Monte Carlo simulation applied to 5-year recertification projects in surface equipment for drilling offshore oil wells

Authors

DOI:

https://doi.org/10.5585/gep.v14i1.23452

Keywords:

Oil and gas, Drilling, Project management, Risks, Schedule, Critical path, PERT, Simulation, Monte Carlo

Abstract

Several project management tools may be applied in oil and gas industry. The PERT tool, also known as estimate of three points, uses specialists’ info to calculate the duration of project schedule tasks, taking into consideration optimistic, most likely, and pessimistic scenarios, all of them related to project’s risks and uncertainty. Monte Carlo Simulation tool proposes a random numbers sampling process, applied throughout project critical path, to predict finishing probabilities within specific dates. This article aims to employ the Monte Carlo simulation as a tool for schedule management, based on risk analysis, applied to five-year recertification projects in surface equipment for drilling offshore oil wells. Therefore, using a combination of PERT and Monte Carlo Simulation tools, combine to other project management concepts, it was possible to perform a probability analysis and obtain a prediction of the project finish scenario, based on its risks and uncertainty analyzed. The results have shown the project original schedule had low chances of finishing within time stablished and so, the simulation performed contributes for the initial schedule revision and improvement.

References

Ansari, R., Khalilzadeh, M., & Hosseini, M. R. (2022). A multi-objective dynamic optimization approach to project schedule management: A case study of a gas field construction. KSCE Journal of Civil Engineering, 26(3), 1005-1013. https://doi.org/10.1007/s12205-021-0410-5

Brito, B. B. D. C. (2020). Utilização de métodos quantitativos na previsão de demandas de cargas marítimas para uma plataforma de petróleo na bacia de Campos. https://app.uff.br/riuff/handle/1/14394

Chu, Y., Li, G., & Zhang, H. (2020, November). Incorporation of ship motion prediction into active heave compensation for offshore crane operation. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1444-1449). IEEE. https://doi.org/10.1109/ICIEA48937.2020.9248283

de Amorim, F. R., de Abreu, P. H. C., Patino, M. T. O., & Terra, L. A. A. (2018). Análise dos riscos em projetos: uma aplicação do método de Monte Carlo em uma empresa do setor moveleiro. Future Studies Research Journal: Trends and Strategies, 10(2), 332-357. https://doi.org/10.24023/FutureJournal/2175-5825/2018.v10i2.314

Gallagher, B. J., Dupal, K., & Jones, R. E. (2021, August). 18 3/4 15000 Psi Shear Anything KBOS for Subsea Well Applications. In Offshore Technology Conference. OnePetro. https://doi.org/10.4043/31048-MS

Ghorbani, M. K., Hamidifar, H., Skoulikaris, C., & Nones, M. (2022). Concept-Based Integration of Project Management and Strategic Management of Rubber Dam Projects Using the SWOT–AHP Method. Sustainability, 14(5), 2541. https://doi.org/10.3390/su14052541

Hartono, B. (2018). From project risk to complexity analysis: a systematic classification. International Journal of Managing Projects in Business, 11(3), 734-760. https://doi.org/10.1108/IJMPB-09-2017-0108

Hirman, M., Benesova, A., Steiner, F., & Tupa, J. (2019). Project management during the industry 4.0 implementation with risk factor analysis. Procedia Manufacturing, 38, 1181-1188. https://doi.org/10.1016/j.promfg.2020.01.208

Islam, M. S., Mohandes, S. R., Mahdiyar, A., Fallahpour, A., & Olanipekun, A. O. (2022). A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects. Journal of Construction Engineering and Management, 148(8), 04022073. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002327

Koulinas, G. K., Xanthopoulos, A. S., Tsilipiras, T. T., & Koulouriotis, D. E. (2020). Schedule delay risk analysis in construction projects with a simulation-based expert system. Buildings, 10(8), 134. https://doi.org/10.3390/buildings10080134

Krisper, M., Dobaj, J., & Macher, G. (2020). Assessing Risk Estimations for Cyber-Security Using Expert Judgment. In Systems, Software and Services Process Improvement: 27th European Conference, EuroSPI 2020, Düsseldorf, Germany, September 9–11, 2020, Proceedings 27 (pp. 120-134). Springer International Publishing. https://doi.org/10.1007/978-3-030-56441-49

Kusumadarma, I. A., Pratami, D., Yasa, I. P., & Tripiawan, W. (2020). Developing project schedule in telecommunication projects using critical path method (CPM). International Journal of Integrated Engineering, 12(3), 60-67. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/4203

Lee, S., & Shvetsova, O. A. (2019). Optimization of the technology transfer process using Gantt charts and critical path analysis flow diagrams: Case study of the korean automobile industry. Processes, 7(12), 917. https://doi.org/10.3390/pr7120917

Lemmens, S. M., Lopes van Balen, V. A., Röselaers, Y. C., Scheepers, H. C., & Spaanderman, M. E. (2022). The risk matrix approach: a helpful tool weighing probability and impact when deciding on preventive and diagnostic interventions. BMC Health Services Research, 22(1), 218. https://doi.org/10.1186/s12913-022-07484-7

Ljiljanić, N., Rajić, Z., & Paunović, T. (2022). Use of PERT (program evaluation and review technique) and PDM (precedence diagramming method) in organizing modern vegetable seedling production. Економика пољопривреде, 69(1), 119-131. https://doi.org/10.5937/ekoPolj2201119L

Macias, A., Corilloclla, D., Porras, M., Venero, R., & Quispe, J. (2022). Monte Carlo simulation in an elementary school building. Journal of Project Management, 7(3), 147-154. https://doi.org/10.5267/j.jpm.2022.3.001

