Call for Paper 25 September, 2023. Please submit your manuscript via online system or email at

ISSN E 2409-2770
ISSN P 2521-2419

Assessing Safety Prognostic Technology for Complex Petroleum Engineering Projects

Kashif Abbas, Wiqas Alam

Vol. 8, Issue 08, PP. 226-231, August 2021


Keywords: Complex Petroleum Engineering Projects, Safety Prognostic Technology, Diagnostic Models

Download PDF

Technological advancements have a broad scope in terms of experimental, analytical, field cases and numerical studies in complex petroleum engineering projects. These can be pertinent to transportation system and gathering and safety in oil and gas production. The current research was aimed at examining the challenges pertinent to safety prognostic technology as well as various ways in which it can be implemented for resolving issues in complex petroleum engineering projects. For the conduct of this research, qualitative methodology was used and primary data was assessed to present critical evaluation of the stated aim. The interviews were conducted from 10 petroleum engineers working in different public and private companies in Pakistan. The snowball technique followed by thematic analysis data analysis technique was applied for the generation of primary findings. The results of the research examined that safety prognostic technologies are significant in terms of enhancing safety, reliability and reducing the possible errors in maintenance. It has further examined that in complex engineering systems, there are multiple propagation paths to different consequences some of which might differ with respect to the most single faults.

  1. Kashif Abbas, , SUIT Peshawar, Pakistan.
  2. Wiqas Alam,, SUIT Peshawar, Pakistan.

Kashif Abbas Wiqas Alam “Assessing Safety Prognostic Technology for Complex Petroleum Engineering Projects” International Journal of Engineering Works Vol. 8 Issue 08 PP. 226-231 August 2021

[1]     Basias, , N., & Pollalis, Y. (2018). Quantitative and qualitative research in business & technology: Justifying a suitable research methodology. , pp. Review of Integrative Business and Economics Research 7., 91-105.

[2]     Beer, A., & Faulkner , D. (2014). How to use primary and secondary data. . In Handbook of Research Methods and Applications in Spatially Integrated Social Science. Edward Elgar Publishing.

[3]     Bigliani, R. (2013). Reducing risk in oil and gas operations. IDC Energy Insights., 1-15.

[4]     Bousdekis, , A., Magoutas , B., Apostolou, D., & Men. (2018). Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. . Journal of Intelligent Manufacturing, 29(6), 1303-1316.

[5]     Epelle, E., & Gerogiorgis, D. (2020). A review of technological advances and open challenges for oil and gas drilling systems engineering. AIChE Journal, 66(4), 16842.

[6]     Erlingsson, C., & Brysiewicz, P. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine, 7(3), 93-99.

[7]     Ershaghi, I., & Paul, D. (2017). October. The changing shape of petroleum engineering education. In SPE Annual Technical Conference and Exhibition. OnePetro.

[8]     Grubic, T., Redding, L., Baines, T., & Julien, D. (2011). The adoption and use of diagnostic and prognostic technology within UK-based manufacturers. . Proceedings of the institution of mechanical engineers, part b: journal of engineering manufacture, 225(8.

[9]     Hameed, H. (2020). Quantitative and qualitative research methods: Considerations and issues in qualitative research. . The Maldives National Journal of Research, 8(1)., 8-17.

[10]  Hasheminasab, H., Gholipour, Y., Kharrazi, M., & Streimikiene, D. (2018). A novel Metric of Sustainability for petroleum refinery projects. Journal of Cleaner Production, 171,, 1215-1224.

[11]  Horowitz, G., Faundez, E., Maestri, M., & Cassanello, M. (2014). Fault diagnosis in oil wells. In SPE annual technical conference and exhibition. Society of Petroleum Engineers.

[12]  Hu, J., Zhang, L., Ma, L., & Liang, W. (2010). An integrated method for safety pre-warning of complex system. . Safety science, 48(5),, 580-597.

