A neural network prediction model of fluid displacements in porous media Academic Article

abstract

• 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.

authors

• Karkoub, Mansour

published proceedings

• COMPUTERS & CHEMICAL ENGINEERING

author list (cited authors)

• Elkamel, A., Karkoub, M., & Gharbi, R.

citation count

• 6

complete list of authors

• Elkamel, A||Karkoub, M||Gharbi, R

publication date

• January 1996

publisher

• Elsevier  Publisher

published in

• Computers and Chemical Engineering  Journal

Digital Object Identifier (DOI)

• 10.1016/0098-1354(96)00095-6

start page

• S515

end page

• S520

volume

• 20

issue

• SUPPL.1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2F0098-1354%2896%2900095-6