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Using actual field cases, a neural network model was developed to identify candidate wells and predict well performance for water shutoff treatments using polymer gels. A feed forward back propagation algorithm was used to develop the neural networks. The before and after treatment data for 22 wells treated with polymer gels in the Arbuckle formation in central Kansas were used to train and verify the neural networks. Polymers and gels have been used extensively in field applications to suppress excess water production and improve oil productivity. Field experience has demonstrated that candidate-well selection is critical to the success of gel-polymer treatments. To date, most candidate-well selections are on the basis of anecdotal screening guidelines, which often results in inconsistent treatment outcomes. With only pretreatment well data as input parameters, the neural networks developed in this work can accurately predict the post-treatment cumulative oil production of the well one month after treatment with an average error of 16%, and the posttreatment cumulative oil production three months after treatment with an average error of 10%. This is a dramatic improvement over the current method of using anecdotal screening guidelines for candidate-well selections. This method represents a major breakthrough where the candidate selection can now be on the basis of the accurate predictions of treatment outcomes without having to use complicated reservoir models to simulate the well performance after treatment. [ABSTRACT FROM AUTHOR]