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

Nowadays, neural networks have a wide range of usage in different fields of engineering. in thepresent work, this method is used to determine a reservoir model. model identification, followedby parameter estimation, is a kind of visual process. pressure derivative curves showing morefeatures are usually used to determine the reservoir model based on the shape of the curveand no calculation is included. so, it is difficult to convert this kind of visual process to anapplicable algorithm for computers. in fact, the model identification is a pattern recognitionwhich is best done by an artificial neural network (ann). if neural networks were learnedsuccessfully, they would be able to categorize different shapes into different groups, due to theirvisual characterization. so, their use in such a job would seem to be useful. in this work, it isshown how to train, examine and use neural networks to determine a reservoir model. the inputof an ann is fifty points of the normalized pressure derivative type curve. each ann is trained,based on a specific model, and the output of the ann is the probability of occurrence of a fedcurve to the related model