Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta

Utiri Collins David; Elizabeth Chinyerem Ndubuisi

doi:doi:10.11648/j.pse.20261002.14

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Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta

Utiri Collins David^*

, Elizabeth Chinyerem Ndubuisi

Published in Petroleum Science and Engineering (Volume 10, Issue 2)

Received: 26 September 2026 Accepted: 9 October 2026 Published: 12 November 2026

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Abstract

Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CR_eff=CR_base(1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R²=0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.

Published in	Petroleum Science and Engineering (Volume 10, Issue 2)
DOI	10.11648/j.pse.20261002.14
Page(s)	115-129
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), 2026. Published by Science Publishing Group

Keywords

Risk-based Inspection, Pipeline Corrosion, Predictive Maintenance, Cross-validation, Niger Delta, Asset Integrity

References

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[2]	American Petroleum Institute (API). API RP 581: Risk-based Inspection Methodology, 4th ed. API. 2025.
[3]	Khan, F., Yarveisy, R., Abbassi, R. Risk-based pipeline integrity management: a road map for the resilient pipelines. Journal of Pipeline Science and Engineering. 2021, 1(1), 74-87. https://doi.org/10.1016/j.jpse.2021.02.001
[4]	Huang, Y., Qin, G., Yang, M. A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents. Process Safety and Environmental Protection. 2023, 180, 588-600. https://doi.org/10.1016/j.psep.2023.10.025
[5]	Nairametrics. Nigeria recorded over 589 oil spills in 2024, most caused by oil theft. Nairametrics, 17 January 2025 (citing NOSDRA Oil Spill Monitor data).
[6]	Ozougwu, P. E., Madu, C. N., Chukwuorji, J. C., Ozougwu, A. O., Ozougwu, S. U. Environmental identities and attitude towards crude oil pipeline vandalism in Niger Delta oil-producing communities. Sustainability. 2023, 15(6), 5610. https://doi.org/10.3390/su15065610
[7]	Jacob, J., Varadharajan, R. Robust variance inflation factor: a promising approach for collinearity diagnostics in the presence of outliers. Sankhya B. 2024, 86(2), 845-871. https://doi.org/10.1007/s13571-024-00342-y
[8]	Javaherdashti, R. Editorial: Microbiologically influenced corrosion (MIC): its mechanisms, technological, economic, and environmental impacts. Frontiers in Microbiology. 2023, 14, 1249565. https://doi.org/10.3389/fmicb.2023.1249565
[9]	Santhosh Kumar, A., Sivakumar, L., Rajadesingu, S., Sathish, S., Malik, T., Parthipan, P. Sustainable corrosion inhibition approaches for the mitigation of microbiologically influenced corrosion—a systematic review. Frontiers in Materials. 2025, 12, 1545245. https://doi.org/10.3389/fmats.2025.1545245
[10]	Prokhorov, O., Prokhorov, V., Tevyashev, A., Khussanov, A., Khussanov, Z., Turdybekova, D. A method for importance and risk assessment of main pipeline facilities. Eastern-European Journal of Enterprise Technologies. 2023, 4(3), 33-44. https://doi.org/10.15587/1729-4061.2023.285862
[11]	Dong, Z., Zhang, M., Li, W., Wen, F., Dong, G., Zou, L., Zhang, Y. Development of a predictive model for carbon dioxide corrosion rate and severity based on machine learning algorithms. Materials. 2024, 17(16), 4046. https://doi.org/10.3390/ma17164046
[12]	Khalaf, A. H., Lin, B., Abdalla, A. N., Han, Z., Xiao, Y., Tang, J. Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models. Results in Engineering. 2024, 24, 102853. https://doi.org/10.1016/j.rineng.2024.102853
[13]	Geroldinger, A., Lusa, L., Nold, M., Heinze, G. Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures—a simulation study. Diagnostic and Prognostic Research. 2023, 7, 9. https://doi.org/10.1186/s41512-023-00146-0
[14]	Li, X., Liu, Y., Han, Z., Chen, G. A risk-based maintenance decision model for subsea pipeline considering pitting corrosion growth. Process Safety and Environmental Protection. 2024, 184, 1306-1317. https://doi.org/10.1016/j.psep.2024.02.072
[15]	International Organization for Standardization (ISO). ISO 55001: Asset Management — Management Systems — Requirements. ISO. 2024.

