Determination of Reservoir Model from Well Test Data, Using an Articial Neural Network

Nowadays, neural networks have a wide range of usage in dierent elds of engineering. In the
present work, this method is used to determine a reservoir model. Model identication, followed
by parameter estimation, is a kind of visual process. Pressure derivative curves showing more
features are usually used to determine the reservoir model based on the shape of the curve
and no calculation is included. So, it is dicult to convert this kind of visual process to an
applicable algorithm for computers. In fact, the model identication is a pattern recognition
which is best done by an Articial Neural Network (ANN). If neural networks were learned
successfully, they would be able to categorize dierent shapes into dierent groups, due to their
visual characterization. So, their use in such a job would seem to be useful. In this work, it is
shown how to train, examine and use neural networks to determine a reservoir model. The input
of an ANN is fty points of the normalized pressure derivative type curve. Each ANN is trained,
based on a specic model, and the output of the ANN is the probability of occurrence of a fed
curve to the related model.