Introduction of Risk Management Method Based on Hierarchy Risk Management and Surface Data to Eliminate Operational Risk (Second Development)
Publish place: International Journal of Reliability, Risk and Safety: Theory and Application، Vol: 6، Issue: 2
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
View: 126
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJRRS-6-2_002
تاریخ نمایه سازی: 5 آذر 1402
Abstract:
Industrial operations in high H۲S gas wells can cause serious environmental, financial & health consequences. Risk management is important, especially when the world is at war with the SARS-COV-۲ pandemic; we should have stronger boundaries to protect lives. One of the common methods is the hierarchy method. In this study, by combining this method and designing a new correlation to calculate static bottom hole pressure at gas wells, we tried to have strong risk management with the final goal of replacing the industrial operation. In the past, time-consuming and imprecise trial and error methods& expensive operations were used to calculate static bottom-hole pressure for gas wells. So, a general equation was modified based on field observations to obtain more accurate static bottom-hole pressure predictions. For this purpose, a unique adjustable parameter, based on the history matching of wells, has been proposed for each reservoir. The accuracy of this equation was investigated in three Iranian gas reservoir information. Good agreement was obtained between the field observations and this proposed equation. The precision of this method depends on field data, and with increasing numbers of field tests, the model becomes more accurate.
Keywords:
Authors
Hamid Babakpour
Department of Environment, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
FAatemeh Karimi Organi
Department of Environment, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :