مشخصات مقاله | |
انتشار | مقاله سال 2018 |
تعداد صفحات مقاله انگلیسی | 12 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع نگارش مقاله | مقاله پژوهشی (Research article) |
مقاله بیس | این مقاله بیس میباشد |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Safety assessment in oil drilling work system based on empirical study and Analytic Network Process |
ترجمه عنوان مقاله | ارزیابی ایمنی در سیستم کار حفاری نفت بر اساس مطالعه تجربی و فرایند شبکه تحلیلی |
فرمت مقاله انگلیسی | |
رشته های مرتبط | مهندسی صنایع |
گرایش های مرتبط | ایمنی صنعتی، برنامه ریزی و تحلیل سیستم ها، مهندسی ایمنی |
مجله | علوم ایمنی – Safety Science |
دانشگاه | Department of Science & Technology Management – China |
کلمات کلیدی | حفاری نفتی، ارزیابی ایمنی، مطالعه تجربی، روند تحلیلی شبکه، عوامل انسانی |
کلمات کلیدی انگلیسی | Oil drilling, Safety assessment, Empirical study, Analytic network process, Human factors |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.ssci.2018.02.004 |
کد محصول | E8469 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
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1. Introduction
In recent years, safety problems in oil drilling have obtained many concerns. As the drilling industry involves complex and hazardous activities, it is of great importance to assess attendant risks in which human factors make up a large proportion. The hoisting and lifting systems are one of the most important components in oil drilling industry; measures should be taken to lower the risk of human factors (Zhou et al., 2017). Many safety studies have been done in drilling industry. AmirHeidari et al. (2015) carried out a case study to assess the human factors, which are identified by what-if and structured brainstorming. Zhao et al. (2011) assessed the qualification of human factor risks associated with the drilling process based on Delphi method. Strand and Lundteigen (2016) studied classification of the human factors and put forward a relative importance of assessment criteria in each risk influencing factor. Abimbola et al. (2015) analyzed the shortcomings existing in overbalanced and underbalanced drilling technique, and proposed a Bayesian network model for managed pressure drilling risk assessment. Ataallahi and Shadizadeh (2015) studied the blowout in onshore Iranian drilling industry, and provided fuzzy method to develop the consequence of blowout for Iranian onshore drilling industry. Ramzali et al. (2015) carried out a survey on a leakage event in production phase, and assessed the barriers of the initiating event by using Event Tree Analysis. Pranesh et al. (2017) analyzed the case study of deep water horizon offshore oil platform accident, in which failures in oil and gas cementing operation exists, and concluded that this tragedy is due to complete human errors and employee’s poor leadership abilities. Researchers studied human factors from different views of classification, while the hierarchical and interactional study of human factors in drilling industry is still incomplete; moreover, there are rare studies in the safety assessment considering the interdependences between human factors in the hoisting and lifting system in oil drilling industry. It has been acknowledged that accident analysis must rely on systemic and organizational models (Rasmussen, 1997; Reason, 1997). And it is essential to choose a model before starting the investigations, according to the characteristics of the system and the nature of the accident (Chauvin et al., 2013). Human Factors Analysis and Classification System (HFACS) is a generic human error framework originally developed for US military aviation as a tool for the analysis of the human factors aspects of accidents. The HFACS is perhaps the most widely used human factors accident analysis framework, including shipping accidents (Akyuz, 2017), mining (Patterson and Shappell, 2010), and construction (Garrett and Teizer, 2009). Wiegmann and Shappell (2001) suggested that the HFACS framework bridges the gap between theory and practice by providing safety professionals with a theoretically based tool for identifying and classifying human errors. In HFACS, factors in higher level affect factors in lower levels. |