The design of a novel expert method for the best artificial lift system selection based on SAW model

  • سال انتشار: 1388
  • محل انتشار: سومین کنفرانس مهندسی معدن ایران
  • کد COI اختصاصی: IMEC03_269
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 360
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نویسندگان

Mehrdad Alemi

M.Sc. Student in Petroleum Engineering Department of Shahid Bahonar University ofKerman, Iran and in collaboration with the Environmental Engineering & Energy Research Center ofShahid Bahonar University of Kerman, Iran, as well as the Petroleum Engineerin

Hossein Jalalifar

Ph.D. Assit.Professor in Petroleum Engineering Department of Shahid Bahonar Universityof Kerman, Iran and in collaboration with the Environmental Engineering & Energy Research Center ofShahid Bahonar University of Kerman, Iran as well as the Petroleum Eng

Gholamreza Kamali

Ph.D. Assit.Professor in Mining Engineering Department of Shahid Bahonar Universityof Kerman, Iran,

Ehsan Khamehchi

Ph.D. Student in collaboration with the Petroleum Engineering and DevelopmentCompany, Tehran, Iran

چکیده

Artificial Lift is defined as any system adding energy to the fluid column in a wellbore with the objective of initiating and enhancing production from the well. Artificial Lift is needed when reservoir drives do not sustain acceptable rates or cause fluids to flow at all in some cases. Artificial Lift Systems use a range of operating principles, including Pumping and Gas lifting.Simple Additive Weighting (SAW) model (method) is one of the most prevalent Multi Criteria Decision Making (MCDM) methods. MCDM is an approach employed to solve problems involving selection from among a finite number of criteria.In this paper, a novel expert computer method (by means of Visual Basic.net Code) based on SAW model has been presented for Artificial Lift Selection in oil industry validated with several certain oil fields (such as the Iranian Kuh-E-Mond (MD-۶) oil field data, Progressive Cavity Pump (PCP) as the best Artificial Lift System).

کلیدواژه ها

Artificial lift, Simple Additive Weighting, Multi Criteria Decision Making-SAW

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