The aim of this research is to investigate the performance of artificial ne
ural networks computing technology, to identify preliminary well test inter
pretation model based on derivative plot. The approach is based on training
the neural network simulator uses back-propagation as the learning algorit
hm for a predefined range of analytically generated well test response. The
trained network is then requested to identify the well test identification
model for test data, which is not used during training sessions. For creat
ion of training examples, an analytical response generator is implemented w
hich is capable of producing responses of various models. Both the neural.
network simulator and the analytical response generator is enfolded into a
single package which is capable of producing diagnosis plots, transferring
data and filtering the input pattern. Unlike the ones presented in literatu
re the package utilises a distributed modular structure, by which saturatio
n possibility of the neural network is reduced considerably. Moreover, the
distributed structure allows the training sequence to be initiated on diffe
rent computers, thus reducing the training time up to sixteen folds. The pa
ckage is verified with several examples either analytically generated or ta
ken from literature.