A new risk assessment framework for safety in oil and gas industry: Application of FMEA and BWM based picture fuzzy MABAC

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, vol.219, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 219
  • Publication Date: 2022
  • Doi Number: 10.1016/j.petrol.2022.111059
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Chemical Abstracts Core, INSPEC, Civil Engineering Abstracts
  • Keywords: Risk assessment, Oil and gas industry, Picture fuzzy, Uncertainty, FMEA, BWM, MABAC, DECISION-MAKING, AGGREGATION OPERATORS, PETROLEUM, MODEL, ACCIDENTS
  • Yıldız Technical University Affiliated: Yes


As one of the riskiest sector, oil and gas production, transportation and storage operations create dangers for workers. Since health hazards and dangerous conditions can result in fatalities or damages, safety precautions are vital in oil and gas industry. As accident types, fire and explosions are among the most frequent primary co-incidences and vary from different causes. The aim of this study is to identify and prioritize fire and explosion risks in the midstream section of the oil and gas industry. For this aim, the common causes of fire and explosions were evaluated to take preventive measures or mitigation strategies. To prioritize risk factors cause fire and explosions in the midstream section of the oil and gas industry, a new risk assessment framework that consists of Best-Worst Method (BWM) and Picture Fuzzy multi-attributive border approximation area comparison (PF-MABAC) method based on Failure Mode and Effect Analysis (FMEA) was proposed. Accordingly, while the weights of risk parameters in FMEA were determined using BWM, to follow proper safety precautions risk factors were evaluated using PF-MABAC method. To demonstrate the effectiveness and practicality of the proposed risk assessment approach, case studies were conducted. At the end, sensitivity analysis was performed to validate and prove robustness of the proposed risk assessment framework via results of a real case.