Multi-objective Optimization of Risk-Based Inspection Planning for Oil and Gas Piping Systems Using NSGA-II Algorithm

Tri Wahono; Endah R.M. Putri; Imam Mukhlash; Agung Purniawan

doi:10.29017/scog.v49i2.2084

Authors

Tri Wahono Institut Teknologi Sepuluh Nopember
Endah R.M. Putri Institut Teknologi Sepuluh Nopember
Imam Mukhlash Institut Teknologi Sepuluh Nopember
Agung Purniawan Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.29017/scog.v49i2.2084

Keywords:

inspection planning, multi-objective optimization, oil and gas, piping, risk-based inspection

Abstract

The systematic examination of piping is essential for the successful implementation of risk-based inspection. The ineffective execution of this activity increases the risk of equipment failure and unnecessary costs. Therefore, this study aims to address the challenge of optimizing inspection planning for oil and gas piping systems within budgetary and operational constraints. The most effective inspection methods are selected to determine the optimal inspection interval. A multi-objective optimization model based on the non-dominated sorting genetic algorithm-II is applied to simultaneously minimize the probability of failure and optimize total inspection costs. The adopted methodology, which differs from traditional methods, eliminates reliance on predefined risk thresholds and explicit failure-consequence evaluations by directly incorporating inspection costs. Additionally, the best-performing model achieves an efficient solution by reducing the total probability of failure from 1.631 10^-1 to 0.8975 10^-1. The application model further provides a robust decision-support tool for efficient inspection planning, increases inspection effectiveness, and reduces the risk of failure. These developments form the basis for effective decision-making to support the implementation of asset integrity management through efficient inspection planning.

References

Abubakirov, R., Yang, M., et al. (2020). A risk-based approach to determination of optimal inspection intervals for buried oil pipelines. Process Safety and Environmental Protection, 134, 95–107. https://doi.org/10.1016/j.psep.2019.11.031

Aditiyawarman, T., Kaban, A. P. S., et al. (2023). A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry: An Artificial Intelligence Perspective. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 9(1). https://doi.org/10.1115/1.4054558

API RP 580. (2023). Elements of a Risk-Based Inspection Program. In API (Fourth Ed., Issue August). American Petroleum Institute.

API RP 581. (2025). Risk-Based Inspection Technology. January.

Asyik, N., Latief, R. U., et al. (2026). Testing Center for Oil and Gas Risk - Informed Strategic Governance in Precast Concrete Production as A High - Risk Industry : Cross - Sector Evidence from The Construction and Oil & Gas Industries. Scientific Contributions Oil and Gas, 49(1), 459–474. https://doi.org/https://doi.org/10.29017/scog.v49i1.1956

Beume, N., Naujoks, B., et al. (2007). SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3), 1653–1669. https://doi.org/10.1016/j.ejor.2006.08.008

Campari, A., Vianello, C., et al. (2024). Machine learning-aided risk-based inspection strategy for hydrogen technologies. Process Safety and Environmental Protection, 191(PA), 1239–1253. https://doi.org/10.1016/j.psep.2024.09.031

Deb, K., Pratap, A., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

Effendi, D., Hani, B., et al. (2020). Determination of Water Characteristics at Gathering Station oil Field Y According to Minister of Environment Regulation No . 19 Year 2010. Lembaran Publikasi Minyak Dan Gas Bumi, 54(2), 111–125. https://doi.org/10.29017/LPMGB.54.2.421

Huang, W. L. Z. Z. S. (2020). Knee Point‐Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management.pdf. Wiley.

Javid, Y. (2021). A bi-objective mathematical model to determine risk-based inspection programs. Process Safety and Environmental Protectionpro, 11(1), 893–904. https://doi.org/10.1016/j.psep.2020.12.022

Javid, Y. (2025). Efficient risk-based inspection framework: Balancing safety and budgetary constraints. Reliability Engineering and System Safety, 253(January 2024), 110519. https://doi.org/10.1016/j.ress.2024.110519

Kuo, R. J., Edbert, E., et al. (2023). Applying NSGA-II to vehicle routing problem with drones considering makespan and carbon emission. Expert Systems with Applications, 221(February), 119777. https://doi.org/10.1016/j.eswa.2023.119777

Moura, M. D. C., Lins, I. D., et al. (2015). A multi-objective genetic algorithm for determining efficient risk-based inspection programs. Reliability Engineering and System Safety, 133, 253–265. https://doi.org/10.1016/j.ress.2014.09.018

Niu, Y. Y., & Li, X. J. (2025). A dynamic utopia point updating strategy for multi-objective optimization. ISA Transactions, 162(April), 37–46. https://doi.org/10.1016/j.isatra.2025.04.030

Noegroho Hadi. (1984). Pengawasan Korosi Di Unit Pengolahan Minyak Mentah. Lembaran Publikasi Minyak Dan Gas Bumi, 18.

Omar, A., & Moselhi, O. (2026). Multi-objective optimization model for automated risk-based inspection planning for concrete bridges. Automation in Construction, 181(PA), 106608. https://doi.org/10.1016/j.autcon.2025.106608

Permana, M. S., Soeweify, M. P., et al. (2025). Innovation in Inspection Planning Using the Corrosion Assessment Information System (CAIS) Analytical Tool to Prevent Stationary Equipment Failure in Crude Distillation Unit (CDU). Scientific Contributions Oil and Gas, 48(3), 203–214. https://doi.org/10.29017/scog.v48i3.1848

Rachman, A., & Ratnayake, R. M. C. (2019). Machine learning approach for risk-based inspection screening assessment. Reliability Engineering & System Safety, 185, 518–532. https://doi.org/https://doi.org/10.1016/j.ress.2019.02.008

Seo, J. K., Cui, Y., et al. (2015). A risk-based inspection planning method for corroded subsea pipelines. Ocean Engineering, 109, 539–552. https://doi.org/10.1016/j.oceaneng.2015.07.066

Sözen, L., Yurdakul, M., et al. (2022). Risk-based inspection planning for internal surface defected oil pipelines exposed to fatigue. International Journal of Pressure Vessels and Piping, 200(August). https://doi.org/10.1016/j.ijpvp.2022.104804

Szparaga, A., Stachnik, M., et al. (2019). Multi-objective optimization based on the utopian point method applied to a case study of osmotic dehydration of plums and its storage. Journal of Food Engineering, 245(December 2017), 104–111. https://doi.org/10.1016/j.jfoodeng.2018.10.014

Tang, J., Wang, H., et al. (2023). Surrogate-assisted multi-objective optimization via knee-oriented Pareto front estimation. Swarm and Evolutionary Computation, 77(September 2022), 101252. https://doi.org/10.1016/j.swevo.2023.101252

Teurupun, A., Nugroho, B. S., et al. (2024). Risk-Based Inspection Analysis of Api 581 Pressure Safety Valve and Stripper Acid Gas Removal Unit at PT. XY. Scientific Contributions Oil and Gas, 47(3), 313–325. https://doi.org/10.29017/SCOG.47.3.1645

Wahono, T., Purniawan, A., et al. (2025). Risk-based asset integrity management in the oil and gas industry from traditional to machine learning approaches: A systematic review. Results in Engineering, 28(September), 107287. https://doi.org/10.1016/j.rineng.2025.107287

Weber, M. (2006). Some Safety Aspects on the Design of Sparger Systems for the. Process Safety Progress, 25(4), 326–330. https://doi.org/10.1002/prs

Zhang, K., Shen, C., et al. (2021). Knee based multimodal multi-objective evolutionary algorithm for decision making. Information Sciences, 544, 39–55. https://doi.org/10.1016/j.ins.2020.07.057