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

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

^*Corresponding author for this work

Petroleum and Chemical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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.

Original language	English
Title of host publication	Computer Science Advances
Subtitle of host publication	Research and Applications
Publisher	Nova Science Publishers, Inc.
Pages	129-145
Number of pages	17
ISBN (Electronic)	9781536148459
ISBN (Print)	9781536148442
Publication status	Published - Jan 1 2019

Keywords

Artificial neural network
Minimum miscibility pressure
Particle swarm optimization
Pure CO injection
Rising bubble apparatus

ASJC Scopus subject areas

Computer Science(all)

Cite this

DEVELOPMENT OF ACCURATE MODELS FOR ESTIMATION OF PURE CO₂-OIL MINIMUM MISCIBILITY PRESSURE BASED ON ARTIFICIAL INTELLIGENCE METHODS. / Al-Hinai, Khalid; Vakili-Nezhaad, G. Reza; Al-Bemani, Ali S. et al.
Computer Science Advances: Research and Applications. Nova Science Publishers, Inc., 2019. p. 129-145.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AU - Al-Bemani, Ali S.

AU - Mamghaderi, Azadeh

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AB - 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.

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KW - Particle swarm optimization

KW - Pure CO injection

KW - Rising bubble apparatus

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

Abstract

Keywords

ASJC Scopus subject areas

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