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Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Mohammad Afshar,Mojtaba Asoodeh,Amin Gholami 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.3
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, whichplays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticatedmodels to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, andreservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extractingthe underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in localminima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithmin charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improvingthe accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied toa dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neurofuzzymodels from local minima trapping, which might occur through back-propagation algorithm.