Revolutionizing Risk-Based Inspection Techniques to Minimize Costs and Maximize Safety in Offshore Operations

References

[1] Ainin, S., Naqshbandi, M. M., & Dezdar, S. (2016). Impact of adoption of Green IT practices on organizational performance. Quality & Quantity, 50, 1929-1948.

[2] Al-Sabaeei, A. M., Alhussian, H., Abdulkadir, S. J., & Jagadeesh, A. (2023). Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review. Energy Reports, 10, 1313-1338.

[3] Amaechi, C. V., Reda, A., Kgosiemang, I. M., Ja’e, I. A., Oyetunji, A. K., Olukolajo, M. A., & Igwe, I. B. (2022). Guidelines on asset management of offshore facilities for monitoring, sustainable maintenance, and safety practices. Sensors, 22(19), 7270.

[4] Asadzadeh, A., Arashpour, M., Li, H., Ngo, T., Bab-Hadiashar, A., & Rashidi, A. (2020). Sensor-based safety management. Automation in Construction, 113, 103128.

[5] Bento, N., & Fontes, M. (2019). Emergence of floating offshore wind energy: Technology and industry. Renewable and sustainable energy reviews, 99, 66-82.

[6] Besada, J. A., Bergesio, L., Campaña, I., Vaquero-Melchor, D., López-Araquistain, J., Bernardos, A. M., & Casar, J. R. (2018). Drone mission definition and implementation for automated infrastructure inspection using airborne sensors. Sensors, 18(4), 1170.

[7] Colombo, E., & Mattarolo, L. (2017). Energy and development: the role of academia in education, research, and technological cooperation for sustainability. Wiley Interdisciplinary Reviews: Energy and Environment, 6(1), e215.

[8] Council, G. W. E. (2020). Global offshore wind report 2020. GWEC: Brussels, Belgium, 19, 10-12.

[9] Cruz, M. G., Peters, G. W., & Shevchenko, P. V. (2015). Fundamental aspects of operational risk and insurance analytics: A handbook of operational risk: John Wiley & Sons.

[10] Denney, E., Pai, G., & Whiteside, I. (2019). The role of safety architectures in aviation safety cases. Reliability Engineering & System Safety, 191, 106502.

[11] Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50, 92-111.

[12] El Himer, M. (2019). Innovation in condition monitoring and predictive maintenance solutions in industrial contexts. University of Stavanger, Norway,

[13] Emimi, M., Khaleel, M., & Alkrash, A. (2023). The current opportunities and challenges in drone technology. Int. J. Electr. Eng. and Sustain., 74-89.

[14] Garcia, H. E., Aumeier, S. E., & Al-Rashdan, A. Y. (2020). Integrated state awareness through secure embedded intelligence in nuclear systems: Opportunities and implications. Nuclear Science and Engineering, 194(4), 249-269.

[15] Hernández-Chover, V., Castellet-Viciano, L., & Hernández-Sancho, F. (2020). Preventive maintenance versus cost of repairs in asset management: An efficiency analysis in wastewater treatment plants. Process Safety and Environmental Protection, 141, 215-221.

[16] Hussain, M., Zhang, T., & Seema, M. (2023). Adoption of big data analytics for energy pipeline condition assessment. International Journal of Pressure Vessels and Piping, 105061.

[17] Ikram, M., Ferasso, M., Sroufe, R., & Zhang, Q. (2021). Assessing green technology indicators for cleaner production and sustainable investments in a developing country context. Journal of cleaner production, 322, 129090.

[18] Javaid, M., Haleem, A., & Suman, R. (2023). Digital twin applications toward industry 4.0: A review. Cognitive Robotics, 3, 71-92.

[19] Kaiser, M. J. (2019). The role of factor and activity-based models in offshore operating cost estimation. Journal of Petroleum Science and Engineering, 174, 1062-1092.

[20] Khan, S., Farnsworth, M., McWilliam, R., & Erkoyuncu, J. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50, 13-28.