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APA Style

David, U. C., Ndubuisi, E. C. (2026). Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Petroleum Science and Engineering, 10(2), 115-129. https://doi.org/10.11648/j.pse.20261002.14

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ACS Style

David, U. C.; Ndubuisi, E. C. Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Pet. Sci. Eng. 2026, 10(2), 115-129. doi: 10.11648/j.pse.20261002.14

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AMA Style

David UC, Ndubuisi EC. Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Pet Sci Eng. 2026;10(2):115-129. doi: 10.11648/j.pse.20261002.14

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@article{10.11648/j.pse.20261002.14,
  author = {Utiri Collins David and Elizabeth Chinyerem Ndubuisi},
  title = {Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta},
  journal = {Petroleum Science and Engineering},
  volume = {10},
  number = {2},
  pages = {115-129},
  doi = {10.11648/j.pse.20261002.14},
  url = {https://doi.org/10.11648/j.pse.20261002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20261002.14},
  abstract = {Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CReff=CRbase (1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R2=0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.},
 year = {2026}
}

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TY  - JOUR
T1  - Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta
AU  - Utiri Collins David
AU  - Elizabeth Chinyerem Ndubuisi
Y1  - 2026/11/12
PY  - 2026
N1  - https://doi.org/10.11648/j.pse.20261002.14
DO  - 10.11648/j.pse.20261002.14
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 115
EP  - 129
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20261002.14
AB  - Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CReff=CRbase (1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R2=0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.
VL  - 10
IS  - 2
ER  -

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Author Information

Utiri Collins David

Institute of Engineering, Technology and Innovation Management (METI), University of Port Harcourt, Port Harcourt, Nigeria

Contact Email

http://orcid.org/0009-0009-8417-1088
Elizabeth Chinyerem Ndubuisi

Department of Petroleum and Gas Engineering, University of Port Harcourt, Port Harcourt, Nigeria

Contact Email

http://orcid.org/0000-0002-5234-9875

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Plain Text BibTeX RIS

APA Style

David, U. C., Ndubuisi, E. C. (2026). Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Petroleum Science and Engineering, 10(2), 115-129. https://doi.org/10.11648/j.pse.20261002.14

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ACS Style

David, U. C.; Ndubuisi, E. C. Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Pet. Sci. Eng. 2026, 10(2), 115-129. doi: 10.11648/j.pse.20261002.14

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AMA Style

David UC, Ndubuisi EC. Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta. Pet Sci Eng. 2026;10(2):115-129. doi: 10.11648/j.pse.20261002.14

Copy | Download

@article{10.11648/j.pse.20261002.14,
  author = {Utiri Collins David and Elizabeth Chinyerem Ndubuisi},
  title = {Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta},
  journal = {Petroleum Science and Engineering},
  volume = {10},
  number = {2},
  pages = {115-129},
  doi = {10.11648/j.pse.20261002.14},
  url = {https://doi.org/10.11648/j.pse.20261002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20261002.14},
  abstract = {Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CReff=CRbase (1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R2=0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta
AU  - Utiri Collins David
AU  - Elizabeth Chinyerem Ndubuisi
Y1  - 2026/11/12
PY  - 2026
N1  - https://doi.org/10.11648/j.pse.20261002.14
DO  - 10.11648/j.pse.20261002.14
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 115
EP  - 129
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20261002.14
AB  - Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CReff=CRbase (1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R2=0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.
VL  - 10
IS  - 2
ER  -

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