Minimum miscibility pressure prediction based on a hybrid neural genetic algorithm

• Published in: Chemical engineering research & design Vol. 86; no. 2; pp. 173 - 185
• Main Authors: Dehghani, S.A. Mousavi, Sefti, M. Vafaie, Ameri, A., Kaveh, N. Shojai
• Format: Journal Article
• Language: English
• Published: Rugby Elsevier B.V 01.02.2008; Institution of Chemical Engineers
• Subjects: Applied sciences; Chemical engineering; Exact sciences and technology; Gas injection; Minimum miscibility pressure; Neural genetic model; Prediction; Minimum miscibility pressure; Gas injection; Neural genetic model; Prediction; Design; Bubble; Genetic algorithm; Neural network; Modeling; Pattern matching; Gas oil
• ISSN: 0263-8762; 1744-3563
• DOI: 10.1016/j.cherd.2007.10.011

Enhanced oil recovery (EOR) processes are unavoidable fact, which will be applied in oil upstream industry. It seems the miscible gas injection into oil reservoirs be one of the most effective methods in EOR approaches. A fundamental factor in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from gas injection is very much dependent on the MMP. From an experimental point of view, slim tube displacements, and rising bubble apparatus (RBA) tests normally determine the MMP. Because such experiments are very costly and time-consuming, searching for quick and vigorous mathematical determination of gas–oil MMP is usually requested. Artificial neural networks (ANN) have been proved to be an effective alternative for forecasting purposes because of the pattern-matching ability. However, there is no specific recommendation on suitable design of network for different structures and generally, the parameters are selected by trial and error, which confines the approach context dependent. In this study, a hybrid neural genetic algorithm (GA-ANN) is proposed with the purpose of automate the design of neural network for dissimilar type of structures. The neural network is trained considering the reservoir temperature, reservoir fluid composition, and injected gas composition as input parameters and the MMP as desired parameter. Consequently, neural genetic model is compared with results obtained using other conventional models to make evaluation among different techniques. The results show that the neural genetic model can be applied effectively and afford high accuracy and dependability for MMP forecasting.