DEVELOPMENT OF ACCURATE MODELS FOR ESTIMATION OF PURE CO2-OIL MINIMUM MISCIBILITY PRESSURE BASED ON ARTIFICIAL INTELLIGENCE METHODS

Khalid Al-Hinai, G. Reza Vakili-Nezhaad*, Ali S. Al-Bemani, Azadeh Mamghaderi

*المؤلف المقابل لهذا العمل

نتاج البحث

ملخص

Design of CO2 miscible flooding for enhanced oil recovery depends on CO2-oil Minimum Miscibility Pressure (MMP) which is determined either experimentally or by analytical methods. Some efforts are recently conducted to calculate MMP based on Artificial Intelligence (AI) methods. In this chapter, an Artificial Neural Network (ANN) is developed to predict the CO2-oil MMP in the case of pure CO2 injection, and a previous Genetic Algorithm (GA) method is improved by using Particle Swarm Optimization (PSO) method. Analysis of the results shows that the ANNbased model yields a better match with the experimental data.

اللغة الأصليةEnglish
عنوان منشور المضيفComputer Science Advances
العنوان الفرعي لمنشور المضيفResearch and Applications
ناشرNova Science Publishers, Inc.
الصفحات129-145
عدد الصفحات17
رقم المعيار الدولي للكتب (الإلكتروني)9781536148459
رقم المعيار الدولي للكتب (المطبوع)9781536148442
حالة النشرPublished - يناير 1 2019

ASJC Scopus subject areas

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بصمة

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