Neural networks versus Linear and Sequential Programming for Gas Lift Optimization in a two oil wells system

Using a model-based optimization, a neural network model is developed to calculate the optimal values of gas injection rate and oil rate of a gas lift production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gas lifted wells. The result...

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Bibliographic Details
Main Authors: Salazar-Mendoza, R., Jimenez de la C, G., Ruz-Hernandez, Jose A.
Format: Conference Proceeding
Language:English
Subjects:
Costs
Linear programming
Neural networks
Particle separators
Performance evaluation
Petroleum
Production systems
Routing
Testing
Transmitters
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Summary:Using a model-based optimization, a neural network model is developed to calculate the optimal values of gas injection rate and oil rate of a gas lift production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gas lifted wells. The results were compared with the linear and sequential programming for gas lift optimization. For both cases minimizing the objective function the proposed strategy shows the ability of the neural networks to approximate the behavior of an oil production system and to solve optimization problems when a mathematical model is not available.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2009.5179056