Mubin, S., Jahan, S., & Gavrishyk, E. (2019). Monte Carlo simulation and modeling of schedule, cost and risks of Dasu hydropower project. Mehran University Research Journal of Engineering & Technology, 38(3), 557-570. https://doi.org/10.22581/muet1982.1903.03

Ochieng, E. G., Ovbagbedia, O. O., Zuofa, T., Abdulai, R., Matipa, W., Ruan, X., & Oledinma, A. (2018). Utilising a systematic knowledge management based system to optimise project management operations in oil and gas organisations. Information Technology & People. https://doi.org/10.1108/ITP-08-2016-0198

Paiva, V. B. (2021). Análise do desempenho de regra de negociação via redes neurais artificiais em operações day trade. https://app.uff.br/riuff/handle/1/21686

Passos, J. D. S. D. (2018). Análise do gerenciamento de custos para implantação de um novo serviço em uma microempresa com a utilização do PMBOK. Gerência de Projetos de Tecnologia da Informação-Unisul Virtual. https://repositorio.animaeducacao.com.br/handle/ANIMA/3748

Pecina, E., Miloš Sprčić, D., & Dvorski Lacković, I. (2022). Qualitative Analysis of Enterprise Risk Management Systems in the Largest European Electric Power Companies. Energies, 15(15), 5328. https://doi.org/10.3390/en15155328

Pinkstone, H., McCluskey, T., Nilsen, J. H., Klepsvik, J., & Lambregts, A. (2018, August). Enhanced Drilling Capabilities With Innovative Drill Ship Design. In IADC/SPE Asia Pacific Drilling Technology Conference. OnePetro. https://doi.org/10.2118/180667-MS

Qazi, A., Shamayleh, A., El-Sayegh, S., & Formaneck, S. (2021). Prioritizing risks in sustainable construction projects using a risk matrix-based Monte Carlo Simulation approach. Sustainable Cities and Society, 65, 102576. https://doi.org/10.1016/j.scs.2020.102576

Samarino, G. T. V., & da Silva, E. D. O. (2018). HistoryRisk: Uma ferramenta para gerenciamento de riscos com base no PMBoK. Caderno de Estudos em Sistemas de Informação, 4(2).

Selvaraj, R., Vergil Contraes, G., Karuppusamy, K. K., Kamal, F. R., & Takieddine, O. H. (2020, November). Offshore Brownfield Slipover Platforms Installation Studies. In Abu Dhabi International Petroleum Exhibition & Conference. OnePetro. https://doi.org/10.2118/203241-MS

Soni, A., Kumar, C. R., & Shrivastava, A. (2022). Construction Projects Risk Assessment Based on Pert, Cpm and Project Management with Fuzzy Logic Technique. Advances and Applications in Mathematical Sciences, 5385-5395.

Sorenson, P. T., McCormick, S., & Dyck, M. (2019). Soil contamination sampling intensity: determining accuracy and confidence using a Monte Carlo simulation. Canadian Journal of Soil Science, 99(3), 254-261. https://doi.org/10.1139/cjss-2019-0001

Tembo-Silungwe, C. K., & Khatleli, N. (2017). Deciphering priority areas for improving project risk management through critical analysis of pertinent risks in the Zambian construction industry. Acta Structilia, 24(2), 1-43. https://doi.org/10.18820/24150487/as24i2.1

Teo, P., Gajanayake, A., Jayasuriya, S., Izaddoost, A., Perera, T., Naderpajouh, N., & Wong, P. S. (2022). Application of a bottom-up approach to estimate economic impacts of building maintenance projects: cladding rectification program in Australia. Engineering, Construction and Architectural Management, 29(1), 333-353. https://doi.org/10.1108/ECAM-10-2020-0802

Tokdemir, O. B., Erol, H., & Dikmen, I. (2019). Delay risk assessment of repetitive construction projects using line-of-balance scheduling and Monte Carlo simulation. Journal of Construction Engineering and Management, 145(2), 04018132. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001595

Vergara, W. R. H., Teixeira, R. T., & Yamanari, J. S. (2017). Análise de risco em projetos de engenharia: uso do PERT/CPM com simulação. Exacta, 15(1), 75-88. https://doi.org/10.5585/ExactaEP.v15n1.6779

Wada, R., Kaneko, T., Ozaki, M., Inoue, T., & Senga, H. (2018). Longitudinal natural vibration of ultra-long drill string during offshore drilling. Ocean Engineering, 156, 1-13. https://doi.org/10.1016/j.oceaneng.2018.02.054

Wulandari, A., & Dachyar, M. (2018). Scheduling of Empennage Structure Design Project of Indonesia’s Aircraft with Critical Path Method (CPM). In MATEC Web of Conferences (Vol. 248, p. 03012). EDP Sciences. https://doi.org/10.1051/matecconf/201824803012

Yu, X., & Zuo, H. (2022). Intelligent Construction Optimization Control of Construction Project Schedule Based on the Fuzzy Logic Neural Network Algorithm. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8111504

Zhang, S., & Jin, L. (2020, June). Research on software project schedule management method based on Monte Carlo simulation. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 1605-1608). IEEE. https://doi.org/10.1109/ITOEC49072.2020.9141570

Published

2023-03-31

How to Cite

Freitas Rodrigues, D., & Barbosa Sobral, A. P. (2023). Monte Carlo simulation applied to 5-year recertification projects in surface equipment for drilling offshore oil wells. Revista De Gestão E Projetos, 14(1), 96–132. https://doi.org/10.5585/gep.v14i1.23452