كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / نهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

Introduction of A Risk and Change Management Method by Design a Modified Equation to Calculate the Static Bottom-hole Pressure of Iranian Gas Wells Based on Hierarchy Risk Management and Surface data To Replace and Eliminate Operational Risk (Third Development)

نویسندگان :

Fatemeh Karimi Organi¹ Hamid Babakpour² Ali Pourbadir³ Bahador Kheirollahpour⁴ Moona Batooie⁵ Hamidreza Pourghorban⁶

1- دانشگاه آزاد اسلامی واحد اهواز 2- دانشگاه آزاد اسلامی واحد اهواز 3- دانشگاه آزاد اسلامی واحد اهواز 4- دانشگاه آزاد اسلامی واحد اهواز 5- Petroleum Engineering and Development Company 6- Farayand Sazan Energy Consultant Engineering Company

کلمات کلیدی :

high H2S gas wells،hierarchy method،modified equation،risk management،SARS-COV 2 pandemic،static bottom-hole pressure

چکیده :

In high H2S gas well doing industrial operations can cause serious environmental, financial & health consequences, risk management plays an important role especially these days that world in war with SARS-COV 2 pandemic we should have stronger boundaries to protect lives. One of the common methods is hierarchy method. In this study by using a combination of this method and design a new correlation to calculate static bottom hole pressure at gas wells we tried to have a strong risk management with a final goal to replace 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 predictions of static bottomhole pressure. For this purpose, a unique adjustable parameter, based on history matching of wells, has been proposed for each reservoir. Accuracy of this equation was investigated in three Iranian gas reservoir information. Good agreement was obtained among 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.