[13]  Knegtering , B., & Pasman, H. (2009). Safety of the process industries in the 21st century: A changing need of process safety management for a changing industry. Journal of Loss Prevention in the Process Industries, 22(2), 162-168.

[14]  Kordestani, M., Saif, M., Orchard, M., Razavi-Far, R., & Khorasni, k. (2019). Failure prognosis and applications—A survey of recent literature. IEEE transactions on reliability.

[15]  Moir, K., Niculita, O., & Milligan, W. (2018). Prognostics and health management in the oil & gas industry–a step change. In Proc. PHM Soc. Eur. Conf (Vol. 4, No. 1., 1-18.

[16]  Møyner, O., Krogstad , S., & Lie, K. (2015). The application of flow diagnostics for reservoir management. . SPE Journal, 20(02), 306-323.

[17]  Müller, R., & Oehm,, L. (2019). Process industries versus discrete processing: How system characteristics affect operator tasks. Cognition, Technology & Work, 21(2), 337-356.

[18]  Neuendorf, K., & Kumar, A. (2015). Content analysis. The international encyclopedia of political communication, 1-10.

[19]  Patel, H., Prajapati, D., Mahida, D., & Shah, M. (2020). Transforming petroleum downstream sector through big data: a holistic review. Journal of Petroleum Exploration and Production Technology, 10(6),, 2601-2611.

[20]  Pham, B., Agarwal, V., Lybeck, N., & Tawfik, . (2012). Prognostic Health Monitoring System: Component Selection Based on Risk Criteria and Economic Benefit Assessment (No. INL/CON-11-23571). Idaho National Laboratory (INL).

[21]  Rolls-Royce. (2009, September 23). TotalCare is described in the RollsRoyce website:. Retrieved from Rolls-Royce plc: totalcare

[22]  Sanni , M. (2018). Petroleum Engineering: Principles, Calculations, and Workflows . John Wiley & Sons (Vol. 237).

[23]  Skaf, Z. (2015). Prognostics: Design, implementation, and challenges.

[24]  Stecki, J., Cross, J., Stecki , C., & Lucas, A. (2012). Autonomous Prognostics and Health Management (APHM). European Conference of Prognostics and Health Management Society.

[25]  Sun, B., Zeng, S., Kang, R., & Pecht, M. (2012). Benefits and challenges of system prognostics. IEEE Transactions on reliability, 61(2), 323-335.

[26]  Tewari, R., Dandekar, A., & Ortiz, J. (2018). Petroleum Fluid Phase Behavior: Characterization, Processes, and Applications. CRC Press.

[27]  Tiddens, W., Braaksma, A., & Tinga, T. (2015). The adoption of prognostic technologies in maintenance decision making: a multiple case study. Procedia CIRP, 38,, 171-176.

[28]  Vogl, G., Weiss, B., & Helu, M. (2019). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30(1),, 79-95.

[29]  von Plate, & M. (2016). Big data analytics for prognostic foresight. In SPE Intelligent Energy International Conference and Exhibition. Society of Petroleum Engineers.

[30]  Xu , G., Liu, M., Wang, J., Ma, Y., Wang,, J., & Li, F. (2019). Data-driven fault diagnostics and prognostics for predictive maintenance: A brief overview. In IEEE 15th International Conference on Automation Science and Engineering (C.

[31]  Yagiz, S., Gokceoglu, C., Sezer, E., & Iplikci, S. (2009). Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. ). Engineering Applications of Artificial Intelligence, 22(4-5, 808-814.

[32]  Zhang, L., & Hu, J. (2013). Safety prognostic technology in complex petroleum engineering systems: progress, challenges and emerging trends. Petroleum Science, 10(4), 486-493.

[33]  Zhong, K., Han, M., & Han, , B. (2019). Data-driven based fault prognosis for industrial systems: a concise overview. IEEE/CAA Journal of automatica sinica, 7(2),, 330-345