volume 9, Issue 2)
DOI	10.11648/j.pse.20250902.22
Page(s)	173-188
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence, Predictive Maintenance, Machine Learning Optimization, Pipeline Corrosion Detection, Oil and Gas Pipeline Integrity

References

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APA Style

Usman, A., Sulaiman, A. D. I., Hassan, U., Inuwa, A. M. (2025). Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Petroleum Science and Engineering, 9(2), 173-188. https://doi.org/10.11648/j.pse.20250902.22

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ACS Style

Usman, A.; Sulaiman, A. D. I.; Hassan, U.; Inuwa, A. M. Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Pet. Sci. Eng. 2025, 9(2), 173-188. doi: 10.11648/j.pse.20250902.22

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AMA Style

Usman A, Sulaiman ADI, Hassan U, Inuwa AM. Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Pet Sci Eng. 2025;9(2):173-188. doi: 10.11648/j.pse.20250902.22

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@article{10.11648/j.pse.20250902.22,
  author = {Abubakar Usman and Alhaji Dodo Ibrahim Sulaiman and Usman Hassan and Ahmed Mohammed Inuwa},
  title = {Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {2},
  pages = {173-188},
  doi = {10.11648/j.pse.20250902.22},
  url = {https://doi.org/10.11648/j.pse.20250902.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250902.22},
  abstract = {Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.},
 year = {2025}
}

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TY  - JOUR
T1  - Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods
AU  - Abubakar Usman
AU  - Alhaji Dodo Ibrahim Sulaiman
AU  - Usman Hassan
AU  - Ahmed Mohammed Inuwa
Y1  - 2025/12/31
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250902.22
DO  - 10.11648/j.pse.20250902.22
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 173
EP  - 188
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250902.22
AB  - Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.
VL  - 9
IS  - 2
ER  -

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Author Information

Abubakar Usman

Department of Petroleum Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

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Alhaji Dodo Ibrahim Sulaiman

Department of Petroleum Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

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Usman Hassan

Department of Petroleum Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

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Ahmed Mohammed Inuwa

Department of Chemical Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

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APA Style

Usman, A., Sulaiman, A. D. I., Hassan, U., Inuwa, A. M. (2025). Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Petroleum Science and Engineering, 9(2), 173-188. https://doi.org/10.11648/j.pse.20250902.22

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ACS Style

Usman, A.; Sulaiman, A. D. I.; Hassan, U.; Inuwa, A. M. Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Pet. Sci. Eng. 2025, 9(2), 173-188. doi: 10.11648/j.pse.20250902.22

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AMA Style

Usman A, Sulaiman ADI, Hassan U, Inuwa AM. Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods. Pet Sci Eng. 2025;9(2):173-188. doi: 10.11648/j.pse.20250902.22

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@article{10.11648/j.pse.20250902.22,
  author = {Abubakar Usman and Alhaji Dodo Ibrahim Sulaiman and Usman Hassan and Ahmed Mohammed Inuwa},
  title = {Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {2},
  pages = {173-188},
  doi = {10.11648/j.pse.20250902.22},
  url = {https://doi.org/10.11648/j.pse.20250902.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250902.22},
  abstract = {Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Systematic Corrosion Prediction Techniques in Oil and Gas Pipelines Using Machine Learning Methods
AU  - Abubakar Usman
AU  - Alhaji Dodo Ibrahim Sulaiman
AU  - Usman Hassan
AU  - Ahmed Mohammed Inuwa
Y1  - 2025/12/31
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250902.22
DO  - 10.11648/j.pse.20250902.22
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 173
EP  - 188
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250902.22
AB  - Corrosion in oil and gas pipelines results in billions of dollars in annual losses and poses serious safety, environmental, and operational risks. Conventional detection methods are often labor-intensive, intermittent, and insufficiently accurate, hindering early identification of critical degradation. This study proposes an intelligent, SCADA-driven corrosion detection framework that integrates advanced machine learning (ML) and artificial intelligence (AI) techniques for real-time pipeline integrity monitoring. Operational parameters, including gas and liquid flow rates (up to 250 m³/h), pressure drops (0.3–0.8 MPa), and phase densities (600–900 kg/m³), were acquired from SCADA systems, pre-processed in MATLAB, and divided into training (30%), validation (30%), and testing (40%) datasets. Supervised ML models, Support Vector Machines (SVM), Random Forests, Boosted Trees, and Neural Networks, were optimized through feature selection and hyperparameter tuning for corrosion detection and rate prediction. The Linear SVM achieved the highest performance, with a ROC-AUC of 0.96 and a prediction tolerance of ± 0.02 mm/year. The integrated SCADA–AI framework achieved detection accuracies above 90%, enabling proactive maintenance scheduling and extending pipeline service life by 12–15%. These findings demonstrate a scalable, data-driven approach for predictive corrosion management and enhanced asset reliability in energy infrastructure.
VL  - 9
IS  - 2
ER  -

Copy | Download