Abstract

This paper aims to propose a decision tool that helps estimate the risk probability in the petroleum sector in Iraq, which has many benefits for identifying the most important factor. This model was developed by using the relative importance index (RII) method incorporated into fuzzy Logic. An extensive literature review was conducted, and a questionnaire was distributed to the private and government sectors to scan all possible risks in the petroleum sector. As a result, forty–eight risks in the petroleum sector were identified and categorized into four groups: operational risks, financial and administrative risks, economic and political risks, and potential risks. Over 170 questionnaire forms have been distributed to engineers, managers, experts, and technicians, and 153 forms have been adopted for the analysis. The SPSS software was used to execute the statistical analysis concerning statical mean and relative important index. Also, the questionnaire results were analyzed using the relative importance index. The ranking of the groups and factors was demonstrated according to their level of effect on risk. Finally, a fuzzy assessment model was constructed and tested being appropriate to identify the probability of risk in the Iraqi petroleum sector to assess and estimate risk. Oil exploration company, one of the associations of the ministry of oil in Iraq, has been taken as a case study of this research to determine the risks and validate the model. This paper will seek the best solution to eliminate or minimized risks in the Iraqi patrolmen sector, especially at the Iraq oil exploration company as it has been taken as a case study. Three types of risk solutions were tested and optimized and those solutions are the avoid, the mitigate, and the transfer of the risks. The ANP mothed was used to determine the priority or the effectiveness of each type of solution to find the best alternative option for each risk factor. The result shows that the best way to deal with oil and gas risks in the Iraq petroleum sector is to avoid these risks. Because the solutions belonging to this group have an effective value of 65.4 %. The other two groups of mitigation and transfer have less effectiveness of 19.8 % and 14.8 % respectively. Moreover, the local and global priorities of the most important risk factor are also determined to validate and optimized the result. Finally, a list of alternative solutions recommended by the experts has been illustrated.

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