Multivariate Prediction Based on the Gamma Classifier: A Data Mining Application to Petroleum Engineering

  • Itzamá López-Yáñez
  • Leonid Sheremetov
  • Oscar Camacho-Nieto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods.


data mining Gamma classifier prediction petroleum engineering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Itzamá López-Yáñez
    • 1
    • 2
  • Leonid Sheremetov
    • 1
  • Oscar Camacho-Nieto
    • 2
  1. 1.Mexican Petroleum Institute (IMP)Mexico CityMexico
  2. 2.Instituto Politécnico Nacional – Centro de Innovación y DesarrolloTecnológico en Cómputo (CIDETEC - IPN)Mexico CityMexico

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