WELL TEST MODEL IDENTIFICATION BY ARTIFICIAL NEURAL NETWORKS

The aim of this research is to investigate the performance of artificial neural networks computing technology, to identify preliminary well test interpretation model based on derivative plot. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm 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 creation of training examples, an analytical response generator is implemented which 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 literature the package utilises a distributed modular structure, by which saturation possibility of the neural network is reduced considerably. Moreover, the distributed structure allows the training sequence to be initiated on different computers, thus reducing the training time up to sixteen folds. The package is verified with several examples either analytically generated or taken from literature.