Artificial Neural Network (ANN) based models were developed for predicting viscosity and wax deposition potentials of petroleum reservoir fluids as a preliminary measure to address the problem of loss of production associated with wax deposition.

Several ANN architectures were trained using supervised paradigms for viscosity modeling and unsupervised paradigms for wax deposition potentials. Input to the models is temperature, pressure and viscosity data of the reservoirs. Five Nigerian crude oil and gas condensate reservoir data were used to validate the models.

ANN competitive layer wax deposition model developed in this work excellently identified crude oil and gas condensate potential to deposit wax in upstream and downstream facilities compared to classical regression technique (CRT) based mathematical model.

The inherent problems of tubing and pipeline blockage by wax deposits would be minimized by the application of the predicting models during well development stage prior to production

Keywords:
equation of state, statistical description, fluid modeling, neural network, viscosity, algorithm, Oil Viscosity, Artificial Intelligence, experimental viscosity, layer network
Subjects:
Fluid Characterization, Information Management and Systems, Fluid modeling, equations of state, Artificial intelligence, Neural networks
Copyright 2009, Society of Petroleum Engineers
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