EOR screening using artificial intelligence Bayesian network

سال انتشار: 1389
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 2,139

فایل این مقاله در 9 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

IOGPC17_023

تاریخ نمایه سازی: 3 آبان 1389

چکیده مقاله:

oil - production form enhanced oil recovery (EOR) projects continues supply an increasing in percentage of the world s oil. Therefore , the importance of choosing the best recovery method becomes increasingly important to petroleum engineers . In recent years computer technology has improved the application of screening criteria through the use of artificial intelligence techniques but the value of these programs depends on the accuracy of the input data used. bayesian network analysis is a powerful tool, which is already applied in some oil industry field. in this work we present screening criteria using bayesian network based on a combination of the reservoir and oil characteristics of successful projects plus generated data by taber table. we provide screening criteria for the six methods that are either the most important or still have some promise . the purpose is to develop software capable of combining the data extracted from different sources either experimental or modeling into a unified expert system in order to select the most proper technique for the situation. the efficiency is also checked with another set of data the accuracy of which in inside the acceptability margins.

کلیدواژه ها:

bayesian ، enhanced oil recovery (EOR) ، screening

نویسندگان

d barzegari

school of chemical and petroleum engineering shiraz

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله 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 مقاله لینک شده اند :
  • Green, D.W., Willhite, G.P., Enhanced Oil Recovery, Society of Petroleum ...
  • Ridha B.C. Gharbi An expert system for selecting and designing ...
  • A.T.F.S. Gaspar Ravagnani , E.L. Ligero b, S.B. Suslick CO2 ...
  • J.J. Taber, F.D. Martin, R.S. Serigh Introduction to screening criteria ...
  • Kevin B. Korb Ann E. Nicholson, Bayesian Artificial Intelligence. 2004 ...
  • Uffe B. Kjerulff Anders L. Madsen Probabilistic Networks - An ...
  • P. Truccoa, _, E. Cagnoa, F. Ruggerib, O. Grandea A ...
  • Moritis, Guntis, EOR dips in U.S. but remains a significant ...
  • Anonymous 1996 worldwide EOR survey Oil & Gas Journal 1996; ...
  • Leena Koottungal, 2008 worldwide EOR survey Oil & Gas Journal ...
  • Anonymous, 1998 worldwide EOR survey Oil & Gas Journal 1998; ...
  • Anonymous, 2004 worldwide EOR survey Oil & Gas Journal 2004; ...
  • Anonymous, Oil & Gas Journal. 2002, 100, 71 SPE 94637. ...
  • P.L Bondor, BonTech; J.R. Hite, Business Fundamentas Group. ...
  • David B. Burnett and Michael W. Dann, screening tests for ...
  • Saad F. Alkafeef, Alforgi M. Zaid, Review of and Outlook ...
  • I. Farzad, M. Amani, Evaluating Reservoir Production Strategies in Miscible ...
  • E.J. Manrique, V.E. Muci, M.E. Gurfinkel, EOR Field Experiences in ...
  • G.O. Goodlett, M.M. Honarpour, F .T.Chung, P.S.Sarathi, Petroleum &Energy Research ...
  • R.M. Ray, T.C. Wesson, Enhanced Oil Recovery Data Base Analysis ...
  • J, J. Taber, F.D. Martin, Technical Screening Guides for the ...
  • K.Dehghani, R. Ehrlich, Chevron Utilization of Associated Produced Gas to ...
  • R.R. Ibatullin., N.G. Ibragimov., R.S. Khisamov, E.D. Podymov, A.A. Shutov ...
  • V. Alvarado, A. Ranson, K. Hermandez, E.Manrique, J. Matheus, L. ...
  • M.A.Al-B ahar, R. Merrill, W.Peake Mo Jumaa , R.Oskui Evaluation ...
  • L.Surguchev, Lun Li, IOR Evaluation and Applicability Screening Using Artificial ...
  • نمایش کامل مراجع