A neural network prediction model of fluid displacements in porous media
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This paper presents the development and design of an artificial neural network that is able to predict the breakthrough oil recovery of immiscible displacement of oil by water in a two-dimensional vertical cross section. The data used in training the neural network was obtained from the results of fine-mesh numerical simulations. Several network architectures were investigated and trained using the back propagation with momentum algorithm. The neural network that gave the best predictive performance was a two-hidden layer network with 8 neurons in the first hidden layer and 8 neurons in the second hidden layer. This network also performed well against a cross validation test. The reservoir simulation data used so far in the training process was for a homogeneous reservoir, the more general case is still under investigation.
COMPUTERS & CHEMICAL ENGINEERING
author list (cited authors)
Elkamel, A., Karkoub, M., & Gharbi, R.
complete list of authors
Elkamel, A||Karkoub, M||Gharbi, R