The Simulation of Microbial Enhanced Oil Recovery by Using a Two-layer Perceptron Neural Network

The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oi...

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Published inPetroleum science and technology Vol. 32; no. 22; pp. 2700 - 2707
Main Authors Morshedi, S., Torkaman, M., Sedaghat, M. H., Ghazanfari, M. H.
Format Journal Article
LanguageEnglish
Published Colchester Taylor & Francis 17.11.2014
Subjects
Applied sciences
Bacteria
Computer simulation
Crude oil, natural gas and petroleum products
Energy
Enhanced oil recovery
Exact sciences and technology
Fuels
Mathematical models
microbial method
Networks
neural network
Neural networks
Oil recovery
perceptron
Petroleum products, gas and fuels. Motor fuels, lubricants and asphalts
Processing of crude oil and oils from shales and tar sands. Processes. Equipment. Refinery and treatment units
Reservoirs
simulation
Neural network
perceptron
enhanced oil recovery
simulation
microbial method
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Summary:The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been applied in modeling of microbial enhanced oil recovery since now. Finally, we are going to design a controller for the neural network. This controller is designed for the case where output of the network is oil recovery factor. For this purpose, the network is designed as a one-layer network in which just one output matches each time. In this case, a one-layer network will have acceptable results.
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ISSN:1091-6466
1532-2459
DOI:10.1080/10916466.2011.572106