Well test analysis represent an essential component of reservoir engineering analysis and management. It provides estimates of reserves, heterogeneties, permeabilities, and information on the status of wellbore conditions. A well testing program can become prohibitively costly due to equipment and personnel costs and lost production. It may not be possible to design an effective well testing program that includes all the important wells in a specific field. In this paper, a novel "Virtual Well Testing" approach is presented. The new method uses artificial neural networks (ANNs) to generate virtual transient pressure data. The proposed method does not eliminate the need for traditional well tests, but provides a basis to extract more information from the existing sets of transient pressure data. In virtual well testing, transient pressure data from selected wells in the field are used to train a neural network that is capable of predicting transient pressure responses at other well locations where well tests have not been conducted. The proposed methodology can also be used for designing well tests and for completing the partially recorded pressure measurements due to malfunction of testing equipment.

This paper provides guidelines for selecting actual well test locations, for choosing input parameter neurons and for training the ANN. Several simulated case studies are used to train and evaluate virtual well testing capabilities of the ANN. The effects of the number of wells tested, well locations and production histories are evaluated. The ANN is successful in predicting interference effects and reservoir permeabilities. The ANN generates reliable virtual transient pressure data in a matter of seconds and is an effective reservoir engineering analysis and management tool.

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