Using neural networks for candidate selection and well performance prediction in water-shutoff treatments using polymer gels - A field-case study Conference Paper uri icon

abstract

  • 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 feedforward-backpropagation 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 based on 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 post-treatment cumulative oil production three months after treatment with an average error 10%. This is a dramatic improvement over the current method of using anecdotal screening guidelines for candidate-well selections. This method allows the candidate selection to be based on the accurate predictions of treatment outcomes without having to use complicated reservoir models to simulate the well performance after treatment. Copyright 2006, Society of Petroleum Engineers.

published proceedings

  • Proceedings - SPE Asia Pacific Oil and Gas Conference and Exhibition 2006: Thriving on Volatility

author list (cited authors)

  • Saeedi, A., Camarda, K. V., & Liang, J. T.

complete list of authors

  • Saeedi, A||Camarda, KV||Liang, JT

publication date

  • December 2006