[21] Kyriakides, S., & Corona, E. (2023). Mechanics of Offshore Pipelines: Volume I: Buckling and collapse: Gulf Professional Publishing.

[22] Lizarelli, F. L., & Toledo, J. C. d. (2016). Practices for continuous improvement of the Product Development Process: a comparative analysis of multiple cases. Gestão & Produção, 23, 535-555.

[23] Ljungberg, S. (2023). Infrared techniques in buildings and structures: Operation and maintenance. In Infrared methodology and technology (pp. 211-252): CRC Press.

[24] Maher, S. T., Long, G. D., Cromartie, R. S., Sutton, I. S., & Steinhilber, M. R. (2016). Paradigm shift in the regulatory application of safety management systems to offshore facilities. Process Safety Progress, 35(4), 317-329.

[25] Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906-1955.

[26] Nooralishahi, P., Ibarra-Castanedo, C., Deane, S., López, F., Pant, S., Genest, M., . . . Maldague, X. P. (2021). Drone-based non-destructive inspection of industrial sites: A review and case studies. Drones, 5(4), 106.

[27] Paul, D. J., & Nassis, G. P. (2015). Testing strength and power in soccer players: the application of conventional and traditional methods of assessment. The Journal of Strength & Conditioning Research, 29(6), 1748-1758.

[28] Raveendran, A., Renjith, V., & Madhu, G. (2022). A comprehensive review on dynamic risk analysis methodologies. Journal of Loss Prevention in the Process Industries, 76, 104734.

[29] Ren, Z., Verma, A. S., Li, Y., Teuwen, J. J., & Jiang, Z. (2021). Offshore wind turbine operations and maintenance: A state-of-the-art review. Renewable and sustainable energy reviews, 144, 110886.

[30] Shukla, A., & Karki, H. (2016). Application of robotics in offshore oil and gas industry—A review Part II. Robotics and Autonomous Systems, 75, 508-524.

[31] Singh, M., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, Y., Murray, N., & Devine, D. (2022). Applications of digital twin across industries: A review. Applied Sciences, 12(11), 5727.

[32] Wanasinghe, T. R., Gosine, R. G., De Silva, O., Mann, G. K., James, L. A., & Warrian, P. (2020). Unmanned aerial systems for the oil and gas industry: Overview, applications, and challenges. IEEE access, 8, 166980-166997.

[33] Wong, B., & McCann, J. A. (2021). Failure detection methods for pipeline networks: From acoustic sensing to cyber-physical systems. Sensors, 21(15), 4959.

[34] Yadigarova, S. A. (2023). Condition Monitoring and Data Collection System. Khazar University (Azerbaijan),

[35] Zio, E. (2018). The future of risk assessment. Reliability Engineering & System Safety, 177, 176-190.

@article{1704852,
      author = {Olusola Temidayo Oladipo, Ikiomoworio Nicholas Dienagha, Wags Numoipiri Digitemie},
      title = {Revolutionizing Risk-Based Inspection Techniques to Minimize Costs and Maximize Safety in Offshore Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {679-686},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704852.pdf},
      abstract = {The offshore industry faces significant challenges in balancing operational safety with cost-efficiency. While effective, traditional inspection methods are often reactive, resource-intensive, and limited in scope. This paper explores the evolution of risk-based inspection (RBI) techniques, emphasizing the role of emerging technologies such as digital twins, predictive analytics, and real-time monitoring systems in transforming inspection practices. These innovative methods' economic and safety benefits are analyzed, highlighting reduced downtime, cost savings, and enhanced worker protection. Despite barriers such as high initial investment, workforce training demands, and regulatory complexities, solutions such as strategic partnerships, modular deployment, and adaptive regulations offer practical pathways for implementation. Recommendations include prioritizing digital tools, fostering collaboration between industry stakeholders, and investing in workforce development to align skills with advanced inspection frameworks. This study underscores the critical need for next-generation strategies to ensure sustainable and efficient offshore operations.},
      keywords = {Offshore operations, Risk-based inspection, Digital twins, Predictive analytics, Real-time monitoring, Safety optimization},
      month = {July},
  }