چکیده مقاله
Determining the fundamental properties of oil reservoir rocks and the parameters governing fluid flowthrough porous media is a complex aspect of reservoir characterization Among the most critical reservoirrock properties are relative permeability of gas oil and water oil systems, which serve as essential inputs forreservoir simulation models Traditionally, laboratory core analysis is considered the gold standard forobtaining accurate information about reservoir rocks However, the high costs and time consuming nature ofcore analysis, coupled with the limited availability of core samples, especially in the early stages of fielddevelopment, necessitate the exploration of alternative techniques for predicting these essential properties This project aims to develop a methodology to reduce uncertainty in relative permeability and capillarypressure, utilizing neural networks to predict the corresponding parameter curves
کلیدواژهها
نویسندگان
Mojtaba Azizi
Faculty of Chemistry and Chemical Engineering, Malek Ashtar University of Technology,P O Box ۱۶۷۶۵ ۳۴۵۴, Tehran, Iran
Ahmadreza Borjikhani
Faculty of Chemical Engineering, University of Tehran, Tehran, Iran
Seyed Karam Saedi
Faculty of Technology management and industrial management, Islamic Azad University Science andResearch Branch, Tehran, Iran
Abbas Abdolmaleki
Faculty of Chemistry and Chemical Engineering, Malek Ashtar University of Technology,P O Box ۱۶۷۶۵ ۳۴۵۴, Tehran, Iran
شیوه ارجاع
Azizi, Mojtaba and Borjikhani, Ahmadreza and Saedi, Seyed Karam and Abdolmaleki, Abbas,1403,Using neural network to predict relative permeability andcapillary pressure diagrams,Third International Congress for Chemical Engineering and Petroleum Industry students,Tehran
ارائهشده در
مجموعه مقالات سومین کنگره بین المللی دانشجویان مهندسی شیمی و صنعت نفت29 آبان 1403 · تهران